From e1e86fc5c904017219c1ab5b6ac6aa48e5e90593 Mon Sep 17 00:00:00 2001 From: Ehsan Moravveji Date: Sun, 12 Oct 2025 19:56:25 +0200 Subject: [PATCH 01/13] restructuring the repo --- docs/PythonIntro.zip | Bin 605621 -> 0 bytes notebooks/GJB_data_types_recap.ipynb | 2170 +++++++ notebooks/basic_datatypes.ipynb | 723 +++ notebooks/basic_syntax.ipynb | 292 + notebooks/control_flow_conditionals.ipynb | 329 ++ notebooks/control_flow_loops.ipynb | 1050 ++++ notebooks/debugging_basics.ipynb | 166 + notebooks/dict.ipynb | 463 ++ notebooks/functions.ipynb | 630 ++ notebooks/getting_started_jupyter.ipynb | 328 ++ notebooks/intro_to_numpy.ipynb | 824 +++ notebooks/io.ipynb | 970 ++++ notebooks/list.ipynb | 804 +++ notebooks/operators.ipynb | 457 ++ notebooks/quick_start_matplotlib.ipynb | 1117 ++++ notebooks/set.ipynb | 244 + notebooks/tuple.ipynb | 317 + notebooks/variables.ipynb | 413 ++ scripts/MOCK_DATA.csv | 1001 ++++ scripts/MOCK_DATA_out.csv | 1001 ++++ 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>YrS!dlw{Rd%t1ZSoUuF0{*S&KCLkLCpqr zfm*1Dyba{sw!)mpcO0Y$X9pV_!t){K&d@_5*_?~mE=@}>#28cv06L53S403IIy-=N zNwg2j7JogOUT0bnX>jN@x`1qLDE=xAQu6AGBzP9&%05zr7V@M9O7Voj2?Fia4U1eP zQY*|jX>6cRQo4l=Gu7xz5Z}TAMu>H8Ej@)Z?P~Jp9 z;S@aZR8}QJtRKyUM?+1}j4_eJXT)znGgOYW^Bf=yva@(LWbqGiptbgd2)w?bQ$#9B zoF1c^0=hhRkAkIr2{r`Wgy*WQO&^5?!Hp|HA&mVV9Tn~7Mo>PSSKFp%HKGGd9?A#8 zU3PK}Kptwe1pzaZ!TM`-cHgHy`^M%dTeJ;ys!~_tYXAx-K!L9Tl_o&(MXmLv;;ZPV zME_`qcMo!EQb*V3Ut4UQI=!*3!Bz!lUvSJJpaBn!y8v|; zwDa26BVcxOW#Hz!{r{p&zea}y>5b&MsQ1MT$QyjXV5aS?rul-am{(`n<^+gD*-&fvvtLmWQetr!+({izf_@o zEep{^PJ!?0uDY(5TkC~kakCYn|9T@pjTl&0x}$MoNq6KuAUr5X2#;-Vpnrp5e9+F& zb82|oFO-S;1(7C?n?ZI+%{j}r%7C~k0YHcq0Km8c((5^H{KLOhVI=UMO+xGYdkOd# z$xgp?*a2~9z=Nx$H2^Hg3KBRLmO2jqb}ZIuGbqAo!=)o@4NV@R4bs34gs#2UX!O4U z);CAI=76TM(XS~`wpeSuhfh8N0)Rd_Md(&c7VNDe;#OP%Z+P5c?AVtNXhXSKxM6j& z`H%L0FOa|tcf+u5H<3em@`Sz~1vzHqJfIC7%y7R#4g;f)f>PK7hi3YV<=8Dy4z8m?dI%pzbK5!e7Zwu$;_sb_R zffZ_t{99n_+9WE+Wvt64aY7bgKm)!Y>2?5QEu{%=>JWD2F=!@b6X_Sq)_SS=ZA^pR z1<>{a1Mb0)*~91)s+2#j{R`>lPF@8lSxGN4OF+Fg3LqOUWod7zYs{nuXR7+#k7S}Dh@>M z>-d4hm2<-4@K$hkJF?=(v;zkrjK6g5e;twk!Ahiaf6{SKlVa-VK}a|jKsfNtutOY> z2I*{YEHJbvfKD9v|yF0iFA&p1@LO~q+_>1fg3jffbTYA ziT?p$G2lK9VGCW+L#NCCl5`yrw0sT7!-L;6SA+=#5 z9o%f7Q@qSr%i0J~hALlhtJ<-FI;aY-HP++1;1;`6h_#8o!#&gf*u%pQC63aV$?*;d z(_`x__<~J<@~}o@oq)pPqr4ZCaAw;)Q%U&YRc^o#^ur0pug6q>`3x(}^u;mllptvW#TJTj+hBC0K%_#ZUi`2w+`^B58?OcD`9Jq^zDHJk%|f7=OQ`by5BAgNv}0;4hyZA!2U{pSRygKCllW4}a_)hTwg) zE|*Ay0pY9d8xg1m00R6CcNl<`x|=T%mxJ(0^o;;J24MgMj&l3YJ$^(02w&RX2*B_b z0Q`A(d*kf4u@_Sjt7Q>BRJoD3_$1YaXDc_-MjSndQ6W;_Abf>!Bkj@;fHuLKj4%wN z\", line 2, in \n", + " patient[1] = 16\n", + "TypeError: 'tuple' object does not support item assignment\n" + ] + } + ], + "source": [ + "try:\n", + " patient[1] = 16\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `tuple` seems a reasonable data type to represent our patients, so let's proceed with the function to read them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def read_patients(file_name):\n", + " '''\n", + " Generator function that yields the patients in a file.\n", + " file_name: name of the data file\n", + " returns tuples representing the patients\n", + " '''\n", + " with open(file_name, 'r') as patients:\n", + " # read but ignore the column names, special use of underscore\n", + " # underscore considered a general purpose \"throwaway\" variable name to indicate \n", + " # that it is being deliberately ignored \n", + " _ = patients.readline()\n", + " # iterate over all the patient data\n", + " for line in patients:\n", + " # strip the line endings, and split on whitespace\n", + " data = line.rstrip().split()\n", + " # turn this function into a generator by yielding a tuple\n", + " # representing a patient; for convenience, do appropriate\n", + " # type conversion\n", + " yield (str(data[0]), int(data[1]), float(data[2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `yield` statement interrupts the execution of the function, returning a value. When the function is called again, execution will resume from that same point in the function, retaining state." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test the function by simply printing the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for patient in read_patients('Data/patients.txt'):\n", + " print(patient)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That seems to work quite well. Let's proceed with some data analytics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Named tuples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although this implementation works, it has a drawback. We have to remember that `patient[0]` is the name, `patient[1]` the age, and `patient[2]` the weight of the patient. This is error-prone, and may give rise to subtle bugs. It would be much more convenient if we could access the `tuple`'s fields by name, rather than by index. Python's standard library implements a convenient way to define named tuples through `typing.NameTuple`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import NamedTuple\n", + "\n", + "class Patient(NamedTuple):\n", + " name: str\n", + " age: int\n", + " weight: float" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will cover defining our own classes elsewhere, but this is quite staightforward. Note that `typing.NamedTuple` was introduced in Python 3.5, for earlier version of Python, you can use the somewhat more cumbersome `collections.namedtuple`. A named tuple of type `Patient` has three fields, `name`, `age`, and `weight`, which we declare using type hints, `str`, `int`, and `float` respectively. Let's redefine the tuple representing Suzan." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient = Patient('suzan', 15, 48.9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The type of this data structure is `Patient`, and we can access the values of its fields by 0-based index, but also by name. The number of fields can be determined using the `len` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "type(patient)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient[0], patient.name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(patient)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to Python's built-in `tuple`, a named tuple is immutable, i.e., it can not be modified after its creation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " patient.age = 16\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modifying the function `read_patients` to return named tuples is trivial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def read_patients(file_name):\n", + " '''\n", + " Generator function that yields the patients in a file.\n", + " file_name: name of the data file\n", + " returns named tuples representing the patients\n", + " '''\n", + " with open(file_name, 'r') as patients:\n", + " # read but ignore the column names\n", + " _ = patients.readline()\n", + " # iterate over all the patient data\n", + " for line in patients:\n", + " # strip the line endings, and split on whitespace\n", + " data = line.rstrip().split()\n", + " # turn this function into a generator by yielding a tuple\n", + " # representing a patient; for convenience, do appropriate\n", + " # type conversion\n", + " yield Patient(str(data[0]), int(data[1]), float(data[2]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate, let's read the patient, but only print their names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for patient in read_patients('Data/patients.txt'):\n", + " print(patient.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `list` is a very convenient data type, representing an ordered sequence. We can for instance create a list of names of our patients." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names = list()\n", + "for patient in read_patients('Data/patients.txt'):\n", + " patient_names.append(patient.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start off with an empty list, created using the `list()` function, and we append the name of each patient to it. The resulting list contains the patient names, in the order they have been read from the file, and added to the list `patient_names`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List elements can be accessed by 0-based index, and the length of a list can be obtained using the `len` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trying to access an element using an invalid index, e.g., one that is larger than the length of the `list` raises an exception." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " print(patient_names[len(patient_names)])\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can iterate over the elements of a `list` using a `for`-loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for patient_name in patient_names:\n", + " print(patient_name.capitalize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In contrast to a `tuple`, the elements of a list can be modified, for instance, if we want to change `'bob'` to `'robert'`, we can just do that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[1] = 'robert'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for patient_name in patient_names:\n", + " print(patient_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Surreptitiously, we have already used a `list` when we used the `split` method on the lines of our data file. The inverse operation, converting a `list` of `str` type elements can be accomplished using the `join` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `join` is a method defined on a `str`, the separator, and that is argument must be an iterable over `str` values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indices can be negative, for instance the element at index `-1` would be the last element of the list, `-2` the second but last, and so on. Hence, if the list has $n$ elements, legal index values run from $-n$ to $n - 1$. Although that is a nice shortcut, it is also a good way to introduce subtle bugs in your code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[-1] == patient_names[len(patient_names) - 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[0] == patient_names[-len(patient_names)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifying & querying lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whereas the number of fields of a `tuple` is always the same, elements can be added to or removed from a list at any time. Besides the `append` method we have already used to build the list, there is the `insert` method to add elements anywhere in the list, and the `pop` method to remove elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.insert(2, 'kathy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for patient_name in patient_names:\n", + " print(patient_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.pop(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our `patient_names` list now has `'kathy'` as the third element, while `'elly'` is no longer an element." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `pop()` method without an index will remove and return the list's last element, `'john'` in this case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.pop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking list membership is easy using the `in` operator, for instance, `'robert'` is in our list, while `'mary'` isn't." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'robert' in patient_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'mary' in patient_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `index` method will return... the index at which an element first occurs in a list, and raises an exception otherise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.index('robert')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " patient_names.index('mary')\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the same element can occur multiple times in a list, for instance, we can add another `'alice'` at the end of the list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.append('alice')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `index` method will only return the index of the first occurence. However, since it can optionally be called with a start, and and end index, we can also search from the end of the list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.index('alice')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.index('alice', -1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `pop` method removes an element at a given index, the `remove` method removes an element by value. If we remove `'alice'` from the list, only the `'alice'` at the end of the list will remain. Removing a value that doesn't occur in the list will raise an exception." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names.remove('alice')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just as for the `index` method, using -1 as a start index would remove the last `'alice'` from the `list`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aliasing versus copying" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It may be a bit counter-intuitive, but assigning a list to a new variable doesn't copy the list. The new variable refers to the same list as the original one. We assign `patient_names` to another variable, remove the first element, and check the value of both variables." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "other_patient_names = patient_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "other_patient_names.pop(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(other_patient_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `copy` method will create an actual copy of the original list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "other_patient_names = patient_names.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "other_patient_names.pop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(other_patient_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "', '.join(patient_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Slicing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create sublists out of list by \"slicing\", i.e., indexing by a start index, and end index, and, optionally, a stride. For instance, we could select the first three elements of the list, or the second to the sixth element, but only every other element." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[1:6:2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both the start and end index can be left out, that means that the slice starts from the beginning, or runs up to the end of the list respectively. Combined with negative indices, this is quite expressive. Getting the first, or the last three elements of list is quite trivial that way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[-3:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Leaving out both start and end index, and using a stride of -1 is a neat thrick to reverse a list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patient_names[::-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that slicing creates a new list, but that it is a shallow copy. This is important when list elements are mutable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simplest way to construct a list is by a literal enumeration of its elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "first_names = ['peter', 'sally', 'vaughan', 'sophie', 'patrick']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, new lists can be constructed from iterables by comprehension. For instance, a list of capitalized names can be constructed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[name.capitalize() for name in first_names]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is also possible to select only certain elements for the new list by adding an `if` clause to the comprehension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[name.capitalize() for name in first_names if name.startswith('p')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Returning to our running example, we can combine this to select the names of the patients that are older than 35." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[patient.name for patient in read_patients('Data/patients.txt') if patient.age > 35]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `list` function, we can also create a list of the patients in our data file using the generator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "patients = list(read_patients('Data/patients.txt'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read_patients returns a named tuple\n", + "patients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(patients[0].age)\n", + "print(patients[6].name)\n", + "print(patients[-1].weight)\n", + "\n", + "# can the age be updated?\n", + "patients[0].age = 88" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists can be concatenated using the `+` operator, or extended throught the `extend` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "['alice', 'bob'] + ['carol', 'dave']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which ages are represented in our group of patients? When we want to answer this question, we actually ask for a mathematical set containing the ages, each element of the set occurs only once. We could accomplish this using a `list` as well, but that would be pretty cumbersome." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_list = list()\n", + "for patient in read_patients('Data/patients.txt'):\n", + " if patient.age not in age_list:\n", + " age_list.append(patient.age)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using Python's built-in `set` type, this task is not only easier, but the data structure represents the mathematical concept we actually have in mind." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set = set()\n", + "for patient in read_patients('Data/patients.txt'):\n", + " age_set.add(patient.age)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is even simpler using a set comprehension, similar to the list comprehension we've encountered before." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set = {patient.age for patient in read_patients('Data/patients.txt')}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of elements of a `set` is obtained using the `len` function, and we can test membership using the `in` operator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(age_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "40 in age_set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "53 in age_set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As opposed to a `list`, a `set` is not a sequence, i.e., it is not ordered, it has no first, second, or last element. This is of course the same for a mathematical set, which is obviously no coincidence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Iterating over the elements of a `set` is done using a `for`-loop." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for age in age_set:\n", + " print(age)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifying sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `add` method adds an element to an existing set. To remove an element, we can use the `pop()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set.pop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the element that is removed is random (well, implementation dependent, to be precise). To remove an element from the set, you can use either the `remove` or `discard` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set.remove(41)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " age_set.remove(41)\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `discard` method will not raise an exception when we try to remove an element that is not in the set. To remove all elements from a `set`, we can use the `clear` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_set.clear()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(age_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the mathematical operation you would expect on sets are indeed defined, e.g., union (`|`), intersection (`&`), difference (`-`), symmetric difference (`^`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1 = {1, 2, 3, 4, 6, 12}\n", + "set2 = {1, 3, 5, 15}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1 | set2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1 & set2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1 - set2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set2 - set1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1 ^ set2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All operations create a new set, an method to perform these operations in place is implemented, mainly for performance reasons. For example, `difference_update` applied to `set1` would modify that set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1.difference_update(set2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Three Boolean methods are available as well,\n", + " * `s1.isdisjoint(s2)` will test whether the sets are disjoint,\n", + " * `s1.issubset(s2)` will check whether `s1` is a subset of `s2`, and\n", + " * `s1.issuperset(s2)` checks whether `s1` is a superset of `s2`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `dict` is a mapping from a set of keys to a map of values, each key maps onto exactly one value, but multiple keys may map the the same value. This corresponds to a mathematical relation called an injection. `dict`s are a very convenient data type to represent associations between objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "back to the running example. What if we want to know how many patient there are of each age? So with an age, we want to associate a count. We can represent this by a dictonary that has a patient's age as keys, and the number of patients at that age as values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr = dict()\n", + "for patient in read_patients('Data/patients.txt'):\n", + " if patient.age not in age_distr:\n", + " age_distr[patient.age] = 0\n", + " age_distr[patient.age] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start off with an empty `dict`, and iterate over the patients as usual. For each patient, we check whether her age is in the `dict` as a key using the `in` operator. If not, we associate the value 0 with that key. Next, we increment the value associated with the key.` " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the value associated with a key by using the latter as an index, and we can get the number of key/value pairs using the `len` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr[33]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(age_distr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trying to access a `dict` with a key not in the `dict` raises an exception." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " print(age_distr[-5])\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can iterate over the keys in a dictionary, and use that to access the corresponding value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using the f format\n", + "# https://www.datacamp.com/community/tutorials/f-string-formatting-in-python\n", + "for age in age_distr:\n", + " print(f'{age}: {age_distr[age]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This pattern is so common that the `items` method is defined for `dict`s. When called, this method yields `tuple`s where the first field is the key, the second the value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for age, count in age_distr.items():\n", + " print(f'{age}: {count}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that starting from Python 3.6, keys and item are returned in insertion order. For older version of Python, the order can and should not be relied on." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifying dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "New items can be added to a `dict` at any time, or existing values can be updated by assignment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr[20] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr[20] = 4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An item can be removed from the `dictt` using the `remove` method, which takes the key as an argument. The value associated with that key is returned. If we try to remove an item with a key that is not in the `dict`, an exception is raised." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "age_distr.pop(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " age_distr.pop(20)\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like sets and lists, dictionaries can also be constructed using comprehensions. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like sets and lists, dictionaries can also be constructed using comprehensions. We will create some people with random ages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from random import randint\n", + "random.seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "people = {name: randint(20, 40) for name in ['zoe', 'wolf', 'thomas']}\n", + "people" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "more_people = {name: randint(20, 40) for name in ['elsie', 'frank', 'thomas']}\n", + "more_people" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two dictionaries can be merged using the `update` method, which modifies the `dict` in-place." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "people.update(more_people)\n", + "people" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The items of `more_people` have been added to `people`, overwriting the original value when an item with that key already existed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nesting data structures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the excpetion of our list of patients, `list` of `tupule`, all the example we consided so far had elements that were simple types like `str`, `int` or `float`. However, it is possible, and often useful to build more complicated data structures out of `list`, `set`, and `dict`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, we could represent a matrix as a `list` of `list`, where each element of the outer `list` represents a row of the matrix. (This is just for illustration purposes, for mathematical applications, you would use numpy arrays, both for performance and features.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rows = 3\n", + "cols = 4\n", + "matrix = list()\n", + "for i in range(rows):\n", + " matrix.append(list())\n", + " for j in range(cols):\n", + " matrix[i].append(i*cols + j)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix[1][2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix[0][3] = -17" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Shallow copying" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shallow copies? Let's explore that here. We create a copy of our matrix, and modify it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_matrix = matrix.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add an extra row to `new_matrix`, and `matrix` is unaffected since we created a copy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_matrix.append([3, 9, 7, 5])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we modify and element of `new_matrix`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_matrix[0][0] = 103" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is all as expected, but inspecting `matrix` may yield a surprise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems that the elements of `matrix` and `new_matrix`, i.e., the first three rows, are the same `list` objects. However, `matrix` and `new_matrix` themselves are distinct objects. This is easy to verify using the `is` operator that tests object identify." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix[0] is new_matrix[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix is not new_matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, the `copy` method produces a shallow copy of the original data structure. Although we illustrated this for `list`, the same is true for both `set` and `dict` as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Limitations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can store elements of type `list`, `set` and `dict` in lists without issues. For instance, we can create a `list` of `set`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "list_of_sets = list()\n", + "for size in range(5):\n", + " list_of_sets.append(set(range(size)))\n", + "list_of_sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, not everything can be stored in a `set` or a `dict`. For instance, if we try to create a `set` of lists, an exception is raised." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " set_of_lists = set()\n", + " for size in range(5):\n", + " set_of_lists.add(list(range(size)))\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some types in Python are 'hashable', while others are not. For instance, a `float` has a `__hash__` function, while a `list` has not." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(17.3).__hash__()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " list_of_sets.__hash__()\n", + "except:\n", + " print_exc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This implies that only values of types that have a `__hash__` method implemented, and are, by definition, 'hashable' can be stored in a `set`, or used as *keys* in a `dict`. Any type can occur as *value* in a `dict` though, regardless of whether it is hashable or not." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Smell" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are using nested data structures, that might actually a code smell, i.e., an indicator that your code design could stand improvement. Quite often, it means that it is time to consider introducing classes to represent the concepts you are trying to model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choices, choices, choices..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How to pick the correct data structure? Of course, there are quite a number of considerations. However, as a rule of thumb, if you pick a data structure that corresponds to the mathemaical model you have in mind while programming, that is probably a good start.\n", + " * Tuples can represent elements of carthesian products of sets.\n", + " * Lists represent ordered sequences of objects.\n", + " * Sets repreesent... well, mathematical sets.\n", + " * Dictionaries represent mathematical relations, or associations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes, your code can be simplified by picking the right data type. For instance, if you find yourself representing something, say, the information on a patient, by a list, and for each patient, the same information is available, and you don't change it in your code, than you might want to replace the list by a tuple.\n", + "\n", + "Similarly, when you represent that information by a dictionary, you can replace it by a named tuple.\n", + "\n", + "If you use a list, but add some item only when it isn't an element yet, you may want to use a set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another consideration is performance, some operations are fast on certain data structures, slow and others, and vice versa. Take membership test for example, this is a lot faster on a set, than on a list. In fact, for large number of elements, the difference can be huge." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = set(range(1_000_000))\n", + "l = list(range(1_000_000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit 1_000_001 in s \n", + "1_000_001 in s " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%timeit 1_000_001 in l\n", + "1_000_001 in l" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# magic command\n", + "%lsmagic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other standard library data structures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python's standard library contains many additional data structures, some quite specialized. We will only discuss a few here that might prove useful. Again, it pays of to read through the documentation to familiarize yourself with what is available." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although arrays are very useful data structures, the implementation in Python's standard library is not so great. However, they can be useful to get some extra performance if you can not use numpy for some reason, or if you require interoperatbility with C. As opposed to lists, array elements all have the same type, and that is specified when you crate the array. Although `array` supports methods such as `append`, `insert` and `pop`, you would probably want to avoid those for performance reasons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from array import array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights = array('d', (patient.weight for patient in read_patients('Data/patients.txt')))\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elements can be accessed by index, and can be modified. The length of an `array` can be determined using the `len` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights[0] = 59.3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Contrary to what you might expect, the `+` operator represents array concatenation, rather than element-wise addition. Hence to do mathematics on arrays in Python, numpy is the way to go" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weights + weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/basic_datatypes.ipynb b/notebooks/basic_datatypes.ipynb new file mode 100644 index 0000000..0993315 --- /dev/null +++ b/notebooks/basic_datatypes.ipynb @@ -0,0 +1,723 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Built-in data types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## built-in data types\n", + "\n", + "| Type | Meaning | Example |\n", + "|------|---------|---------|\n", + "| int | integers (whole numbers) | a = 2 |\n", + "| float | floating point numbers (real numbers) | a = 2.0 |\n", + "| complex | complex numbers with a real and imaginary part | a = 1.2 + 3j |\n", + "| bool | boolean: True/False values | a = True |\n", + "| str | string: characters or text | a = 'abc' |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## integer\n", + "- Most basic numerical type. \n", + "- Any number without a decimal point is an integer.\n", + "- Note: Python integers are variable-precision, not limited as in C, Matlab to 4 or 8 bytes.\n", + " - see https://rushter.com/blog/python-integer-implementation/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 2\n", + "b = 2**200\n", + "print('type of a', type(a))\n", + "print('type of b', type(b))\n", + "print('a: ', a)\n", + "print('b: ', b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## floating point \n", + "\n", + "- The floating-point type can store fractional numbers. \n", + "- standard decimal notation, or in exponential notation\n", + " * ´a = 0.000005´\n", + " * ´b = 5e-6´\n", + " - Note: limited precision, never rely on exact equality tests with floating-point values.\n", + "0.1 + 0.2 == 0.3\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 0.000005\n", + "b = 5.08e-6\n", + "print(a, b)\n", + "print('type', type(a))\n", + "print('type', type(b))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "comparisons with float numbers can be tricky\n", + "\n", + "due to the limited precision of representing floats, never rely on exact equality tests with floating-point values. 0.1 + 0.2 == 0.3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 0.1\n", + "b = 0.2\n", + "print('a + b =', a + b)\n", + "c = 0.3\n", + "print('test a + b == c: ', (a + b) == c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A better way to cmpare floats: check the absolute difference\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "epsilon = 0.00001\n", + "x = 0.1 + 0.2\n", + "y = 0.3\n", + "print(abs(x - y) < epsilon) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "get an idea of the binary representation of a float: you can convert the float to an integer using the int.from_bytes() function, and then convert that integer to binary with the bin() function.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import struct\n", + "\n", + "x = 0.1\n", + "print(x.hex())\n", + "packed = struct.pack('f', x)\n", + "integer = int.from_bytes(packed, byteorder='big')\n", + "binary = bin(integer)\n", + "print(binary)\n", + "y = x\n", + "\n", + "x = 0.10000000000000001\n", + "print(x.hex())\n", + "packed = struct.pack('f', x)\n", + "integer = int.from_bytes(packed, byteorder='big')\n", + "binary = bin(integer)\n", + "print(binary)\n", + "\n", + "print(x==y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the context of programming and numerical computation, “machine epsilon” refers to the smallest representable number such that 1.0 + epsilon is not equal to 1.0. It represents the upper bound on the relative error due to rounding in floating point arithmetic.\n", + "\n", + "Machine epsilon is a measure of precision in the representation of real numbers in a computer. It varies between systems and is specific to the floating-point format used by the system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "epsilon = sys.float_info.epsilon\n", + "print(epsilon)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## complex \n", + "- A complex number consists of 2 doubles\n", + "- It accepts either `J` or `j` but the numerical value of the imaginary part must immediately precede it. If the imaginary part is a variable the 1 must be present." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 7.86\n", + "c1 = complex(1, 2)\n", + "c2 = 3 + 5.3j\n", + "print('type: ', type(c1))\n", + "print('dir: ', dir(c1))\n", + "c1.imag\n", + "c1.real\n", + "c3 = 2.3 + a*1j\n", + "#c3 = 2.3 + a*j\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## boolean\n", + "- Simple type with two possible values: True and False\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result = (4 > 5)\n", + "print('result: ', result)\n", + "print('type: ', type(result))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 2.3\n", + "b = 3.4\n", + "c = bool(a*b)\n", + "print(c)\n", + "d = bool(0*a)\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## type conversion\n", + "If a variable is of one type but it needs to be of a different type, it is necessary to do a type conversion aka a cast.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 6\n", + "r = float(i)\n", + "print('r: ', r)\n", + "ii = int(3.14)\n", + "print('ii: ', ii)\n", + "z = complex(1.2, 3.4)\n", + "print('z: ', z)\n", + "s = str(r)\n", + "print('type s: ', type(s))\n", + "print('s: ', s)\n", + "z = s + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## string\n", + "\n", + "(some inspiration from http://www.cs.cornell.edu/courses/cs1110/2019fa/lectures/lecture5/presentation-05.pdf)\n", + "\n", + "- A string is a (ordered) sequence of characters\n", + "- Created with single ‘ or double quotes “\n", + " - 'ab cd' (' preferred)\n", + " - \"ab cd\"\n", + " - slicing\n", + "- immutable: cannot be changed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# '012345678901'\n", + "s = 'Hello World!'\n", + "print(s)\n", + "print(id(s))\n", + "print('s[0] ', s[0])\n", + "print('s[2:] ', s[2:])\n", + "print('s[2:4] ', s[2:4])\n", + "print('s[2:8:1] ', s[2:8:1])\n", + "print('s[2:8:2] ', s[2:8:2])\n", + "\n", + "# assign a new value\n", + "s = 'Where are you?'\n", + "print(s)\n", + "print(id(s))\n", + "s[7] = 'x'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### string concatenation and replication\n", + "\n", + "concatenation of string variables or constants using the + operator\n", + "\n", + "replication: multiply a string by a number\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 'John'\n", + "b = 'Mary'\n", + "c = a + b\n", + "print(c)\n", + "c_bis = a + ' '+ b\n", + "print(c_bis)\n", + "\n", + "# add a number\n", + "d =a + str(4.5)\n", + "print(d)\n", + "e = a*4+b*2\n", + "print(e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### strings and Methods \n", + "\n", + "`rstrip`\n", + "\n", + "- Getting rid of line endings: `rstrip('\\r\\n')`\n", + "- Method will strip all combinations of \\r and \\n from right end of string\n", + "- Similar methods:\n", + " - `lstrip`: strips from left end of string\n", + " - `strip`: strips from both ends of string\n", + "- No arguments, strips: space, \\t, \\r, \\n, …\n", + "- These methods do not modify the original string, instead they return a new string that has been altered\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "str1 = 'this is a string'\n", + "print(dir(str1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "str1.capitalize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_str_1 = 'this is my string,\\t with some text in it \\n More text on a next line \\n'\n", + "dir(my_str_1)\n", + "print(my_str_1)\n", + "print(my_str_1.rstrip())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`split`\n", + "\n", + "Splitting string: `split()` returns list of strings\n", + "- no argument: split on (multiple) whitespace\n", + "- otherwise, split on provided string\n", + "- limit number of splits by providing extra argument\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_str_1 = 'this is my string,\\t with some text in it \\n More text on a next line \\n'\n", + "print(my_str_1)\n", + "my_str_split_1 = my_str_1.split()\n", + "print(my_str_split_1)\n", + "my_str_split_2 = my_str_1.split(',')\n", + "print(my_str_split_2)\n", + "\n", + "my_str_3 = 'end 1: 2013-03-28 03:05:57'\n", + "# split on :, but keep the time format!\n", + "my_str_split_3 = my_str_3.split(':', 1)\n", + "print(my_str_split_3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_long_string = ' this is a nice piece of text for you '\n", + "my_words = my_long_string.split()\n", + "print(my_words)\n", + "\n", + "# split on a specified character\n", + "my_words_bis = my_long_string.split('i')\n", + "print(my_words_bis)\n", + "\n", + "#%% putting things together\n", + "new_word = ' '.join(my_words_bis).strip()\n", + "print(new_word)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More methods: is\n", + " isupper\n", + " islower\n", + " isspace\n", + " isdigit\n", + " isalpha" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('ABC'.isupper())\n", + "print('A19'.isupper())\n", + "print('Abc'.isupper())\n", + "print('19'.isupper())\n", + "my_str_1 = 'this is my string,\\t with some text in it \\n More text on a next line \\n'\n", + "print(my_str_1.isalpha())\n", + "print(my_str_1.isalnum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### searching and replacing\n", + "\n", + "- does a string contain a substring: `in`\n", + "\n", + "- search: `index` or `find`\n", + " - find returns -1 when the substring is not found\n", + " - only for strings\n", + " - index will return an error\n", + " - also for lists and tuples\n", + "- Function `len()` computes str length \n", + "- replace with `replace`\n", + " \n", + "Make combinations\n", + "- cut to pull out parts\n", + "- glue to create new strings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# substring in string\n", + "print('ab' in 'ABCD')\n", + "print('ab' in 'ABCDab')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extract information between braces\n", + "str1 = ' this is a string containing (some) text'\n", + "# find the first occurence of '('\n", + "start_brc = str1.index('(')\n", + "# cut from the first brace\n", + "tail = str1[start_brc+1:]\n", + "# find the first occurence of ')'\n", + "end_brc = tail.index(')')\n", + "text = tail[:end_brc]\n", + "print('extracted text: ', text)\n", + "print('len(str1) :', len(str1))\n", + "print(str1.replace('is', 'as'))\n", + "str1 = str1.replace('is', 'was')\n", + "print(str1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# '012345678901'\n", + "s = 'Hello World!'\n", + "print('s[0] ', s[0])\n", + "print('s[2:] ', s[2:])\n", + "print('s[2:4] ', s[2:4])\n", + "print('s[2:8:1] ', s[2:8:1])\n", + "print('s[2:8:2] ', s[2:8:2])\n", + "print('s[:4] ', s[:4])\n", + "\n", + "print('s[:] ', s[:])\n", + "print('s[-1] ', s[-1])\n", + "print('s[8:4:-1] ', s[8:4:-1])\n", + "print('s[:4:-1] ', s[:4:-1])\n", + "print('s[8::-1] ', s[8::-1])\n", + "print('s[::-1] ', s[::-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "small_str_1 = ' small'\n", + "small_str_2 = ' part'\n", + "concat_1 = small_str_1 + small_str_2\n", + "print('concat_1: ', concat_1)\n", + "concat_2 = 3*small_str_1\n", + "print('concat_2: ', concat_2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "str1 = 'help'\n", + "str2 = 'me'\n", + "a1 = str1 + str2\n", + "print(a1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(str1*3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## format output\n", + "\n", + "### printf-style formatting: \n", + "This is similar to the formatting used in C’s printf function. It’s less commonly used in modern Python code.\n", + "\n", + "### str.format(): \n", + "This method is more versatile and can be used in Python 2.7 and above.\n", + "\n", + "### string Interpolation (f-strings): \n", + "Available since Python 3.6\n", + "F-string is a string literal that is prefixed with `f` or `F`. These strings may contain replacement fields (delimited by curly braces {} – fill out the braces). F-string is evaluated at run time.\n", + "\n", + "File: fstring_01.py\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name = 'Peter'\n", + "age = 23\n", + "\n", + "print('%s is %d years old' % (name, age)) # printf style\n", + "print('{} is {} years old'.format(name, age)) # .format\n", + "print(f'{name} is {age} years old') # f-string" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fnum = 2007.12345\n", + "print(f\"Simple = {fnum}\")\n", + "print(f\"Decimal Places specified = {fnum:.2f}\")\n", + "print(f\"Significant Figures={fnum:.3g}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## character\n", + "\n", + "`ord()` takes a string argument of a single Unicode character and returns its integer Unicode code point value.\n", + "\n", + "`chr()` function takes integer argument and returns the string representing a character.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(ord('a'))\n", + "\n", + "print(chr(98))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## docstring\n", + "\n", + "Documentation strings or “docstrings” use a special form of comment.\n", + "The lines are enclosed in triple double quotes \n", + "\"\"\" \"\"\"\n", + "- Everything within triple double quotes is treated as a literal string and a comment, including line breaks.\n", + "- Docstrings are placed at the top of program units, just under the declaration of the unit name (if present). \n", + "- If they are correctly placed, certain automated tools are available to display the documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### None\n", + "\n", + "`None` is the equivalent of null in C.\n", + "\n", + "When to use? Often you want to perform an action that may or may not work. Using `None` is one way you can check the state of the action later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a=None\n", + "print(id(a))\n", + "print(type(a))\n", + "\n", + "r_v = print('abc')\n", + "print(r_v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/basic_syntax.ipynb b/notebooks/basic_syntax.ipynb new file mode 100644 index 0000000..f2b35bc --- /dev/null +++ b/notebooks/basic_syntax.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Some basic Python syntax\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check:\n", + "\n", + "https://jakevdp.github.io/WhirlwindTourOfPython/02-basic-python-syntax.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comment\n", + "Comments are marked by `#`\n", + "Anything on the line following the hash sign is ignored by the interpreter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# some comments\n", + "#\n", + "x = 2 # assign x a value of 2\n", + "\n", + "# print the value of x\n", + "\n", + "print(x) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statement\n", + "\n", + "A statement is one complete “sentence” in the language. It contains one complete instruction. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 2 + 1\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Continuation\n", + "\n", + "`\\` marker is a continuation line" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "a = 8\n", + "x = 1 + 2 + 3 + 4 +\\\n", + " 5 + 6 + 7 + 8\n", + "print('x = ', x)\n", + "%who" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `;`\n", + "Semicolon `;` can optionally terminate a statement (discouraged)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lower = []; upper = []\n", + "%whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%who" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Blocks\n", + "\n", + "Statements can be grouped into **blocks**. \n", + "\n", + "a **block of code** is a set of statements that should be treated as a unit (meant to be executed together).\n", + "\n", + "* Structures that introduce a block end with a colon `:`\n", + "* Blocks are indicated by **indentation level**.\n", + "\n", + "Indentation is used to define the level of a block of code. This is different from many other programming languages, which often use braces {} or keywords.\n", + "\n", + "Indent each block by however many spaces you wish, but each block level must be indented by exactly the same number. \n", + "* Do not use tabs.\n", + "* Some editors (e.g. Spyder) will automatically indent the next statement to the same level as the one before it.\n", + "* The Python standard is to use four white spaces to indent code.\n", + "\n", + "\n", + "Some key points:\n", + "\n", + "* **Indentation matters**: In Python, indentation is not just for readability. It determines how statements are grouped together.\n", + "* **Consistent indentation**: You must use the same amount of indentation for every line in the same block. This can be any number of spaces or tabs, but spaces are preferred.\n", + "* **Blocks can contain other blocks**: If a statement like `if`, `for`, or `while` has its own block, it can contain other blocks. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 5 > 2:\n", + " print(\"Five is greater than two!\")\n", + "else:\n", + " print(\"Five is not greater than two!\")\n", + " \n", + "if False:\n", + " print(\"I'm a code block\")\n", + " print(\"Look at the indent\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from math import sqrt\n", + "\n", + "# calculate the sum of square root of even numbers\n", + "my_numbers = [1, 4, 3, 4, 16, 9]\n", + "sum = 0\n", + "for i in my_numbers:\n", + " if i%2 == 0:\n", + " sum = sum + sqrt(i)\n", + " print(sum)\n", + " print('final sum = ',sum)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad check_whitespace.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "!python check_whitespace.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run check_whitespace.py" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whitespace within lines does not matter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a=1+2\n", + "b = 1 + 2\n", + "c = 1 + 2\n", + "print(a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "tip: check the way python is implemented (what is under the hood)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import platform \n", + "platform.python_implementation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "tip: check in what environment the code is running" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "print(sys.prefix)\n", + "print(sys.base_prefix)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/control_flow_conditionals.ipynb b/notebooks/control_flow_conditionals.ipynb new file mode 100644 index 0000000..f28d028 --- /dev/null +++ b/notebooks/control_flow_conditionals.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computer programs can’t do that many things, they can:\n", + "\n", + "- Assign values to variables (memory locations).\n", + "- Make decisions based on comparisons.\n", + "- Repeat a sequence of instructions over and over.\n", + "- Call subprograms.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Control flow: conditionals\n", + "\n", + "based on:\n", + "- https://jakevdp.github.io/WhirlwindTourOfPython/07-control-flow-statements.html\n", + "- https://realpython.com/python-for-loop/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With control flow, you can execute certain code blocks conditionally and/or repeatedly: these basic building blocks can be combined to create programs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conditionals\n", + "\n", + "- Boolean values: `True`, `False`\n", + "- Boolean operators: `not`, `and`, `or`\n", + "- Comparison operators: `==`, `!=`, `<`, `<=`, `>`, `>=`\n", + "- membership: `in`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "9 > 8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### if statement\n", + "\n", + "If expression is True, execute all statements **indented** underneath\n", + "*note the indents and the colon :*\n", + "```python\n", + "if expression:\n", + " statement(s)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 0==0:\n", + " print('Hello World')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if 0==1:\n", + " print('Is this happening?')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# change the value of x\n", + "x = 0\n", + "\n", + "if (x > 2):\n", + " print('Hello')\n", + "print('World')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### if else statement\n", + "\n", + "If expression is True, execute all statements indented under if.\n", + "If expression is False, execute all statements indented under else.\n", + "\n", + "```python\n", + "if expression:\n", + " statement(s)\n", + "else:\n", + " statement(s)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 0\n", + "\n", + "if (x > 2):\n", + " print('Hello')\n", + "else:\n", + " print('No')\n", + " print('World')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## if elif else statement\n", + "Execute a code block only if some condition holds\n", + "\n", + "\n", + "**syntax**\n", + " \n", + "*note the indents and the colon :*\n", + "```python\n", + "if expression:\n", + " statement(s)\n", + "elif expression:\n", + " statement(s)\n", + "elif expression:\n", + " statement(s)\n", + "else:\n", + " statement(s)\n", + "```\n", + "\n", + "`elif` and `else` blocks are optional\n", + "\n", + "- no limit on number of `elif`\n", + "- Booleans are checked in order. Once it finds first True, skips over all others\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = -100\n", + "\n", + "if x == 0:\n", + " print(x, 'is zero')\n", + "elif x > 0:\n", + " print(x, 'is positive')\n", + "elif x < 0:\n", + " print(x, 'is negative')\n", + "else:\n", + " print(x, 'what happend here?')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## nesting\n", + "\n", + "combine the expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "don't know\n" + ] + } + ], + "source": [ + "temp = 60\n", + "pressure = 100\n", + "\n", + "if 20 <= temp < 40:\n", + " if pressure <= 60:\n", + " print('Case A')\n", + " else:\n", + " print('Case B')\n", + "elif temp >80:\n", + " print('Case C')\n", + "\n", + "else:\n", + " print('don\\'t know')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exceptions\n", + "\n", + "AN exception is an event that arises when an error occurs during the execution of a program. Python uses exception objects to signal that something has gone wrong. \n", + "\n", + "Some built-in exceptions:\n", + "\n", + "- `Exception`: This is the base class for most error types.\n", + "- `ArithmeticError`: This is the base class for exceptions that occur for numeric calculations.\n", + "- `ZeroDivisionError`: This is a subclass of ArithmeticError and is raised when you’re trying to divide by zero.\n", + "- `FileNotFoundError`: This exception is raised when a file or directory is requested but doesn’t exist.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caught an exception: division by zero\n" + ] + } + ], + "source": [ + "try:\n", + " x = 1 / 0\n", + "except ZeroDivisionError as e:\n", + " print(f'Caught an exception: {e}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "problem: name 'x' is not defined\n" + ] + } + ], + "source": [ + "# x = 5 # put in comment to test\n", + "\n", + "try:\n", + " print(x/0)\n", + "except NameError as e:\n", + " print(f\"problem: {e}\")\n", + "except:\n", + " print(\"Something else went wrong\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "del x" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/control_flow_loops.ipynb b/notebooks/control_flow_loops.ipynb new file mode 100644 index 0000000..8a00ba3 --- /dev/null +++ b/notebooks/control_flow_loops.ipynb @@ -0,0 +1,1050 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.core.interactiveshell import InteractiveShell\n", + "InteractiveShell.ast_node_interactivity = \"all\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Control flow: loops\n", + "\n", + "based on:\n", + "- https://jakevdp.github.io/WhirlwindTourOfPython/07-control-flow-statements.html\n", + "- https://realpython.com/python-for-loop/\n", + "- https://www.w3schools.com/python/default.asp\n", + "- https://docs.python.org/3/tutorial/index.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loops\n", + "\n", + "**for** loops are executed a fixed number of iterations. It is possible to exit early but this must be added to the code.\n", + "\n", + "**while** loops do not have a predetermined number of iterations. They terminate when some condition becomes False.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computer programs can’t do that many things, they can:\n", + "\n", + "- Assign values to variables (memory locations).\n", + "- Make decisions based on comparisons.\n", + "- Repeat a sequence of instructions over and over.\n", + "- Call subprograms.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motivation for loops\n", + "\n", + "example of calculating sum of the numbers 1 ... 100\n", + "\n", + "calculate the sum of 1 + 2 + 3 + ...+ 100\n", + "\n", + "- brute force\n", + "- for loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type sum_brute_force.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run sum_brute_force.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type sum_for_loop.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run sum_for_loop.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# for loops\n", + "repeat a code block\n", + "\n", + "definite iteration: the number of repetitions is specified explicitly in advance\n", + "\n", + "**(basic) syntax**\n", + "\n", + "```python\n", + "for in :\n", + " statement(s) \n", + "```\n", + "The indentation is very important!\n", + "\n", + "\n", + "**concept**\n", + "\n", + "To carry out the iteration this for loop describes, Python does the following:\n", + "- Calls iter() to obtain an iterator\n", + "- Calls next() repeatedly to obtain each item from the iterator in turn\n", + "- Terminates the loop when next() raises the StopIteration exception\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum=0\n", + "numbers =[1,22,31,45]\n", + "for num in numbers:\n", + " print(num)\n", + " sum=sum+num # check the indentation\n", + "avg=sum/len(numbers)\n", + "print ('Average:', avg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "example for loop from Python tutorial R 3.7.0\n", + "\"\"\"\n", + "words = ['cat', 'window', 'defenestrate']\n", + "\n", + "for w in words[:]: # Loop over a slice copy of the entire list.\n", + "#for w in words: # Loop over the sequence, beware for an infinite list!.\n", + " if len(w) > 6:\n", + " words.insert(0, w)\n", + "\n", + "print(words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loop over collections\n", + "\n", + "strings, list and tuple preserve order" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_string = 'mississippi'\n", + "\n", + "for x in (my_string):\n", + " print(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l_my_string = list(my_string)\n", + "print(l_my_string)\n", + "\n", + "for x in (l_my_string):\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t_my_string = tuple(my_string)\n", + "print(t_my_string)\n", + "\n", + "for x in (l_my_string):\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_my_string = set(my_string)\n", + "print(s_my_string)\n", + "\n", + "for x in (s_my_string):\n", + " print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = {'6': 's', '7': 's', '8':'i', '9': 'p', '10': 'p', '11': 'i',\n", + " '1': 'm', '2': 'i', '3': 's', '4': 's', '5':'i',}\n", + "# Dictionaries preserve order of entry (newer version of python)\n", + "for x in d:\n", + " print(x)\n", + "print('****')\n", + " \n", + "for x in d.values():\n", + " print(x)\n", + "print('****')\n", + " \n", + "for x, y in d.items():\n", + " print(x,y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loops can be nested" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "count = 0\n", + "# sequential loops\n", + "for i in range(0, 10):\n", + " count += 1\n", + "for j in range(0, 20):\n", + " count += 1\n", + "print(count)\n", + "print(\"-----------------\")\n", + "# -------------------------\n", + "# nested loop\n", + "count = 0\n", + "for i in range(0, 10):\n", + " for j in range(0, 20):\n", + " count += 1\n", + "print(count)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# while loops\n", + "\n", + "Repeat a code block until some condition is met.\n", + "\n", + "The argument of the while loop is evaluated as a boolean statement, and the loop is executed until the statement evaluates to False.\n", + "It is not always known beforehand how many times the loop will have to repeated.\n", + "\n", + "**(basic) syntax**\n", + "\n", + "```python\n", + "while condition :\n", + " statement(s) \n", + "```\n", + "The indentation is very important!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "while i < 10:\n", + " print(i, end=' ')\n", + " i += 1\n", + "else:\n", + " print('\\n the end \\n')\n", + "print('this is the next statement') " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run while_loop_1.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "watch out for infinite loops!\n", + "\n", + "trigger a KeyboardInterrupt in a Notebook via the menu \"Kernel --> Interrupt\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "while i < 10:\n", + " print(i, end=' ')\n", + " i -= 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## fine-tuning loops\n", + "### Skipping loop iterations\n", + "\n", + "`continue` : skips the remainder of the current loop and goes to the next iteration.\n", + "\n", + "`break`: breaks-out of the loop entirely and go to the next statement following the loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### continue\n", + "\n", + "the loop iterates over a list of numbers. If the current number (num) is even (i.e., num % 2 == 0), the continue statement is executed. This causes the loop to skip the print(num) statement for even numbers and immediately move on to the next iteration. As a result, only the odd numbers are printed. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "3\n", + "77\n", + "19\n", + "77.7\n", + "101\n", + "23\n" + ] + } + ], + "source": [ + "num_list = [1, 2, 3, 44, 56, 77, 8, 18, 19, 77.7, 100, 101, 23]\n", + "for num in num_list:\n", + " if num%2 == 0:\n", + " continue \n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### break\n", + "\n", + "the loop iterates over a list of numbers. If the current number (num) is even (i.e., num % 2 == 0), the break statement is executed. This causes the loop to break out of the loop when an even number is encountered." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "num_list = [1, 2, 3, 44, 56, 77, 8, 18, 19, 77.7, 100, 101, 23]\n", + "for num in num_list:\n", + " if num%2 == 0:\n", + " break \n", + " print(num)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 101\n", + "while n < 1000:\n", + " if not(n%20):\n", + " break \n", + " print(n, 'in while loop') \n", + " n += 1\n", + "else:\n", + " print('else part of the while statement')\n", + "print('out of while loop')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run while_loop_break_continue.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `pass`\n", + "\n", + "The `pass` statement in Python is a placeholder statement that is used when the syntax requires a statement, but you don’t want to execute any code. It’s often used as a placeholder for future code.\n", + "\n", + "The example below uses `pass`as an alternative for the `continue` statement." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "3\n", + "77\n", + "19\n", + "77.7\n", + "101\n", + "23\n" + ] + } + ], + "source": [ + "num_list = [1, 2, 3, 44, 56, 77, 8, 18, 19, 77.7, 100, 101, 23]\n", + "for num in num_list:\n", + " if num%2 == 0:\n", + " pass\n", + " else:\n", + " print(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `else` part\n", + "\n", + "**(full) syntax `for` loop**\n", + "\n", + "```python\n", + "for in :\n", + " statement(s) \n", + "else: # optional \n", + " statement(s)\n", + "```\n", + "\n", + "**(full) syntax `while` loop**\n", + "\n", + "```python\n", + "while condition :\n", + " statement(s) \n", + "else: # optional \n", + " statement(s)\n", + "```\n", + "The indentation is very important!\n", + "\n", + "**else** part\n", + "\n", + "use case: implement search loops, searching for an item meeting a particular condition, and need to perform additional processing or raise warning if no acceptable value is found\n", + "\n", + "- Loop should contain break statement.\n", + "- The statements in the else block will be executed after all iterations are completed (normal termination).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "numbers =[1,2,32,45]\n", + "for i in numbers: \n", + " print(i) \n", + " if i > 5.5:\n", + " break\n", + "else: # Not executed as there is a break \n", + " print('No break encountered') \n", + "print('finished')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "count = 0\n", + "\n", + "while count < 5:\n", + " print(count)\n", + " count += 1\n", + "else:\n", + " print(\"Loop ended, count is no longer less than 5.\")\n", + "print(\"last line\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# another demo of the else clause\n", + "for n in range(2, 10):\n", + " for x in range(2, n):\n", + " if n % x == 0:\n", + " print( n, 'equals', x, '*', n/x)\n", + " break\n", + " else:\n", + " # loop fell through without finding a factor\n", + " print(n, 'is a prime number')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# iterators\n", + "\n", + "\n", + "An iterator is a special object that gives values in succession, it implements the `next` protocol and raises `StopIteration` when exhausted.\n", + "Iterators:\n", + "\n", + "- range()\n", + "- enumerate()\n", + "- zip()\n", + "\n", + "## range\n", + "\n", + "If you need to iterate over a sequence of numbers, the built-in function `range()` can be used, it\n", + "generates arithmetic progressions\n", + "\n", + "for loop is often used with the `range` function (a commonly-used iterator in Python)\n", + "* `range(n)` - sequence 0,1,…,n−1\n", + "* `range(m, n)` - sequence m,m+1,…,n−1\n", + "* `range(m, n, s)` - sequence m,m+s, m+2s,… <= n-1\n", + "\n", + "Note: limitation of `range()`: it works only with integers. (consider NumPy `arange`). See also : https://pynative.com/python-range-for-float-numbers/\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1 2 3 4 5 6 7 8 9 \n", + "Hello World\n", + "Hello World\n", + "Hello World\n", + "Hello World\n", + "Hello World\n", + "\n", + "Hello World\n", + "Hello World\n", + "Hello World\n", + "Hello World\n", + "Hello World\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " print(i, end=' ')\n", + "print()\n", + " \n", + "for i in range(5):\n", + " print('Hello World')\n", + "print('')\n", + "\n", + "for _ in range(5): # throwaway variable, the loop variable isn't used.\n", + " print('Hello World')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In many ways the object returned by `range()` behaves as if it is a list, but it is an object\n", + "which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really\n", + "make the list, thus saving space.\n", + "\n", + "Such an object is an iterable, that is, suitable as a target for functions and constructs that expect\n", + "something from which they can obtain successive items until the supply is exhausted. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "range(0, 10)\n" + ] + } + ], + "source": [ + "print(range(10))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(range(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: The benefit of the iterator indirection is that the full list is never explicitly created!\n", + "```python\n", + "N = 10 ** 12\n", + "\n", + "for i in range(N):\n", + " if i >= 10: break\n", + " print(i, end=', ')\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "range(0, 1000000000000)\n", + "\n", + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, " + ] + } + ], + "source": [ + "N = 10 ** 12\n", + "\n", + "range_N = range(N)\n", + "print(range_N)\n", + "print(type(range_N))\n", + "\n", + "for i in range_N:\n", + " if i >= 10: break\n", + " print(i, end=', ')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i = 3\n", + "i = 4\n", + "i = 6\n", + "i = 8\n", + "al: [2, 3, 5, 7]\n", + "al: [3, 4, 6, 8]\n" + ] + } + ], + "source": [ + "al = [2, 3, 5, 7]\n", + "for i in al:\n", + " i += 1 \n", + " print('i = ',i )\n", + "print('al: ', al)\n", + "\n", + "# simulate a counter \n", + "al_len = len(al)\n", + "pos = range(al_len)\n", + "for i in pos:\n", + " al[i] +=1\n", + "print('al: ', al)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "banana\n", + "apple\n", + "mango\n" + ] + } + ], + "source": [ + "# python way\n", + "fruitslist = ['banana', 'apple', 'mango']\n", + "for i in fruitslist:\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "banana\n", + "apple\n", + "mango\n" + ] + } + ], + "source": [ + "# Traditional indexing\n", + "fruitslist = ['banana', 'apple', 'mango']\n", + "for i in range(len(fruitslist)):\n", + " print(fruitslist[i])\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* regular for loop uses the items\n", + "* use `enumerate` when index is needed. The function gives you back two loop variables:\n", + " - The count of the current iteration\n", + " - The value of the item at the current iteration\n", + "* use `zip` when skimming multiple lists" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, 'banana')\n", + "(1, 'apple')\n", + "(2, 'mango')\n", + "(3, 'grape')\n", + "(4, 'orange')\n", + " \n", + "1 banana\n", + "2 apple\n", + "3 mango\n", + "4 grape\n", + "5 orange\n", + " \n", + "banana\n", + "apple\n", + "mango\n", + "grape\n", + "orange\n", + " \n" + ] + } + ], + "source": [ + "fruitslist = ['banana', 'apple', 'mango', 'grape', 'orange']\n", + "# use of enumerate - tuple\n", + "for line in enumerate(fruitslist, start=0):\n", + " print(line) \n", + "print(' ')\n", + "\n", + "# use of enumerate - individual elements\n", + "for num, color in enumerate(fruitslist, start=1):\n", + " print(num, color) \n", + "print(' ')\n", + "\n", + "for num, color in enumerate(fruitslist, start=1):\n", + " print(color) \n", + "print(' ')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 grape\n", + "11 orange\n", + "12 mango\n", + "13 banana\n", + "14 apple\n" + ] + } + ], + "source": [ + "# is it working for a set?\n", + "fruitset = {'banana', 'apple', 'mango', 'grape', 'orange'}\n", + "for num, color in enumerate(fruitset, start=10):\n", + " print(num, color)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fruitslist = ['banana', 'apple', 'mango']\n", + "ratios = [0.2, 0.3, 0.1, 0.4]\n", + "winelist = ['merlot', 'chenin', 'chardonnay', 'temperanillo', 'whatever']\n", + "for fruit, ratio, wine in zip(fruitslist, ratios, winelist):\n", + " print(ratio * 100, fruit, wine)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### note: iter\n", + "\n", + "`iter` object is a container that gives you access to the next object for as long as it’s valid\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "winelist = ['merlot', 'chenin', 'chardonnay', 'temperanillo', 'whatever']\n", + "it_winelist = iter(winelist)\n", + "\n", + "print(type(it_winelist))\n", + "print(it_winelist)\n", + "\n", + "next(it_winelist)\n", + "next(it_winelist)\n", + "next(it_winelist)\n", + "next(it_winelist)\n", + "next(it_winelist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "I = iter([2, 4, 6, 8, 10])\n", + "print(type(I))\n", + "print(next(I))\n", + "print(next(I))\n", + "print(next(I))\n", + "print(next(I))\n", + "print(next(I))\n", + "print(next(I))\n", + "print(next(I))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### note: generator\n", + "\n", + "A generator is a function that produces a sequence of results instead of a single value. As generators do not store the sequence that is generated, they are memory-efficient. \n", + "\n", + "Many built in Python functions return generators to minimize use of memory - e.g. `range, zip, open` - A loop can be used to evaluate them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zz = zip(range(10), 'abcdefghij')\n", + "print(zz)\n", + "\n", + "for i,c in zz:\n", + " print(i,c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### note: comprehension\n", + "\n", + "check http://justinbois.github.io/bootcamp/2023/lessons/l15_comprehensions.html#List-comprehensions\n", + "\n", + "Syntax sugar similar to that of generator expressions can be used to create lists, sets and dictionaries. A powerful functionality within a single line of code; provides a compact way to create lists, dictionaries, sets!\n", + "\n", + "for a list:\n", + "`newlist = [expression for item in iterable if condition == True]`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# list comprehension\n", + "\n", + "lc1 = [i for i in range(10)]\n", + "print(lc1)\n", + "\n", + "lc2 = [i**2 for i in range(10) if i % 2 == 1]\n", + "print(lc2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dictionary comprehension\n", + "\n", + "names = ['ann', 'jules', 'charles']\n", + "ages = [55, 44, 45]\n", + "\n", + "dc = {a1: b1 for a1, b1 in zip(names, ages)}\n", + "print(dc)\n", + "dc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#set comprehension\n", + "\n", + "l1 = list(range(1,11))\n", + "sc = {element**3 for element in l1}\n", + "print(sc)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/debugging_basics.ipynb b/notebooks/debugging_basics.ipynb new file mode 100644 index 0000000..c394b82 --- /dev/null +++ b/notebooks/debugging_basics.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9a57d658", + "metadata": {}, + "source": [ + "# Debugging\n", + "\n", + "A short step into debugging code.\n", + "\n", + "The demo code is taken from Jake Van der Plas https://jakevdp.github.io/PythonDataScienceHandbook/01.06-errors-and-debugging.html\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc5c5da6", + "metadata": {}, + "outputs": [], + "source": [ + "def func1(a, b):\n", + " return a / b\n", + "\n", + "def func2(x):\n", + " a = x\n", + " b = x - 1\n", + " return func1(a, b)" + ] + }, + { + "cell_type": "markdown", + "id": "2bc0a427", + "metadata": {}, + "source": [ + "## Controlling exceptions\n", + "\n", + "inspired by Jake vdp\n", + "\n", + "when a Python script fails, it will raise an Exception. When the interpreter hits one of these exceptions, information about the cause of the error can be found in the traceback, which can be accessed from within Python. \n", + "\n", + "With the `%xmode` magic function, the level of information can be set by specifying the argument\n", + "- `Plain`: compact form \n", + "- `Context`: default\n", + "- `Verbose`: some extra information\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5425c8e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exception reporting mode: Context\n" + ] + } + ], + "source": [ + "# %xmode Plain\n", + "%xmode Context\n", + "# %xmode Verbose" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cffba13b", + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mfunc2\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[4], line 7\u001b[0m, in \u001b[0;36mfunc2\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m 5\u001b[0m a \u001b[38;5;241m=\u001b[39m x\n\u001b[0;32m 6\u001b[0m b \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[4], line 2\u001b[0m, in \u001b[0;36mfunc1\u001b[1;34m(a, b)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc1\u001b[39m(a, b):\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\n", + "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" + ] + } + ], + "source": [ + "func2(1)" + ] + }, + { + "cell_type": "markdown", + "id": "80b0b903", + "metadata": {}, + "source": [ + "## Debugger\n", + "The magic command `%%debug` enables to interactively use Python’s `pdb` debugger." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "90947921", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NOTE: Enter 'c' at the ipdb> prompt to continue execution.\n", + "> \u001b[1;32m\u001b[0m(3)\u001b[0;36m\u001b[1;34m()\u001b[0m\n", + "\n", + "ipdb> b func2\n", + "*** Line c:\\users\\u0015831\\appdata\\local\\temp\\ipykernel_17956\\4021589855.py:4 does not exist\n", + "ipdb> r\n", + "--Return--\n", + "None\n", + "> \u001b[1;32m\u001b[0m(3)\u001b[0;36m\u001b[1;34m()\u001b[0m\n", + "\n", + "ipdb> n\n", + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n", + "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)\n", + "Cell \u001b[1;32mIn[4], line 7\u001b[0m, in \u001b[0;36mfunc2\u001b[1;34m(x)\u001b[0m\n", + "\u001b[0;32m 5\u001b[0m a \u001b[38;5;241m=\u001b[39m x\n", + "\u001b[0;32m 6\u001b[0m b \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n", + "\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc1\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n", + "\n", + "Cell \u001b[1;32mIn[4], line 2\u001b[0m, in \u001b[0;36mfunc1\u001b[1;34m(a, b)\u001b[0m\n", + "\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunc1\u001b[39m(a, b):\n", + "\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\n", + "\n", + "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero\n" + ] + } + ], + "source": [ + "%%debug\n", + "\n", + "func2(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/dict.ipynb b/notebooks/dict.ipynb new file mode 100644 index 0000000..2cbc1d9 --- /dev/null +++ b/notebooks/dict.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)\n", + "\n", + "\n", + "## data containers\n", + "\n", + "Organize the data structure into four different families: \n", + "- Ordered data structure: list and tuple\n", + "- Unordered data structure: set and dictionary \n", + "- Mutable: list, set and dictionary \n", + "- Immutable: tuple \n", + "\n", + "\n", + "| Type | Example | Description |\n", + "|------|---------|---------|\n", + "| list | `[1, 2, 3]` | Ordered collection|\n", + "| tuple | `(1, 2, 3)` | Immutable ordered collection|\n", + "| dict | `{'a':1, 'b':2, 'c':3}` | Unordered (key:value) pair mapping|\n", + "| set | `{1, 2, 3}` | Unordered collection of unique values |\n", + "\n", + "\n", + "## operations on any sequence\n", + "\n", + "| Operation | Operator | Description |\n", + "|------|---------|---------|\n", + "| indexing | `[]` | Access an element of a sequence |\n", + "|concatenation | `+` | Combine sequences together|\n", + "| repetition | `*` | Concatenate a repeated number of times|\n", + "| membership | `in` | Ask whether an item is in a sequence |\n", + "| length | `len` | Ask the number of items in the sequence|\n", + "| slicing | `[:]` | Extract a part of a sequence |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dictionaries\n", + "- {'a':1, 'b':2, 'c':3}\n", + "- **(key,value) mapping**: the key must be immutable, mostly a string. Dictionaries in Python are used to store data values in key-value pairs. Each key-value pair maps the key to its associated value.\n", + "- **mutable**: meaning that we can change, add, or remove items after the dictionary has been created.\n", + "- **nested dictionaries**: A dictionary can contain another dictionary. \n", + "- **ordered?**: as of Python version 3.7, dictionaries are ordered. This means that the items have a defined order, and that order will not change. Note that in Python 3.5 and older dictionaries have no sense of order. In Python 3.6, dictionaries were stored in insertion order as an implementation improvement. In Python 3.7 and beyond, dictionaries are guaranteed to be ordered in the order in which their entries were created. It is therefore advisable be cautious when relying on ordering in dictionaries. For safety’s sake, it is better to assume there is no sense of order.\n", + "- **no indexing**: an index value as in `lists`and `tuples` cannot be used.\n", + "- **no duplicates**: dictionaries do not allow duplicate keys. If you try to use a duplicate key, the old value for that key will be replaced with the new value.\n", + "- **various data types**: the values in dictionary items can be of any data type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building a dictionary\n", + "\n", + "- `{}`\n", + "- `dict()`: \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#First way to setup a dictionary\n", + "zipdict = {'Hasselt': 3500, 'Luik':4000}\n", + "\n", + "#Add a couple of cities to the dictionary\n", + "zipdict['Heverlee'] = 3001\n", + "zipdict['Leuven'] = 3000\n", + "zipdict['Brussel'] = 1000\n", + "zipdict['Gent'] = 9000\n", + "\n", + "# another way\n", + "zipdict.update({'Antwerpen': 2000, 'Brugge': 8000})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(zipdict)\n", + "\n", + "print(zipdict['Leuven'])\n", + "\n", + "print(zipdict[4])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict = dict([ \n", + " ('Leuven', 'Bears'),\n", + " ('Hasselt','Limburg United'),\n", + " ('Aalst','Okapi'),\n", + " ('Oostende','BC Oostende')])\n", + "\n", + "print(basketdict)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accessing dictionary values\n", + "\n", + "- specify the corresponding key in square brackets `[]`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict['Aalst']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict['Antwerpen']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict.get('Aalst')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict.get(Aalst)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Built-in functions, operators\n", + "\n", + "- `in` use with key values\n", + "- `len`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('len(zipdict): ', len(zipdict))\n", + "\n", + "#Use the function key() - \n", + "if 'Gent' in zipdict:\n", + " print('Gent is in the dictionary', zipdict['Gent'])\n", + "else:\n", + " print('Gent is not in the dictionary')\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The order in dictionaries is kept. \n", + "\n", + "In the example below, elements are added to the dictionary in a specific order: ‘first’, ‘second’, ‘third’, and ‘fourth’. When the dictionary is printed, the elements are displayed in the order they were added. This is because, as of Python 3.7, dictionaries maintain the insertion order of their elements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a dictionary\n", + "my_dict = {}\n", + "\n", + "# Adding elements to the dictionary\n", + "my_dict['first'] = 1\n", + "my_dict['second'] = 2\n", + "my_dict['third'] = 3\n", + "my_dict['fourth'] = 4\n", + "\n", + "# Printing the dictionary\n", + "for key, value in my_dict.items():\n", + " print(f\"{key}: {value}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Methods\n", + "\n", + "- clear(): clears a dictionary\n", + "- keys(): returns a list of keys\n", + "- values(): returns a list of values\n", + "- pop(): removes a key, and returns its value\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zipdict = {'Hasselt': 3500,\n", + " 'Luik': 4000,\n", + " 'Heverlee': 3001,\n", + " 'Leuven': 3000,\n", + " 'Brussel': 1000,\n", + " 'Gent': 9000,\n", + " 'Antwerpen': 2000,\n", + " 'Brugge': 8000}\n", + "\n", + "#Use the function keys() - \n", + "keys = zipdict.keys()\n", + "print(type(keys))\n", + "print(keys)\n", + "\n", + "values = zipdict.values()\n", + "print(values)\n", + "\n", + "zipdict.pop('Gent')\n", + "print(zipdict)\n", + "\n", + "zipdictbis = zipdict\n", + "print('zipdictbis: ', zipdictbis)\n", + "zipdictbis.clear()\n", + "print('zipdictbis: ', zipdictbis)\n", + "\n", + "# alias?\n", + "print('zipdict: ', zipdict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "basketdict = dict([ \n", + " ('Leuven', 'Bears'),\n", + " ('Hasselt','Limburg United'),\n", + " ('Aalst','Okapi'),\n", + " ('Oostende','BC Oostende')])\n", + "\n", + "#Use the function keys() - \n", + "for city in basketdict.keys():\n", + " if city[0] == 'L':\n", + " print('city starting with L:', city)\n", + "\n", + "# create a list out of the keys\n", + "keys = basketdict.keys()\n", + "alist = list(keys)\n", + "alist.sort()\n", + "# print a sorted list\n", + "print(alist)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a histogram\n", + "# https://swcarpentry.github.io/python-second-language/06-dict/\n", + "numbers = [1, 0, 1, 2, 0, 0, 1, 2, 1, 3, 1, 0, 2]\n", + "count = {}\n", + "print(type(count))\n", + "for n in numbers:\n", + " if n not in count:\n", + " count[n] = 1\n", + " else:\n", + " count[n] = count[n] + 1\n", + "print(count)\n", + "print(count[1]) # 1 is a key, not an index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to create a nested dictionary: a dictionary within a dictionary. \n", + "\n", + "In this example, a dictionary is created for an observation, the observations are combined in a parent-dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Obs01 = {'name': 'experiment 1', 'val': 23.33, 'temp': 100.0, 'press': 200.0, 'date':'2022-02-02'}\n", + "Obs02 = {'name': 'experiment 2', 'val': 27.00, 'temp': 110.0, 'press': 200.0, 'date':'2022-02-02'}\n", + "Obs03 = {'name': 'experiment 3', 'val': 27.88, 'temp': 120.0, 'press': 200.0, 'date':'2022-02-03'}\n", + "series_01 = {'t1': Obs01,\n", + " 't2': Obs02,\n", + " 't3': Obs03} # insert individual dictionaries in a dictionary\n", + "# print result of the 3rd observation\n", + "print('val = {}, temp = {}'.format(series_01['t3']['val'], series_01['t3']['temp']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### aliasing\n", + "\n", + "\n", + "Because dictionaries are mutable, you need to be aware of aliasing (as with lists). Whenever two variables refer to the same dictionary object, changes to one affect the other. \n", + "\n", + "\n", + "Use the dictionary copy method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict1 = {\"key1\": \"value1\", \"key2\": \"value2\"}\n", + "dict2 = dict1\n", + "dict2[\"key2\"] = \"WHY?!\"\n", + "dict1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "\n", + "dict1 = {\"key1\": \"value1\", \"key2\": \"value2\"}\n", + "dict2 = copy.deepcopy(dict1)\n", + "dict2[\"key2\"] = \"WHY?!\"\n", + "dict1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A dictionary is an implementation of a hash table\n", + "\n", + "http://justinbois.github.io/bootcamp/2023/lessons/l09_dictionaries.html\n", + "\n", + "\n", + "Each entry in the dictionary is stored at a different location in memory. The dictionary itself also has its own address. \n", + "\n", + "Dictionaries use a hash function to do this. A hash function converts its input to an integer. \n", + "\n", + "Python’s built-in hash function can be used to convert the keys to integers.\n", + "Under the hood, Python then converts these integers to integers that could correspond to locations in memory. A collection of elements that can be indexed this way is called a hash table. This is a very common data structure in computing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create dictionary\n", + "my_dict = {'a': 6, 'b': 7, 'c':12.6}\n", + "\n", + "# Find where they are stored\n", + "print(id(my_dict))\n", + "print(id(my_dict['a']))\n", + "print(id(my_dict['b']))\n", + "print(id(my_dict['c']))\n", + "\n", + "# python hash function\n", + "print(hash('a'), hash('b'), hash('c'))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/functions.ipynb b/notebooks/functions.ipynb new file mode 100644 index 0000000..cabfbca --- /dev/null +++ b/notebooks/functions.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functions\n", + "Functions are groups of code that have a name, and can be called\n", + "- A piece of code written to carry out a specified task.\n", + "- This makes code easier to read, maintain and debug.\n", + "- Code can be tested separately.\n", + "- Must be named and created before you can use them.\n", + "- All functions are placed (in general) at the beginning of the programs. \n", + "- The input comes in parentheses after the function name\n", + " - The ordered sequence of variables is strictly called the argument list in the caller and the parameter list in the function definition. \n", + "\n", + "**syntax**\n", + "- Declare the function with the keyword `def` followed by the function name.\n", + "- Write the parameters of the function inside round braces `( )`, and end the declaration with a colon `:`.\n", + " - Empty parentheses when no parameters are used!\n", + "- Add the program statements to be executed.\n", + " - All statements are indented\n", + "- End the function with/without `return` statement.\n", + " - Without the return statement, the function will return an object None.\n", + " - Return multiple values in a tuple ()\n", + "\n", + "```python\n", + "def function_name(parameters):\n", + "\t\"\"\"docstring\"\"\"\n", + "\tstatement(s)\n", + " return statement\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Naming rules: \n", + "- Cannot use any of Python’s keywords\n", + "- No spaces\n", + "- First character must be A-Z or a-z or the “_” character\n", + "- After the first character you can use A-Z, a-z, “_” or 0-9\n", + "- Case sensitive\n", + "- Two different functions can’t have the same name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def greetings(name):\n", + " \"\"\"This function greets the name passed as an argument\"\"\"\n", + " print('Hello, ', name , ' Welcome!')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "greetings('jules')\n", + "print('type greetings: ', type(greetings))\n", + "\n", + "x = greetings('jules')\n", + "print('type x: ', type(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once a function is defined a function, call it:\n", + "- Python prompt\n", + "- from another function \n", + "- within Python interpreter environment\n", + "\n", + "To call a function, type the function name with appropriate parameters.\n", + "The interpreter must see the function definition before it can be called." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad greetings_func.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "%run greetings_func.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def greetings_more(name):\n", + " \"\"\"This function greets the name passed as an argument\"\"\"\n", + " greetings('Dear')\n", + " greetings(name) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%whos\n", + "greetings_more('Marie')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad function_1.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run function_1.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Passing arguments**\n", + "\n", + "You can call a function by using the following types of formal arguments\n", + "\n", + "- Required arguments: pass arguments 'by position'\n", + "- Keyword arguments: allows to skip arguments or place them out of order because the Python interpreter is able to use the keywords to match the values with parameters. \n", + "- Default arguments: assume a default value if a value is not provided in the function call.\n", + "- Variable-length arguments\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A function does not require arguments or returning values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# no passing of arguments\n", + "def print_header ():\n", + " columns = 40\n", + " print('this is an header')\n", + "#call\n", + "print_header()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# no passing of arguments, returning some value\n", + "def return_the_answer():\n", + " return 42\n", + "#call\n", + "print('result = ', return_the_answer())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def f_avg(num1, num2, num3, num4):\n", + " sum = num1+num2+num3+num4\n", + " avg = sum / 4\n", + " print('first value:', num1)\n", + " print (avg)\n", + "f_avg(100,90,92,77)\n", + "f_avg(100,90,92)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def f_avg(num1, num2, num3, num4=999):\n", + " sum = num1+num2+num3+num4\n", + " avg = sum / 4\n", + " print('first value:', num1)\n", + " print (avg)\n", + "f_avg(100,90,92,77)\n", + "f_avg(100,90,92)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# https://www.tutorialspoint.com/python/python_functions.htm\n", + "def printinfo( name, age ):\n", + " \"This prints a passed info into this function\"\n", + " print(\"Name: \", name)\n", + " print(\"Age \", age)\n", + " return;\n", + "\n", + "# Now you can call printinfo function\n", + "help(printinfo)\n", + "printinfo( age=50, name=\"miki\" )\n", + "printinfo( \"jane\", 45 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add some flexibility: `*args` and `**kwargs` \n", + "\n", + "`*args`: \n", + "The number of arguments needs to match. When in doubt on the number of arguments, use the * operator (unpacking operator - * before a variable means “expand this as a sequence”)\n", + "\n", + "\n", + "`**kwargs`:\n", + "works like *args, but instead of accepting positional arguments it accepts keyword arguments\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def mySum(*values):\n", + " \"\"\"\n", + " Calculate the sum of values\n", + " Input: integers/floats\n", + " Output: sum value\n", + " \"\"\"\n", + " sumVal = 0\n", + " \n", + " for each in values:\n", + " sumVal += each\n", + " \n", + " return sumVal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('run 1: ', mySum(1, 2, 3))\n", + "print('run 2: ', mySum(1, 2.3, 3.14, 98.7, 110))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate_2.py\n", + "# source https://realpython.com/python-kwargs-and-args/\n", + "def concatenate(**words):\n", + " # the iterable object is a standard dict.\n", + " result = \"\"\n", + " for arg in words.values():\n", + " result += arg\n", + " return result\n", + "\n", + "print(concatenate(a=\"Real\", b=\"Python\", c=\"Is\", d=\"Great\", e=\"!\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run function_arg_kwarg_1.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Passing arguments (by object)\n", + "\n", + "If the argument is immutable, it will not be changed, even if the function changes it. A local copy will be made within the function block. \n", + "\n", + "If the variable is mutable, consider 2 cases:\n", + "- Elements of the mutable variable can be changed in place, i.e. the list will be changed even in the caller's scope. \n", + "- If a new list is assigned to the name, the old list will not be affected, i.e. the list in the caller's scope will remain untouched.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad function_argument_pass_1.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def change_me(v):\n", + " print (\"function got:\", v, 'id(v) = ', id(v))\n", + " v = 10\n", + " print (\"argument is now:\", v, 'id(v) = ', id(v))\n", + "\n", + "def change_me_bis(v):\n", + " print (\"function got:\", v, 'id(v) = ', id(v))\n", + " v.append(555)\n", + " print (\"argument is now:\", v, 'id(v) = ', id(v))\n", + "\n", + "def change_me_tris(v):\n", + " print (\"function got:\", v, 'id(v) = ', id(v))\n", + " v = v * 3\n", + " print (\"argument is now:\", v, 'id(v) = ', id(v))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myvar = 5\n", + "print (\"starting with:\", myvar)\n", + "change_me(myvar)\n", + "print (\"ending with:\", myvar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myvar2 = [1, 2, 3]\n", + "print (\"starting with:\", myvar2)\n", + "change_me(myvar2)\n", + "print (\"ending with:\", myvar2)\n", + "\n", + "print (\"starting with:\", myvar2)\n", + "change_me_bis(myvar2)\n", + "print (\"ending with:\", myvar2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myvar = 5\n", + "print (\"starting with:\", myvar)\n", + "change_me_tris(myvar)\n", + "print (\"ending with:\", myvar)\n", + "\n", + "myvar2 = [1, 2, 3]\n", + "print (\"starting with:\", myvar2)\n", + "change_me_tris(myvar2)\n", + "print (\"ending with:\", myvar2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Return values\n", + "\n", + "- Values are returned by the return statement. It can return an item or an expression. \n", + "- Return: immediate exit.\n", + "- Python functions can return only one item but that item can be any object, in particular a tuple, list, or dictionary.\n", + "- A function may have multiple return statements but only one (the first encountered) will be executed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function definition is here\n", + "def calcul( arg1, arg2 ):\n", + " # Add both the parameters and return them.\"\n", + " calc_sum = arg1 + arg2\n", + " calc_prod = arg1 * arg2\n", + " if (calc_sum >= 10):\n", + " return calc_sum\n", + " if (calc_prod < 0):\n", + " return calc_prod\n", + " return (calc_sum, calc_prod)\n", + "\n", + "\n", + "r1 = calcul(3, 7.89)\n", + "print(r1)\n", + "r2 = calcul(-3, 7.89)\n", + "print(r2)\n", + "r3 = calcul(0.56, 7.89)\n", + "print(r3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scope\n", + "- Functions can be considered as a mini program\n", + "- Variables can be created inside functions\n", + " - are considered local to that function. \n", + " - they only exist within that function. \n", + " - Objects outside the scope of the function will not be able to access that variable \n", + " - To create a global variable inside a function, you can use the global keyword.\n", + " Be careful with global variables\n", + "- Variables that are only referenced inside a function are implicitly global. \u000b", + "If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scope variables\n", + "\n", + "- local: variables are defined inside a function body \n", + "- global: variables defined outside" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# var = 'ff'\n", + "# var_bis = 'ffw'\n", + "\n", + "def ex(s): \n", + " global var \n", + " var = 'abc'\n", + " var_bis = 'def'\n", + " print('inside the function var is ', var)\n", + " print('inside the function var_bis is ', var_bis)\n", + " return s+var\n", + "\n", + "\n", + "print(ex('f'))\n", + "print('outside the function var is ', var)\n", + "print('outside the function var_bis is ', var_bis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad function_scope_5.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run function_scope_5.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module import\n", + "\n", + "In Python you can reuse code by `import`ing functions from files: import a definition from a module. A file containing the functions is called a module. The file name is the module name with the suffix .py\n", + "\n", + "- Explicit module import : Explicit import of a module preserves the module's content in a namespace. The namespace is then used to refer to its contents with a \".\" between them.\n", + "- Explicit module import by alias. For long module names, use the \"import ... as ...\" \n", + "- Explicit import of module contents: Sometimes rather than importing the module namespace, just like import a few particular items from the module, use \"from ... import ...“\n", + "- Implicit import of module contents: it is sometimes useful to import the entirety of the module contents into the local namespace. This can be done with the \"from ... import *\" \n", + "This pattern should be used sparingly, if at all. The problem is that such imports can sometimes overwrite function names that you do not intend to overwrite, and the implicitness of the statement makes it difficult to determine what has changed.\n", + "\n", + "https://nbviewer.jupyter.org/github/jakevdp/WhirlwindTourOfPython/blob/master/13-Modules-and-Packages.ipynb\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "math.cos(math.pi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.cos(np.pi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from math import cos, pi\n", + "cos(pi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from math import *\n", + "sin(pi) ** 2 + cos(pi) ** 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lambda expressions create anonymous functions " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = lambda a : a + 10\n", + "f = lambda a, b: a + b \n", + "v1 = 8.8\n", + "\n", + "print('type x:', type(x))\n", + "print(x(6))\n", + "print(x(v1))\n", + "\n", + "print('type f:', type(f))\n", + "print(f(5.6,6))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/getting_started_jupyter.ipynb b/notebooks/getting_started_jupyter.ipynb new file mode 100644 index 0000000..27e5a5a --- /dev/null +++ b/notebooks/getting_started_jupyter.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started with Jupyter\n", + "\n", + "See Also: [documentation](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Notebook%20Basics.html)\n", + "\n", + "No Python installed? Go to https://try.jupyter.org. \n", + "\n", + "\n", + "Start the notebook server from the command line:\n", + "\n", + "`jupyter notebook`\n", + "\n", + "When the notebook opens in your browser, you will see the Notebook Dashboard, which will show a list of the notebooks, files, and subdirectories in the directory where the notebook server was started. Most of the time, you will wish to start a notebook server in the highest level directory containing notebooks. Often this will be your home directory. Or start the notebook in the folder where you keep your notebook projects.\n", + "\n", + "\n", + "more text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basics\n", + "\n", + "This document is a Jupyter notebook, it is built out of cells: text and code. The text cells can be formatted using the *markdown* syntax. The code cells contain code statements, usually Python code. \n", + "It is an interactive environment that enbles you to execute the code cells. \n", + "\n", + "This text block is a *Markdown Cell*. Double click this text and you will be able to edit the text. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tip: for Mac users, the buttons are different, see the list below\n", + "\n", + "- `Ctrl`: command key ⌘\n", + "- `Shift`: Shift ⇧\n", + "- `Alt`: option ⌥" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## cell mode\n", + "\n", + "Cells can be in 2 modes: **command** mode and **edit** mode. \n", + "\n", + "### Edit mode \n", + "\n", + "- Is indicated by a *green cell border* and a prompt showing in the editor area\n", + "- When a cell is in edit mode, you can type into the cell, like a normal text editor.\n", + "- Enter edit mode by pressing `Enter` or using the mouse to click on a cell’s editor area.\n", + "\n", + "### Command mode\n", + "\n", + "- Is indicated by a *grey cell border with a blue left margin*\n", + "- When you are in command mode, you are able to manipulate the notebook as a whole, but not type into the individual cell. \n", + "- Enter command mode by pressing `Esc` or click the mouse outside the cell’s editor area." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Execute a cell\n", + "\n", + "Running a cell works for both text and code cells.\n", + "\n", + "- `Shift + Enter` runs the current cell, move to cell below, add a new cell when no cell is below\n", + "- `Ctrl + Enter` runs the selected cell \n", + "- `Alt + Enter` runs the current cell, insert a cell below \n", + "\n", + "The piece of code below is a *Code Cell* with a short Python command, computing a value and storing it in a variable. This variable is printed. \n", + "\n", + "To execute the code in the cell below, select it with a click and then either press the `Run` button from the menu bar, or use the keyboard shortcut `Ctrl + enter` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_val = 24 * 69\n", + "print(my_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Menu / Toolbar\n", + "\n", + "All the actions that you can do on a cell are possible from the Menu / Toolbar. \n", + "\n", + "- You can always start by cleaning up the notebook: `Cell > All Output > Clear`\n", + "- You can run the whole notebook: `Cell > Run All`\n", + "- When in trouble, restart the Kernel: `Kernel > Restart`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "1+2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Useful shortcuts while in *command* mode\n", + " \n", + "- `Enter` from command into edit mode\n", + "- `H` show all shortcuts\n", + "- `A` insert cell above\n", + "- `B` insert cell below\n", + "- `X` cut selected cell\n", + "- `C` copy selected cell\n", + "- `V` paste cells below\n", + "- `Shift + V` paste cell above\n", + "- `D D` (press the key twice) delete selected cell\n", + "- `Z` undo cell deletion\n", + "- `Y` change the cell type to Code\n", + "- `M` change the cell type to Markdown\n", + "- `P` open the command palette.\n", + "- `Shift + Space` scroll notebook up\n", + "- `Space` scroll notebook down" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "1 < 22.3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "string = 'Hello'\n", + "print(string)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### hands-on\n", + "\n", + "- select a cell, copy it and paste it\n", + "- edit the cell and make some changes\n", + "- execute the cell\n", + "- delete the cell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "1 <= -22.3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Useful shortcuts while in *edit* mode\n", + " \n", + "- `Esc` from edit into command mode\n", + "- `Tab` code completion or indent\n", + "- `Shift + Tab` tooltip\n", + "- `Ctrl + A` select all\n", + "- `Ctrl + Z` undo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Useful to know\n", + "\n", + "- All cells modify the same global state, so variables that you define by executing a cell can be used in other cells\n", + "- `[prompt number] in front of a code cell`, indicates the execution number. The kernel should have a single, monotonically increasing counter of all execution requests\n", + "- `[*] instead of a prompt number`, indicates that the kernel is busy. Go to the menu Kernel and click Interrupt if you suspect the program . If this does not work click Restart.\n", + "- tab completion works!\n", + "\n", + "### Magic commands\n", + "\n", + "A magic function begins with either a `%` or `%%` sign, and let you perform various useful tasks. Those with a `%` sign work within the environment, and those with a `%%` sign work at the cell level.\n", + "Information of a specific magic function is obtained by putting a `?` behind the command e.g. %magicfunction?\n", + "\n", + "- `%lsmagic`Display the list of magic functions\n", + "- `%time` Times a single statement.\n", + "- `%reset` Delete all variables and names defined in the current namespace.\n", + "- `%run` Run a python script inside a notebook. %run script.py\n", + "- `%who, %who_ls, %whos` Display variables defined in the interactive namespace, with varying levels of verbosity\n", + "- `%pwd`Display the current working directory\n", + "- `!`Run a OS command from within Jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%lsmagic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run first_sum.py\n", + "\n", + "%time %run first_sum.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# lauch notepad editor (windows)\n", + "!notepad first_sum.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%cd Temp/PythonDev/\n", + "%pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "who" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "%%time is a cell magic command that measures the execution time of the cell. \n", + "\n", + "The cell contains a Python command that makes the program sleep for two seconds. \n", + "\n", + "The %%time command will measure and print the time it took to execute the cell. \n", + "\n", + "Note that cell magics only work in IPython and Jupyter Notebooks. They are not part of the standard Python language." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "import time\n", + "time.sleep(2) # sleep for two seconds" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/intro_to_numpy.ipynb b/notebooks/intro_to_numpy.ipynb new file mode 100644 index 0000000..f865196 --- /dev/null +++ b/notebooks/intro_to_numpy.ipynb @@ -0,0 +1,824 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to Numpy arrays\n", + "\n", + "\n", + "sources:\n", + "\n", + "* https://pythonnumericalmethods.berkeley.edu/notebooks/chapter02.07-Introducing_numpy_arrays.html\n", + "* http://justinbois.github.io/bootcamp/2023/lessons/l21_intro_to_numpy_and_scipy.html\n", + "* https://numpy.org/doc/stable/user/basics.creation.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the NumPy package with the `np` alias, the convention is to use the `np` abbreviation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A very brief introduction to NumPy arrays\n", + "\n", + "The central object for NumPy and SciPy is `ndarray`, \"NumPy array.\" This is an array object that is convenient for scientific computing. \n", + "\n", + "NumPy arrays are homogeneous, meaning all elements must be of the same data type. They are similar to Python lists but offer more functionality, especially for numerical operations.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an array\n", + "\n", + "There are several mechanisms for creating arrays as mentioned in the NumPy documentation:\n", + "\n", + "* Conversion from other Python structures (i.e. lists and tuples)\n", + "* Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.)\n", + "* Replicating, joining, or mutating existing arrays\n", + "* Reading arrays from disk, either from standard or custom formats\n", + "* Creating arrays from raw bytes through the use of strings or buffers\n", + "* Use of special library functions (e.g., random)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conversion from other Python structures\n", + "\n", + "convert to a NumPy array using `np.array()`\n", + "\n", + "NumPy arrays can be defined using Python sequences such as lists and tuples. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 1D numpy array\n", + "arr1 = np.array([10, 20, 30, 40])\n", + "\n", + "print(arr1)\n", + "print(type(arr1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Create a 2D numpy array\n", + "arr2 = np.array([[10,20,30],[40,50,60]])\n", + "\n", + "print(arr2)\n", + "print(type(arr2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 2D numpy array\n", + "arr3 = np.array([[[10, 20], [30, 40]], [[50, 60], [70, 80]]])\n", + "\n", + "print(arr3)\n", + "print(type(arr3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a 1D numpy array\n", + "arrt1 = np.array((10.2, 20, 30, 40))\n", + "\n", + "print(arrt1)\n", + "print(type(arrt1))\n", + "\n", + "# Create a 2D numpy array\n", + "arrt2 = np.array(((10, 20, 30, 40),(50.2,60,70,80)))\n", + "\n", + "print(arrt2)\n", + "print(type(arrt2))\n", + "\n", + "# Create a 2D numpy array\n", + "arrt3 = np.array((((10, 20), (30, 40.2)), ((50, 60), (70, 80))))\n", + "\n", + "print(arrt3)\n", + "print(type(arrt3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using numpy.array to define a new array, consider the `dtype` of the elements in the array, which can be specified explicitly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr1_int8 = np.array([120, 121, 122], dtype=np.int8)\n", + "print(arr1_int8)\n", + "print(type(arr1_int8))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(np.array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intrinsic NumPy array creation functions \n", + "\n", + "use `arange`, `ones`, `zeros`, etc.\n", + "check the routines at: https://numpy.org/doc/stable/reference/routines.array-creation.html#routines-array-creation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr_i_1 = np.arange(10)\n", + "print(arr_i_1)\n", + "\n", + "arr_i_2 = np.arange(2, 10, dtype=float)\n", + "print(arr_i_2)\n", + "\n", + "arr_i_3 = np.arange(2, 3, 0.1)\n", + "print(arr_i_3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "az_1 = np.zeros(5)\n", + "print(az_1)\n", + "print(az_1.dtype)\n", + "\n", + "az_2 = np.zeros((5,), dtype=int)\n", + "print(az_2)\n", + "print(az_2.dtype)\n", + "\n", + "az_3 = np.zeros((2, 1))\n", + "print(az_3)\n", + "\n", + "az_4 = np.zeros((2, 2))\n", + "print(az_4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a NumPy array filled with zeros the same shape as another NumPy array\n", + "arr22 = np.array([[1, 2], [3, 4]])\n", + "arr_0_22 = np.zeros_like(arr22)\n", + "print(arr_0_22)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr_d = np.diag([1, 2, 300.])\n", + "print(arr_d)\n", + "\n", + "arr_e = np.eye(3)\n", + "print(arr_e)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reshaping, concatenating arrays\n", + "Once arrays are been created, these can be transformed to create new arrays. \n", + "When you assign an array or its elements to a new variable, you have to explicitly numpy.copy the array, otherwise the variable is a view into the original array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# reshape with shape \n", + "vec1 = np.linspace(3, 9, 10)\n", + "print(vec1)\n", + "\n", + "vec1.shape = (2,5)\n", + "print(vec1)\n", + "\n", + "# a new 2D array using the reshape() method\n", + "arr_vec1 = vec1.reshape((5, 2))\n", + "print(arr_vec1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Concatenating arrays\n", + "\n", + "The `np.concatenate()` function accomplishes this. We simply have to pass it a tuple containing the NumPy arrays we want to concatenate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr2 = np.array([[10,20,30],[40,50,60]])\n", + "arr2b = np.array([[101,201,301],[401,501,601]])\n", + "\n", + "combi_1 = np.concatenate((arr2,arr2b))\n", + "print(combi_1)\n", + "\n", + "combi_2 = np.concatenate((arr2,arr2b), axis=1)\n", + "print(combi_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## array object\n", + "An array has several attributes and lots of methods, check it with the `dir` function\n", + "\n", + "* `shape`: Returns the dimensions of the array.\n", + "* `dtype`: Returns the data type of the elements in the array.\n", + "* `size`: Returns the total number of elements in the array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "dir(np.ndarray)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(np.ndarray.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The data type of stored entries\n", + "print(arr3.dtype)\n", + "print(arrt3.dtype)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The shape of the array\n", + "print(arr1.shape)\n", + "print(arr2.shape)\n", + "print(arr3.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The size of the array, showing the number of elements\n", + "print(arr1.size)\n", + "print(arr2.size)\n", + "print(arr3.size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also lots of methods. \n", + "\n", + "`astype()` converts the data type of the array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(arr2)\n", + "print(arr2.dtype)\n", + "\n", + "float_arr2 = arr2.astype(float)\n", + "print(float_arr2)\n", + "print(float_arr2.dtype)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are many others, compute summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(arr2.max())\n", + "print(arr2.min())\n", + "print(arr2.sum())\n", + "print(arr2.mean())\n", + "print(arr2.std())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NumPy arrays can be arguments to NumPy functions, doing the same operations as the methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(np.max(arr2))\n", + "print(np.min(arr2))\n", + "print(np.sum(arr2))\n", + "print(np.mean(arr2))\n", + "print(np.std(arr2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indexing\n", + "\n", + "Getting access to the 1D numpy array is similar to lists or tuples, it has an index to indicate the location. It is 0-based, and accepts negative indices for indexing from the end of the array.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.array([1, 4, 3])\n", + "\n", + "# get an element at a specific index\n", + "print(arr[1])\n", + "\n", + "# linspace(a,b,n) generates an array of n equally spaced elements starting from a and ending at b.\n", + "vec1 = np.linspace(3, 9, 10)\n", + "print(vec1)\n", + "\n", + "print(vec1[-1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is not necessary to separate each dimension’s index into its own set of square brackets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr2 = np.array([[10,20,30],[40,50,60]])\n", + "\n", + "print(arr2[1,2])\n", + "print(arr2[1][2])\n", + "\n", + "# stay between the boundaries\n", + "print(arr2[2,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Slicing NumPy arrays\n", + "\n", + "Slice NumPy arrays like lists and tuples. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.array([1, 4, 3])\n", + "\n", + "# Reversed array\n", + "arr[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Entries 1 to 2\n", + "arr[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fancy indexing\n", + "\n", + "NumPy arrays also allow **fancy indexing**, where we can slice out specific values. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x100 = np.arange(1,101)\n", + "x100[[1, 19, 6]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Boolean indexing can be used with Numpy arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# slice out the big ones\n", + "idx_bool = (x100 > 80)\n", + "print(idx_bool)\n", + "\n", + "big_ones = x100[idx_bool]\n", + "print(big_ones)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the indices where the values are high through `np.where()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.where(x100 > 80)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy arrays are mutable\n", + "\n", + "NumPy arrays are mutable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make an array\n", + "my_ar = np.array([1, 2, 3, 4])\n", + "\n", + "# Change an element\n", + "my_ar[2] = 6\n", + "\n", + "# See the result\n", + "my_ar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's try attaching another variable to the NumPy array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Attach a new variable\n", + "my_ar2 = my_ar\n", + "\n", + "# Set an entry using the new variable\n", + "my_ar2[3] = 9\n", + "\n", + "# Does the original change? (yes.)\n", + "my_ar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how messing with NumPy in functions affects things." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Array from Existing Array\n", + "Create an array from an existing array by copying array elements into the other array.\n", + "\n", + "Using the `copy()` Method\n", + "To create an array from an existing NumPy array Python provides an in-built method that is the copy() method. In simpler words to copy the array elements into another array. If you make changes in an original array that will not be reflected in a copy method. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create array from existing array\n", + "# Using copy()\n", + "arr_0=np.array([10,20,30])\n", + "print(\"Original array\",arr_0)\n", + "print('id arr_0', id(arr_0))\n", + "\n", + "arr_1 = arr_0\n", + "print('arr_1', arr_1)\n", + "print('id arr_1', id(arr_1))\n", + "\n", + "arr_2=arr_0.copy()\n", + "print('arr_2', arr_2)\n", + "print('id arr_2', id(arr_2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Slices of NumPy arrays are **views**, not copies\n", + "\n", + "A very important distinction between NumPy arrays and lists is that slices of NumPy arrays are **views** into the original NumPy array, NOT copies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make list and array\n", + "my_list = [1, 2, 3, 4]\n", + "print(my_list)\n", + "my_ar = np.array(my_list)\n", + "print(my_ar)\n", + "\n", + "# Slice out of each\n", + "my_list_slice = my_list[1:-1]\n", + "print(my_list_slice)\n", + "my_ar_slice = my_ar[1:-1]\n", + "print(my_ar_slice)\n", + "\n", + "# Mess with the slices\n", + "my_list_slice[0] = 9\n", + "my_ar_slice[0] = 9\n", + "\n", + "# Look at originals\n", + "print(my_list)\n", + "print(my_ar)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mathematical operations with arrays\n", + "\n", + "Mathematical operations on arrays are done elementwise to all elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([1, 2, 3])\n", + "b = np.array([4, 5, 6])\n", + "\n", + "# Element-wise addition\n", + "c = a + b\n", + "print(c)\n", + "\n", + "# Element-wise multiplication\n", + "d = a * b\n", + "print(d)\n", + "\n", + "# Element-wise division\n", + "e = a / b\n", + "print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Multiply by scalar\n", + "-4 * a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Raise to power\n", + "b**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "matrix operations like dot products." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "help(np.dot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([[1, 2], [3, 4]])\n", + "b = np.array([[1, 1], [1, 1]])\n", + "c = np.dot(a, b)\n", + "print(c)\n", + "\n", + "\n", + "a = np.array([1, 2, 3])\n", + "b = np.array([4, 5, 6])\n", + "c = np.dot(a, b)\n", + "print(c)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy mathematical functions\n", + "The NumPy functions also work elementwise on the arrays when it is intuitive to do soNumPy provides a set of universal functions that operate element-wise on arrays, including mathematical, logical, and bitwise operations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Showing all functions in NumPy\n", + "dir(np)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arrx = np.array([1, 2, 3])\n", + "\n", + "print(np.square(arrx)) # Square each element\n", + "\n", + "print(np.sin(arrx)) # Calculate sine of each element" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/io.ipynb b/notebooks/io.ipynb new file mode 100644 index 0000000..90cae95 --- /dev/null +++ b/notebooks/io.ipynb @@ -0,0 +1,970 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Console IO\n", + "see also https://realpython.com/python-input-output/\n", + "\n", + "### input\n", + "Take input from the user: `input()`\n", + "\n", + "- a prompt can be included\n", + "- `input()` returns a string. \n", + "- need for a numeric type, convert the string to the appropriate type with the `int()`, `float()`, or `complex()` " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name = input('Enter your name: ')\n", + "print('Hello ' + name + '!')\n", + "print(type(name))\n", + "age = input('Enter your age: ')\n", + "print(age)\n", + "print(type(age))\n", + "\n", + "#use typecasting\n", + "xString = input(\"Enter a number: \")\n", + "x = float(xString)\n", + "yString = input(\"Enter a second number: \")\n", + "y = float(yString)\n", + "print('The sum of ', x, ' and ', y, ' is ', x+y, '.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run input_1.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### output\n", + "\n", + "Display output to the user: `print()`. print automatically puts a single space between items and a newline at the end.\n", + "\n", + "Syntax: `print(objects, sep=' ', end='\\n', file=sys.stdout, flush=False)`\n", + "\n", + "\n", + "- unformatted \n", + " - list variables separated by comma\n", + " - complex types like lists, dictionaries, etc. can be displayed\n", + "- formatted\n", + " - keywords\n", + " - sophisticated way cfr. Matlab `fprintf`, C `printf`\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fname = 'Louis'\n", + "age = 33\n", + "print('Name:', fname, ', age:', age)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l1 = [1, 2, 3]\n", + "numb = -12\n", + "d1 = {'key1': 1, 'key2': 22}\n", + "print(l1, numb, d1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sep\n", + "print('text', 42, [1, 'zz', 3])\n", + "print('text', 42, [1, 'zz', 3], sep='/')\n", + "print('text', 42, [1, 'zz', 3], sep='...')\n", + "\n", + "# end\n", + "print('text', 42, [1, 'zz', 3], end='/')\n", + "print('text', 42, [1, 'zz', 3], end='/\\n')\n", + "\n", + "for n in range(5):\n", + " print(n)\n", + "\n", + "for n in range(8):\n", + " print(n, end=(' ' if n < 5 else '\\n'))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "fill-in-the-braces\n", + "\n", + "Syntax: `S.format(item0, item1, item2, ..., itemk)`\n", + "\n", + "- string type has `format()` method which takes arguments and returns another string which is the original with the method’s arguments inserted into it in certain places. \n", + "- `format()` replaces each pair of curly brackets with the corresponding argument. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s1 = 'The {} in the {}'.format('car', 'room') \n", + "print(s1)\n", + "s2 = '{2} color {1}, {0} {3}'.format('dark', 'brown', 'yellow', 'red') \n", + "print(s2)\n", + "s3 = '{c} color {b}, {a} {d}'.format(a='dark', b='brown', c='yellow', d='red') \n", + "print(s3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### format specifiers\n", + "\n", + "| Conversion | Description |\n", + "|------|-------------------|\n", + "| d | Signed integer decimal.|\n", + "| i | Signed integer decimal.|\n", + "| o | Unsigned octal.|\n", + "| x | Unsigned hexadecimal(lowercase).|\n", + "| X | Unsigned hexadecimal(uppercase).|\n", + "| e | Floating point exponential format (lowercase).|\n", + "| E | Floating point exponential format (uppercase).|\n", + "| f,F | Floating point.|\n", + "| g | Same as \"e\" if exponent is greater than -4 or less than precision, \"f\" otherwise.|\n", + "| G | Same as \"G\" if exponent is greater than -4 or less than precision, \"F\" otherwise.|\n", + "| c | Single character (accepts integer or single character string).|\n", + "| s | String (converts any python object using str()).|\n", + "\n", + "\n", + "Justification codes\n", + "\n", + "|< |left justifies|\n", + "| > |right justifies|\n", + "| ^| centers|\n", + "\n", + "Filling characters\n", + "\n", + "- Character after : pads with that character\n", + "- `+` before the field width forces a preceding sign\n", + "\n", + "\n", + "Escape characters\n", + "\n", + "| Conversion | Description |\n", + "|------|-------------------|\n", + "|single quote \t| \\'|\n", + "|double quote \t|\\\"|\n", + "|backslash \t\t|\\\\|\n", + "|alert (bell) \t|\t\\a|\n", + "|backspace \t\t|\\b|\n", + "|formfeed \t\t|\\f|\n", + "|newline \t\t|\\n|\n", + "|return \t\t|\\r|\n", + "|tab \t\t\t|\\t|\n", + "|vertical tab \t|\t\\v|\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# formatted output\n", + "count = 5 \n", + "amount = 45.56 \n", + "print('count is {0} and amount is {1:9.5f}'.format(count, amount))\n", + "print('count is {0:5d} and amount is {1:9.5f}'.format(count, amount))\n", + "print('count is {:d} and amount is {:f}'.format(count, amount))\n", + "print('count is {:05d} and amount is {:09.5f}'.format(count, amount))\n", + "print('count is {:+5d} and amount is {:09.5f}'.format(count, amount))\n", + "\n", + "person = 'Louis'\n", + "greeting = 'Hello, {}!'.format(person)\n", + "print(greeting)\n", + "greeting = 'Hello, {0}, Good morning, {0}!'.format(person)\n", + "print(greeting)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# f-string\n", + "count = 5 \n", + "amount = 45.56 \n", + "print(f'count is {count} and amount is {amount:9.5f}')\n", + "print(f'count is {count:5d} and amount is {amount:9.5f}')\n", + "print(f'count is {count:d} and amount is {amount:f}')\n", + "print(f'count is {count:05d} and amount is {amount:09.5f}')\n", + "print(f'count is {count:+5d} and amount is {amount:09.5f}')\n", + "\n", + "person = 'Louis'\n", + "greeting = f'Hello, {person}!'\n", + "print(greeting)\n", + "greeting = f'Hello, {person}, Good morning, {person}!'\n", + "print(greeting)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Command line IO\n", + "\n", + "- command line parameters live inside of sys.argv, so import sys is needed. \n", + "- sys.argv is a list in Python, which contains the command-line arguments passed to the script.\n", + "- The name of the program is always the first item (argv[0])\n", + "- The elements of argv are strings\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad command_line_io.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run command_line_io.py a b 1 hello" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File IO\n", + "\n", + "### Motivation\n", + "\n", + "Storing data is important when working over multiple sessions and sharing the results with collaborators. When Python closes, all the variables in the memory are lost, data must be stored in some way. \n", + "\n", + "### Steps involved\n", + "Working with files, involves the steps:\n", + "* Opening the file and associating it with a file handle. (`open`)\n", + "* Using the file handle: read from or write to a file\n", + "* Closing the file handle to commit to a disk file. (Do not forget!) (`close`)\n", + "\n", + "open a file:\n", + "```python\n", + "f = open(\"name.txt\", mode=\"w\", encoding=\"utf8\")\n", + "```\n", + "The parameters are:\n", + "* name of the file, it can also contain an absolute or a relative path\n", + "* `mode` argument defines how to access the file. \n", + "\n", + "Character | Meaning\n", + ":--------:|--------\n", + "'r' | open for reading (default)\n", + "'w' | open for writing, truncating the file first\n", + "'x' | open for exclusive creation, failing if the file already exists\n", + "'a' | open for writing, appending to the end of the file if it exists\n", + "\n", + "* `encoding` parameter defines the character encoding. It is not mandatory.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Writing to a file\n", + "- `f.write(string)`\n", + " - Copies the value of string to the file\n", + " - Returns the number of characters written\n", + "\n", + "- `f.writelines(list)`\n", + " - Copies the value of each element of sequence to the file\n", + " - Does not add a newline after each string - must be explicitly included in the string" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "username = input('Enter your name: ')\n", + "f = open('name_01.txt', mode='w', encoding='utf8')\n", + "numchar = f.write(username)\n", + "print(numchar)\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type name_01.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# no automatic generation of 'new lines'\n", + "name1 = 'KU Leuven'\n", + "name2 = 'campus Arenberg'\n", + "listnames = [name1, name2]\n", + "f = open('name_02.txt', mode='w', encoding='utf8')\n", + "f.write(name1)\n", + "f.write(name2)\n", + "f.write('\\n')\n", + "f.writelines(listnames)\n", + "f.write('\\n')\n", + "# or add a new line for each item\n", + "f.writelines(line + '\\n' for line in listnames)\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KU Leuvencampus Arenberg\n", + "KU Leuvencampus Arenberg\n", + "KU Leuven\n", + "campus Arenberg\n" + ] + } + ], + "source": [ + "!type name_02.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "write the squares of the values 0, 1, ..., 9 into a file\n", + "\n", + "remark the use of the `with` statement and the block of code. A close statement is not neede when using this approach" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# write to a file\n", + "file_name = 'file_squares.txt'\n", + "with open(file_name, 'w') as text_file:\n", + " for i in range(0, 10):\n", + "# text_file.write('{0}: {1}\\n'.format(i, i*i))\n", + " text_file.write(f'{i}: {i*i}\\n')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: 0\n", + "1: 1\n", + "2: 4\n", + "3: 9\n", + "4: 16\n", + "5: 25\n", + "6: 36\n", + "7: 49\n", + "8: 64\n", + "9: 81\n" + ] + } + ], + "source": [ + "!type file_squares.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# add to a file\n", + "file_name = 'file_squares.txt'\n", + "with open(file_name, 'a') as text_file:\n", + " for i in range(10, 20):\n", + " text_file.write('{0}: {1}\\n'.format(i, i*i))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type file_squares.txt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run file_io_write_1.py\n", + "!type testfile-write.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read from a file\n", + "\n", + "* `f.read()` reads the whole file from the current position to the end of the file as a string\n", + "* `f.read(b)` reads `b` bytes of the file, as data is read, a pointer is set of where the next data will be read\n", + "* `f.readline()` reads the data from the current position in the file to the end of that line. \n", + "* `f.readlines()` reads every line of the file and stores them in a list.\n", + "\n", + "To read the whole text file line by line, you can use `for` loop with the file object:\n", + "```python\n", + "for line in f:\n", + " print(line)\n", + "```\n", + "\n", + "Caution: `read`, `readline` and `readlines` do not check memory to perform the task." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: 0\n", + "1: 1\n", + "2: 4\n", + "3: 9\n", + "4: 16\n", + "5: 25\n", + "6: 36\n", + "7: 49\n", + "8: 64\n", + "9: 81\n", + "\n", + "--\n", + "\n" + ] + } + ], + "source": [ + "txtfile = open(\"file_squares.txt\",\"r\")\n", + "txt = txtfile.read()\n", + "print(txt)\n", + "print('--')\n", + "# what will be printed?\n", + "txtbis = txtfile.read()\n", + "print(txtbis)\n", + "txtfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: 0\n", + "\n", + "--\n", + "1: 1\n", + "2: 4\n", + "3: 9\n", + "4: 16\n", + "5: 2\n" + ] + } + ], + "source": [ + "txtfile = open(\"file_squares.txt\",\"r\")\n", + "txt = txtfile.read(5)\n", + "print(txt)\n", + "print('--')\n", + "# what will be printed?\n", + "txtbis = txtfile.read(25)\n", + "print(txtbis)\n", + "txtfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: 0\n", + "\n", + "--\n", + "1: 1\n", + "\n" + ] + } + ], + "source": [ + "txtfile = open(\"file_squares.txt\",\"r\")\n", + "txt = txtfile.readline()\n", + "print(txt)\n", + "print('--')\n", + "# what will be printed?\n", + "txtbis = txtfile.readline()\n", + "print(txtbis)\n", + "txtfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0: 0\\n', '1: 1\\n', '2: 4\\n', '3: 9\\n', '4: 16\\n', '5: 25\\n', '6: 36\\n', '7: 49\\n', '8: 64\\n', '9: 81\\n']\n", + "--\n", + "[]\n" + ] + } + ], + "source": [ + "txtfile = open(\"file_squares.txt\",\"r\")\n", + "txt = txtfile.readlines()\n", + "print(txt)\n", + "print('--')\n", + "# what will be printed?\n", + "txtbis = txtfile.readlines()\n", + "print(txtbis)\n", + "txtfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "some text Lorem ipsum and also quam\n", + "some text Mauris venenatis and also Cras\n", + "some text Nunc dictum, and also vel\n", + "some text Aenean accumsan and also varius\n", + "some text Curabitur commodo and also id\n", + "some text In faucibus and also diam.\n", + "some text Orci varius and also ridiculus\n", + "some text Curabitur interdum, and also nulla,\n", + "some text Aliquam vitae and also nibh\n", + "some text Phasellus posuere and also mollis\n", + "some text Sed malesuada and also sodales\n", + "some text Nulla condimentum and also vel\n", + "some text Cras et and also nisi\n", + "some text Vestibulum vel and also ut\n", + "some text Suspendisse id and also tincidunt\n", + "some text Nulla sed and also urna\n", + "some text Donec tristique and also Donec\n", + "some text Donec facilisis and also velit\n", + "some text Pellentesque suscipit, and also et\n", + "some text Suspendisse orci and also Quisque\n", + "some text Nulla pretium and also nisl,\n", + "some text Sed eu and also ultrices\n", + "some text Nam diam and also Cras\n", + "some text Phasellus gravida and also vitae\n", + "some text Maecenas ligula and also Morbi\n" + ] + } + ], + "source": [ + "txtfile = open(\"test_text.txt\",\"r\")\n", + "\n", + "for aline in txtfile:\n", + " values = aline.split()\n", + " if len(values) > 10:\n", + " print('some text', values[0], values[1], ' and also ', values[10] )\n", + "\n", + "txtfile.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "seek() method\n", + "\n", + "`fileObject.seek(offset[, whence])`\n", + "\n", + "Parameters\n", + "\n", + "- offset: position of the read/write pointer within the file.\n", + "- whence: optional parameter, defaults to 0 which means absolute file positioning, 1 which means seek relative to the current position and 2 means seek relative to the file's end." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "txtfile = open(\"file_squares.txt\",\"r\")\n", + "# set the file handle to some spot in the file\n", + "txtfile.seek(50)\n", + "txt = txtfile.read()\n", + "print(txt)\n", + "txtfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type file_io_3.py\n", + "%run file_io_3.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type file_io_readline.py\n", + "%run file_io_readline.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!type file_io_readlines.py\n", + "%run file_io_readlines.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Pythonic way:\n", + "\n", + "```python\n", + "with open('file_path', 'w') as file: \n", + " file.write('hello world !') \n", + "```\n", + "\n", + "The `with` statement automatically takes care of closing the file once it leaves the with block, even in cases of error.\n", + "no need to call `file.close()` when using with statement. \n", + "\n", + "`with` statement itself ensures proper acquisition and release of resources. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice example from bernd Klein: Numerisches Python\n", + "\n", + "- open a file as input\n", + "- read the data, split the lines, manipulate the data\n", + "- write the results to another file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example from bernd Klein: Numerisches Python\n", + "with open('./Data/bKlein_bundeslaender.txt') as fh_in, \\\n", + " open('./Data/bKlein_bundeslaender_bis.txt', 'w') as fh_out:\n", + " fh_in.readline() # read header input file\n", + " fh_out.write('Land Surface Population PopulationDensity \\n')\n", + " for line in fh_in:\n", + " land, surf, man, women = line.split() # get a line and split into text components\n", + " surf = float(surf)\n", + " population = int(man) + int(women)\n", + " pDensity = round(population * 1000/ surf, 2)\n", + " fh_out.write(land + \" \" + str(surf) + \" \" + str(population) + \" \" + \\\n", + " str(pDensity) + \"\\n\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IO and data formats\n", + "\n", + "use the __batteries included__\n", + "\n", + "Python provides the csv library. While you can also just simply use `split` function, to separate lines and data within each line, the CSV module can also be used to make things easy.\n", + "Each row read from the csv file is returned as a list of strings.\n", + "\n", + "https://docs.python.org/3/library/csv.html\n", + "\n", + "The csv module's `reader` and `writer` objects read and write sequences. Read and write data in dictionary form using the `DictReader` and `DictWriter` classes.\n", + "\n", + "- `reader`\treturns a reader object which iterates over lines of a CSV file\n", + "- `writer`\treturns a writer object which writes data into CSV file\n", + "- `DictReader` class operates like a regular reader but maps the information read into a dictionary. The keys for the dictionary can be inferred from the first row of the CSV file\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "with open('MOCK_DATA.csv') as csvfile:\n", + " readCSV = csv.reader(csvfile, delimiter=',')\n", + " for row in readCSV:\n", + " print(row)\n", + " print(row[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "\n", + "with open('MOCK_DATA.csv') as csvfile:\n", + " readCSV = csv.reader(csvfile, delimiter=',')\n", + " dates = []\n", + " ipads = []\n", + " for row in readCSV:\n", + " date = row[6]\n", + " ipad = row[5]\n", + " dates.append(date)\n", + " ipads.append(ipad)\n", + "\n", + "print(dates)\n", + "print(ipads)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "file_name = 'MOCK_DATA.csv'\n", + "f_csv = open(file_name)\n", + "csv_data = csv.DictReader(f_csv)\n", + "print(\"Type of object csv_data: {}\\n\".format(type(csv_data)))\n", + "f_csv.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "file_name = 'MOCK_DATA.csv'\n", + "f_csv = open(file_name)\n", + "csv_data = csv.DictReader(f_csv)\n", + "\n", + "line_count = 1\n", + "data_dict = dict()\n", + "for row in csv_data:\n", + "\n", + "# print('Row {:d}: {} (length: {})'.format(line_count, dict(row), len(row)))\n", + " data_dict[line_count] = row\n", + " line_count += 1\n", + " \n", + "f_csv.close()\n", + "\n", + "# use dictionary\n", + "print(data_dict[54])\n", + "print(data_dict[154]['last_name'])\n", + "print(data_dict[254]['ip_address'])\n", + "print(data_dict[354]['unit cost'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "copy data from a file to another" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "with open('MOCK_DATA_out.csv' ,'w') as outFile:\n", + " fileWriter = csv.writer(outFile)\n", + " with open('MOCK_DATA.csv','r') as inFile:\n", + " fileReader = csv.reader(inFile)\n", + " for row in fileReader:\n", + " fileWriter.writerow(row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad 'MOCK_DATA_out.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use writerow\n", + "import csv\n", + "csvData = [('Peter', '22'), ('Jasmine', '21'), ('Sam', '24')]\n", + "csvFile = open('person.csv', 'w', newline='')\n", + "obj=csv.writer(csvFile)\n", + "\n", + "for person in csvData:\n", + " obj.writerow(person)\n", + " \n", + "csvFile.close()\n", + "\n", + "#use writerows\n", + "csvfile = open('persons_bis.csv','w', newline='')\n", + "obj = csv.writer(csvfile)\n", + "obj.writerows(csvData)\n", + "csvFile.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad 'person.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad 'persons_bis.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/list.ipynb b/notebooks/list.ipynb new file mode 100644 index 0000000..37edc8d --- /dev/null +++ b/notebooks/list.ipynb @@ -0,0 +1,804 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)\n", + "\n", + "\n", + "## data containers\n", + "\n", + "Organize the data structure into four different families: \n", + "- Ordered data structure: list and tuple\n", + "- Unordered data structure: set and dictionary \n", + "- Mutable: list, set and dictionary \n", + "- Immutable: tuple \n", + "\n", + "\n", + "| Type | Example | Description |\n", + "|------|---------|---------|\n", + "| list | `[1, 2, 3]` | Ordered collection|\n", + "| tuple | `(1, 2, 3)` | Immutable ordered collection|\n", + "| dict | `{'a':1, 'b':2, 'c':3}` | Unordered (key:value) pair mapping|\n", + "| set | `{1, 2, 3}` | Unordered collection of unique values |\n", + "\n", + "\n", + "## operations on any sequence\n", + "\n", + "| Operation | Operator | Description |\n", + "|------|---------|---------|\n", + "| indexing | `[]` | Access an element of a sequence |\n", + "|concatenation | `+` | Combine sequences together|\n", + "| repetition | `*` | Concatenate a repeated number of times|\n", + "| membership | `in` | Ask whether an item is in a sequence |\n", + "| length | `len` | Ask the number of items in the sequence|\n", + "| slicing | `[:]` | Extract a part of a sequence |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lists\n", + "\n", + "based on http://ehmatthes.github.io/pcc/cheatsheets/README.html\n", + "\n", + "- use square brackets to define a list,\n", + "- []\n", + "- use commas to separate individual items in the list\n", + "- Syntax: < variable > = [< comma separated expressions >]\n", + "- Lists are **ordered** collections of objects. \n", + " - **heterogeneous**: elements of a list don't have to be the same type. Each element of the list can be of any type.\n", + " - **indexing**: elements can be referenced by an index. \n", + " - **nesting**: lists can be nested to arbitrary depth\n", + " - **mutable**: lists are changeable\n", + " - **duplicates allowed**: lists are indexed, items can have the same value\n", + " \n", + "\n", + "*Note:* \n", + "* is different from the Matlab array, closer to the Matlab cell array\n", + "* is different from the C array\n", + "* tip: use plural names for lists, to make your code easier to read." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## creation\n", + "\n", + "- Create the list with brackets `[ ]` \n", + "- Inside the brackets, the elements are separated by a comma (,). \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zips = [3001, 3000, 1000, 9000, 8000]\n", + "test_lists = ['test1','test2',3, [1, 2, 7]] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## accessing elements of a list\n", + "\n", + "- Individual elements in a list are accessed according to their index. \n", + "- The index is 0 based.\n", + "- Use square brackets []\n", + "- Syntax: < var name >[< index >], where index is an integer expression\n", + "\n", + "negative index: \n", + "- -1 corresponds to the last element\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "first = zips[0]\n", + "print(first)\n", + "last = zips[-1]\n", + "print(last)\n", + "print(test_lists[2])\n", + "print(test_lists[3])\n", + "print(test_lists[3][0])\n", + "\n", + "#Refer to the index of the item you want to modify.\n", + "zips[1] = 8500\n", + "zips[-2] = 2000\n", + "print(zips)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "whos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Slicing lists, to create sublists\n", + "\n", + "- Assigning to slices\n", + "- All slice operations return a new list containing the requested elements\n", + "- Syntax: < var name >[< index1 >:< index2 >[:step]]\n", + " - index1: Optional. Starting index of the slice. Defaults to 0.\n", + " - index2: Optional. The last index of the slice or the number of items to get. Defaults to len(sequence).\n", + " - step: Optional. Extended slice syntax. Step value of the slice. Defaults to 1.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = list(range(1, 6))\n", + "print('l', l)\n", + "l_sub = l[2:4]\n", + "print('l_sub', l_sub)\n", + "l_sub = l[:4]\n", + "print('l_sub', l_sub)\n", + "l_sub = l[2:]\n", + "print('l_sub', l_sub)\n", + "l_sub = l[0:4:3]\n", + "print('l_sub', l_sub)\n", + "l_sub = l[::2]\n", + "print('l_sub', l_sub)\n", + "l_sub = l[4:1:-1]\n", + "print('l_sub', l_sub)\n", + "\n", + "l_r = l[::-1]\n", + "print('l_r', l_r)\n", + "\n", + "#create a copy\n", + "l_bis = l[:]\n", + "print('l_bis', l_bis)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Slice assignment is allowed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = list(range(1, 6))\n", + "print('l:', l)\n", + "\n", + "# replace a chunk with the same number of items\n", + "print(l[::2])\n", + "l[::2] = ['a', 'b', 'c'] \n", + "print('l:', l)\n", + "\n", + "# replace a chunk with a larger number of items\n", + "print(l[:3])\n", + "l[:3] = [999, 999, 999, 999, 999]\n", + "print('l:', l)\n", + "\n", + "# replace a chunk with a smaller number of items\n", + "print(l[:3])\n", + "l[:3] = [55555]\n", + "print('l:', l)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir(list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add elements to the end of a list, \n", + "or insert at wherever position in the list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(type(zips))\n", + "print(dir(zips))\n", + "print('original: ', zips)\n", + "zips.append(3500)\n", + "zips.insert(0,3050)\n", + "zips.insert(3,4000)\n", + "print('modified: ', zips)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More operations on lists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate with +\n", + "l = [1,2,3] + [4,5,6]\n", + "print(l)\n", + "l1 = [1, 2]\n", + "l2 = ['a', 'b', 9, True]\n", + "l3 = l1 + l2\n", + "print('l3', l3)\n", + "\n", + "# multiply a list\n", + "l = [1, 2]\n", + "l3 = l * 3\n", + "print('l3:', l3)\n", + "\n", + "# multiply to quickly initialize a list\n", + "list0 = [0]*10\n", + "print('list0: ', list0)\n", + "\n", + "# beware\n", + "list1 = [1,2,3]\n", + "list1_3 = list1*3\n", + "print(list1, ' / ', list1_3)\n", + "list1[2]=4\n", + "print(list1, ' / ', list1_3)\n", + "\n", + "list1 = [1,2,3]\n", + "list1_3 = [list1]*3\n", + "print(list1, ' / ', list1_3)\n", + "list1[2]=4\n", + "print(list1, ' / ', list1_3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use a list as a stack, appends and pops from the end of list are\n", + "fast!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack = [3.01, 40, 'hello']\n", + "print(stack)\n", + "\n", + "# add elements to the list\n", + "stack.append(6)\n", + "stack.append(7.77)\n", + "print(stack)\n", + "\n", + "# remove last elements added using pop\n", + "stack.pop()\n", + "print(stack)\n", + "stack.pop()\n", + "print(stack)\n", + "stack.pop()\n", + "print(stack)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removing elements from a list\n", + "\n", + "* `remove`: delete a specific object in the list. Remove is the only one that searches object (not index based).\n", + "* `del`: delete the object at a specific location (index) in the list.\n", + "* `pop`: delete the object at a specific location (index) in the list and get the object at the specific location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# remove elements by their position in a list, \n", + "# or by the value of the item. Python removes only the first occurrence of that value\"\n", + "zips = [3050, 3001, 8500, 4000, 1000, 2000, 8000, 3500]\n", + "\n", + "print('zips: ', zips)\n", + "del zips[1]\n", + "print('zips: ', zips)\n", + "del zips[1:4]\n", + "print('zips: ', zips)\n", + "zips.remove(8000)\n", + "print('zips: ', zips)\n", + "zips.remove(8000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pop() returns the last element in the list, but any index can be specified.\n", + "recent_zip = zips.pop()\n", + "print('zips: ', zips)\n", + "first_zip = zips.pop(0)\n", + "print('zips: ', zips)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The len() function returns the number of items\n", + "num_zips = len(zips)\n", + "print('num_zips = ', num_zips)\n", + "\n", + "# The sort() method changes the order of a list permanently. \n", + "# The sorted() function returns a copy of the list, leaving the original list unchanged. \"\n", + "zips.sort()\n", + "print('zips (sorted): ', zips)\n", + "zips.sort(reverse=True)\n", + "print('zips (sorted - reverse): ', zips)\n", + "\n", + "zips = [3050, 3001, 8500, 4000, 1000, 2000, 8000, 3500]\n", + "zs = sorted(zips)\n", + "print('zips: ', zips)\n", + "print('zs (using sorted): ', zs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zips = [3050, 3001, 8500, 4000, 1000, 2000, 8000, 3500]\n", + "\n", + "#Python loop pulls each item from the list one at a time and stores it in a temporary variable, which you provide a name for.\n", + "for zip in zips:\n", + " print(zip)\n", + "# number of elements\n", + "numel = len(zips)\n", + "print('number of elements in zips: ', numel)\n", + "# Simple statistics are possible on a list containing numerical data.\n", + "smallest = min(zips)\n", + "largest = max(zips)\n", + "sumzips = sum(zips)\n", + "print('min = {}, max = {}, sum = {}'.format(smallest, largest, sumzips))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conversion\n", + "\n", + "`list()`: takes sequence types and converts them to lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xl = list('help')\n", + "print(xl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `range`\n", + "\n", + "The `range` function returns a range object from start to one less than the stop value, a step size can be set (only integers)\n", + "\n", + "`range(start, stop, step)`\n", + "\n", + "create a list: convert the range object into a list\n", + "`LR = list(range(2, 9, 3))`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "R1 = range(2,9,3)\n", + "print(R1)\n", + "print('type R1', type(R1))\n", + "LR1 = list(R1)\n", + "print(LR1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xr = range(10)\n", + "print(type(xr))\n", + "\n", + "for i in range(10):\n", + " print(i, end=' ')\n", + "print('\\n') \n", + "\n", + "# enumerate\n", + "L1 = [2, 4, 6, 8, 10]\n", + "for i, val in enumerate(L1):\n", + " print(i, val)\n", + "print('\\n') \n", + " \n", + "L2 = ['pizza', 'pasta', 'salad', 'nachos']\n", + "print(list(enumerate(L2)))\n", + "for i, val in enumerate(L2):\n", + " print(i, val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## membership\n", + "\n", + "Check if some item is a member of the list\n", + "\n", + "Syntax: < expression > `in` < iterable >): Returns True/False if < expression > is a member of the list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = [2, 3, 4, 5, 6, 8]\n", + "print('3 in x :', 3 in x)\n", + "print('7 in x :', 7 in x)\n", + "\n", + "l3 = ['help', [1, 2], 5, 2]\n", + "print(1 in l3)\n", + "print([1,2] in l3)\n", + "print('e' in l3)\n", + "print('help' in l3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## aliasing\n", + "\n", + "If variable v1 refers to an object and an assignment is made v2 = v1, then both variables refer to the same object.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It’s important to note that any modifications to the alias object will reflect in the existing object and vice versa, because they both point to the same memory location.\n", + "\n", + "If the aliased object is mutable, changes made with one alias affect the other!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "v1 = [1, 2, 3]\n", + "v2 = v1\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n", + "print(v1 is v2)\n", + "print(\"Reference of first_variable:\", id(v1))\n", + "print(\"Reference of second_variable:\", id(v2))\n", + "\n", + "v1[1] = 101\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### shallow copy\n", + "\n", + "A partial solution is to use the list constructor or the slice operation to make a new list via a shallow copy. But if the list contains other compound types then they won't be independently copied\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use slicing notation to create a shallow copy\n", + "v1 = [1, 2, 3]\n", + "v2 = v1[:]\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n", + "print(v1 is v2)\n", + "print(\"Reference of first_variable:\", id(v1))\n", + "print(\"Reference of second_variable:\", id(v2))\n", + "\n", + "v1[1] = 101\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use the list function to create a shallow copy\n", + "v1 = [1, 2, 3]\n", + "v2 = list(v1)\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n", + "print(v1 is v2)\n", + "print(\"Reference of first_variable:\", id(v1))\n", + "print(\"Reference of second_variable:\", id(v2))\n", + "\n", + "v1[1] = 101\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use the copy function to create a shallow copy\n", + "v1 = [1, 2, 3]\n", + "v2 = v1.copy()\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n", + "print(v1 is v2)\n", + "print(\"Reference of first_variable:\", id(v1))\n", + "print(\"Reference of second_variable:\", id(v2))\n", + "\n", + "v1[1] = 101\n", + "print('v1 = ', v1)\n", + "print('v2 = ', v2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# be careful with a shallow copy on compound types!\n", + "# example taken from: https://cvw.cac.cornell.edu/python-intro/objects-and-oop/lists\n", + "\n", + "myList = [[1,2,3],4]\n", + "print(myList)\n", + "myOtherList = myList.copy() # use the copy method of the list, is a shallow copy\n", + "\n", + "myList[1] = 'four'\n", + "print(myList)\n", + "print (myOtherList)\n", + "\n", + "#OK so far, but now try to change an element in the sublist\n", + "myList[0][1] = 'two'\n", + "print (myList)\n", + "\n", + "#This time the item also changes in the shallow copy, because it was in a sublist\n", + "print (myOtherList)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Safer to avoid aliasing when working with mutable objects.\n", + "\n", + "The way to make a completely independent copy of a list —a deep copy— is to use the the `copy.deepcopy()` method. \n", + "`copy.deepcopy()` to makes copies of compound objects, as the errors which can occur from shallow copies are often frustrating to debug.\n", + "\n", + "Python’s standard library provides the copy module, which provides functions that can be used to create deep copies of objects.\n", + "\n", + "https://cvw.cac.cornell.edu/python-intro/objects-and-oop/lists" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "myList = [[1,2,3],4]\n", + "\n", + "#Making a deep copy, myOtherList\n", + "myOtherList = deepcopy(myList)\n", + "\n", + "#Reassigning an element of the sublist in myList\n", + "myList[0][1] = 'two'\n", + "print(myList)\n", + "#No effect on myOtherList\n", + "print(myOtherList)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### list unpacking / packing\n", + "\n", + "assign elements of a list to multiple variables.\n", + "\n", + "packing: wraps the arguments into a single variable. Putting the asterisk (*) in front of a variable name, you’ll pack the leftover elements into a list and assign them to a variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[a, b, c, d, e] = [1, 2, 3, 4, 5]\n", + "print(a,b,c, d, e)\n", + "\n", + "# try\n", + "#[a, b, c] = [1, 2, 3, 4, 5]\n", + "\n", + "[a, *b, c] = [1, 2, 3, 4, 5]\n", + "print(a,b,c)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " List packing is the process of bundling several values into a single iterable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num1 = 1\n", + "num2 = 2\n", + "num3 = 3\n", + "*num, = num1, num2, num3\n", + "print(num)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### list comprehension\n", + "\n", + "Comprehension: powerful functionality within a single line of code; provides a compact way to create lists. \n", + "\n", + "The general syntax of list comprehension is: `[expression for item in list if condition]`. The if condition part is optional. List comprehension provides a more concise and readable way to create lists compared to using loops.\n", + "\n", + "Works also for dictionaries and sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sqlist = [x * x for x in range(15)]\n", + "print(sqlist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# with if statement\n", + "even_numbers = [num for num in range(1, 10) if num % 2 == 0]\n", + "print(even_numbers) # Output: [2, 4, 6, 8]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# using if else\n", + "numbers = [1, 2, 3, 4, 5, 6]\n", + "even_odd_list = [\"Even\" if i % 2 == 0 else \"Odd\" for i in numbers]\n", + "print(even_odd_list) # Output: ['Odd', 'Even', 'Odd', 'Even', 'Odd', 'Even']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example from Python Turial\n", + "[(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/operators.ipynb b/notebooks/operators.ipynb new file mode 100644 index 0000000..8deee20 --- /dev/null +++ b/notebooks/operators.ipynb @@ -0,0 +1,457 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Operators\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Operators\n", + "## Arithmetic operators\n", + "\n", + "| Operator | Name | Meaning |\n", + "|----------|------|-------------|\n", + "| a + b | addition | sum of a and b|\n", + "| a - b | subtraction | difference of a and b|\n", + "| a * b | multiplication | product of a and b|\n", + "| a / b | true division | quotient of a and b|\n", + "| a // b | floor division | quotient of a and b, removing fractional part|\n", + "| a % b | modulus | remainder after division of a by b|\n", + "| a ** b | exponentiation | a raised to power of b|\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# working with integers\n", + "a = 13\n", + "b = 3\n", + "\n", + "# Output: a + b\n", + "print('a + b =', a + b)\n", + "# Output: a - b\n", + "print('a - b =', a - b)\n", + "# Output: a * b\n", + "print('a * b =', a * b)\n", + "# Output: a / b\n", + "print('a / b =', a / b)\n", + "# Output: a // b\n", + "print('a // b =', a // b)\n", + "# Output: a % b\n", + "print('a % b =', a % b)\n", + "# Output: a ** b\n", + "print('a ** b =', a ** b)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example with real numbers\n", + "a = 1.2\n", + "b = 8.75\n", + "# Output: a + b\n", + "print('a + b =', a + b)\n", + "# Output: a - b\n", + "print('a - b =', a - b)\n", + "# Output: a * b\n", + "print('a * b =', a * b)\n", + "# Output: a / b\n", + "print('a / b =', a / b)\n", + "# Output: a // b\n", + "print('a // b =', a // b)\n", + "# Output: a % b\n", + "print('a % b =', a % b)\n", + "# Output: a ** b\n", + "print('a ** b =', a ** b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison operators\n", + "\n", + "| Operator | Meaning |\n", + "|----------|---------|\n", + "| >\t| Greater than - True if left operand is greater than the right |\n", + "| <\t|Less that - True if left operand is less than the right |\n", + "| == |\tEqual to - True if both operands are equal |\n", + "| != |\tNot equal to - True if operands are not equal |\n", + "| >= |\tGreater than or equal to - True if left operand is greater than or equal to the right |\n", + "| <= |\tLess than or equal to - True if left operand is less than or equal to the right |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 13\n", + "b = 3.6\n", + "\n", + "# Output: a > b\n", + "print('a > b :', a > b)\n", + "# Output: a < b\n", + "print('a < b :', a < b)\n", + "# Output: a == b\n", + "print('a == b :', a == b)\n", + "# Output: a != b\n", + "print('a != b :', a != b)\n", + "# Output: a >= b\n", + "print('a >= b :', a >= b)\n", + "# Output: a <= b\n", + "print('a <= b :', a <= b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, we can also use\n", + "```python\n", + "a < b < c\n", + "```\n", + "as a shorthand for\n", + "```python\n", + "a < b and b < c\n", + "```\n", + "and\n", + "```python\n", + "a < b > c\n", + "```\n", + "as a shorthand for\n", + "```python\n", + "a < b and b > c\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"1 < 2 < 3:\", 1 < 2 < 3)\n", + "print(\"1 < 2 < 1:\", 1 < 2 < 1)\n", + "print(\"1 < 2 > 3:\", 1 < 2 > 3)\n", + "print(\"1 < 2 > 1:\", 1 < 2 > 1)\n", + "\n", + "print(\"1 < 2 < 3 < 99 :\", 1 < 2 < 3 < 99)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logical operators\n", + "\n", + "Logical operators are the `and`, `or`, `not` operators.\n", + "\n", + "| Operator | Meaning |\n", + "|----------|---------|\n", + "| and | True if both the operands are true |\n", + "| or | True if either of the operands is true |\n", + "| not | True if operand is false (complements the operand) |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = True\n", + "b = False\n", + "\n", + "# Output: a and b\n", + "print('a and b :', a and b)\n", + "# Output: a or b\n", + "print('a or b :', a or b)\n", + "# Output: not a\n", + "print('not a :', not a)\n", + "\n", + "# what happens with numbers?\n", + "a = 1.5\n", + "b = 0\n", + "\n", + "# Output: a and b\n", + "print('a and b :', a and b)\n", + "# Output: a or b\n", + "print('a or b :', a or b)\n", + "# Output: not a\n", + "print('not a :', not a)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assignment operation\n", + "\n", + "`a = value` (regular assignment)\n", + "\n", + "`a = a + val` is a common operation to increase a variable a by some fixed amount val, we can write:\n", + "`a = a + val`\n", + "\n", + "`a OP= b` is equivalent to `a = a OP b`\n", + "\n", + "| Operator | Example | Meaning |\n", + "|----------|---------|---------|\n", + "| =\t| a = 5\t| a = 5 |\n", + "| += | a += 5 | a = a + 5 |\n", + "| -= | a -= 5\t| a = a - 5 |\n", + "| *= | a *= 5\t| a = a * 5 |\n", + "| /= | a /= 5\t| a = a / 5 |\n", + "| %= | a %= 5\t| a = a % 5 |\n", + "| //= | a //= 5 | a = a // 5 |\n", + "| **= | a **= 5\t| a = a ** 5 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 5\n", + "print('a : ', a)\n", + "a += 5\n", + "print('a : ', a)\n", + "a -= 5\n", + "print('a : ', a)\n", + "a *= 5\n", + "print('a : ', a)\n", + "a /= 5\n", + "print('a : ', a)\n", + "a //= 2\n", + "print('a : ', a)\n", + "a **= 5\n", + "print('a : ', a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "x = x + val\n", + "a common operation to increase a variable x by some fixed amount val, we can write:\n", + "\n", + "x = x + val\n", + "\n", + "x += val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 9.9\n", + "print(x)\n", + "x = x + 8\n", + "print(x)\n", + "x += 5.5\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Special operator\n", + "\n", + "Identity operator `is` is used to check if two values (or variables) are located on the same part of the memory. Two variables that are equal does not imply that they are identical.\n", + "\n", + "| Operator | Meaning |\n", + "|----------|---------|\n", + "|is | True if the operands are identical (refer to the same object) |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 9\n", + "b = a\n", + "print('a is b', a is b)\n", + "print(id(a))\n", + "print(id(b))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 9\n", + "b = 9\n", + "print('a == b', a == b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 9.9\n", + "b = 9.9\n", + "print('a is b', a is b)\n", + "print('id(a)', id(a))\n", + "print('id(b)', id(b))\n", + "print('a == b', a == b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bit operators\n", + "\n", + "| Operator | Meaning | Description |\n", + "|----------|---------|-------------|\n", + "|`a & b` | Bitwise AND | Bits defined in both a and b |\n", + "|`a \\| b` | Bitwise OR | Bits defined in a or b or both |\n", + "|`a ^ b` | Bitwise XOR | Bits defined in a or b but not both|\n", + "|`a << b` | Shift left | Shift bits of a left by b units |\n", + "|`a >> b` | Shift right | Bits defined in both a and b |\n", + "|`~a` | Bitwise NOT | Bitwise negation of a|\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example taken from https://www.tutorialspoint.com/python/bitwise_operators_example.htm\n", + "\n", + "a = 60 # 60 = 0011 1100 \n", + "b = 13 # 13 = 0000 1101 \n", + "print('a = ', a, ' bin(a) = ', bin(a))\n", + "print('b = ', b, ' bin(b) = ', bin(b))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = a & b; # 12 = 0000 1100\n", + "print ('Value of c is ', c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = a | b; # 61 = 0011 1101 \n", + "print ('Value of c is ', c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = a ^ b; # 49 = 0011 0001\n", + "print ('Value of c is ', c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = a << 2; # 240 = 1111 0000 - a multiplied by 4\n", + "print ('Value of c is ', c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = a >> 2; # 15 = 0000 1111 - a divided by 4\n", + "print ('Value of c is ', c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = ~a; # -61 = 1100 0011\n", + "print ('Value of c is ', c)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/quick_start_matplotlib.ipynb b/notebooks/quick_start_matplotlib.ipynb new file mode 100644 index 0000000..2eb0577 --- /dev/null +++ b/notebooks/quick_start_matplotlib.ipynb @@ -0,0 +1,1117 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# In recent versions of Jupyter notebooks, the command %matplotlib inline is unnecessary \n", + "# as the plots are displayed below the cell by default.\n", + "%matplotlib inline\n", + "\n", + "# Display output in separate window, interactivity is possible\n", + "#%matplotlib\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# matplotlib: an anthology\n", + "\n", + "This tutorial covers some basic usage patterns and best practices to\n", + "help you get started with Matplotlib.\n", + "\n", + "`pyplot` provides a convenient interface to the matplotlib object-oriented plotting library. It is modeled closely after Matlab(TM). Therefore, the majority of plotting commands in pyplot have Matlab(TM) analogs with similar arguments. \n", + "\n", + "resources used:\n", + "\n", + "- https://matplotlib.org/stable/tutorials/introductory/quick_start.html\n", + "- https://www.stat.berkeley.edu/~nelle/teaching/2017-visualization/README.html\n", + "- https://github.com/rougier/matplotlib-tutorial\n", + "- https://github.com/stefmolin/python-data-viz-workshop/blob/main/notebooks/1-getting_started_with_matplotlib.ipynb\n", + "- https://realpython.com/python-matplotlib-guide/\n", + "\n", + "\n", + "## pyplot vs. Object-Oriented interface\n", + "source: https://matplotlib.org/matplotblog/posts/pyplot-vs-object-oriented-interface/\n", + "\n", + "When using matplotlib we have two approaches:\n", + "\n", + "- implicit: pyplot interface / functional interface.\n", + "- explicit: Object-Oriented interface (OO).\n", + "\n", + "Check https://matplotlib.org/stable/users/explain/api_interfaces.html#api-interfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pyplot interface\n", + "matplotlib on the surface is made to imitate MATLAB's method of generating plots, which is called pyplot. All the pyplot commands make changes and modify the same figure. This is a state-based interface, where the state (i.e., the figure) is preserved through various function calls (i.e., the methods that modify the figure). This interface allows us to quickly and easily generate plots. The state-based nature of the interface allows us to add elements and/or modify the plot as we need, when we need it.\n", + "\n", + "This interface shares a lot of similarities in syntax and methodology with MATLAB.\n", + "The example below draws the graph of distance vs time\n", + "\n", + "```python\n", + "# matplotlib_first_plot.py\n", + "# using pyplot functions\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for plotting\n", + "time = [2, 4, 6, 8, 10]\n", + "distance = [1, 4, 9, 19, 39]\n", + "velocity = [1, 16, 26, 36, 111]\n", + "\n", + "plt.figure(figsize=(9,7), dpi=100)\n", + "\n", + "plt.plot(time,distance,'bo-', label='distance')\n", + "plt.plot(time,velocity, label='velocity') # scaling, another y-axis???\n", + "\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Distance\")\n", + "\n", + "plt.legend()\n", + "plt.grid(True)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run matplotlib_first_plot.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Object-Oriented interface\n", + "When using the OO interface, it helps to know how the matplotlib structures its plots. The final plot that we see as the output is a ‘Figure’ object. The Figure object is the top level container for all the other elements that make up the graphic image. These other elements are called _Artists_. Artists are basically all the elements that are rendered onto the figure. This can include text, patches (like arrows and shapes), etc. The Figure object can be thought of as a canvas, upon which different artists act to create the final graphic image. This Figure can contain any number of various artists.\n", + "\n", + "Each plot that we see in a figure, is an Axes object. The Axes object holds the actual data that we are going to display. It will also contain X- and Y-axis labels, a title. Each Axes object will contain two or more Axis objects.\n", + "The Axis objects set the data limits. It also contains the ticks and ticks labels. ticks are the marks that we see on a axis.\n", + "\n", + "```python\n", + "# matplotlib_first_plot_oo.py\n", + "\n", + "# using oo approach\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Data for plotting\n", + "time = [2, 4, 6, 8, 10]\n", + "distance = [1, 4, 9, 19, 39]\n", + "velocity = [1, 16, 26, 36, 111]\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "ax1.set_ylabel(\"distance (m)\")\n", + "ax1.set_xlabel(\"time\")\n", + "ax1.plot(time, distance, \"blue\")\n", + "\n", + "ax2 = ax1.twinx() # create another y-axis sharing a common x-axis\n", + "\n", + "\n", + "ax2.set_ylabel(\"velocity (m/s)\")\n", + "ax2.set_xlabel(\"time\")\n", + "ax2.plot(time, velocity, \"green\")\n", + "\n", + "fig.set_size_inches(7,5)\n", + "fig.set_dpi(100)\n", + "\n", + "plt.show()\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run matplotlib_first_plot_oo.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Showcase example code: Anatomy of a figure\n", + "\n", + "The code below shows the name of several matplotlib elements composing a figure\n", + "\n", + "source: https://matplotlib.org/2.0.2/examples/showcase/anatomy.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Anatomy of a figure\n", + "# This figure shows the name of several matplotlib elements composing a figure\n", + "# https://matplotlib.org/2.0.2/examples/showcase/anatomy.html\n", + "\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.ticker import AutoMinorLocator, MultipleLocator, FuncFormatter\n", + "\n", + "\n", + "np.random.seed(19680801)\n", + "\n", + "X = np.linspace(0.5, 3.5, 100)\n", + "Y1 = 3+np.cos(X)\n", + "Y2 = 1+np.cos(1+X/0.75)/2\n", + "Y3 = np.random.uniform(Y1, Y2, len(X))\n", + "\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(1, 1, 1, aspect=1)\n", + "\n", + "\n", + "def minor_tick(x, pos):\n", + " if not x % 1.0:\n", + " return \"\"\n", + " return \"%.2f\" % x\n", + "\n", + "ax.xaxis.set_major_locator(MultipleLocator(1.000))\n", + "ax.xaxis.set_minor_locator(AutoMinorLocator(4))\n", + "ax.yaxis.set_major_locator(MultipleLocator(1.000))\n", + "ax.yaxis.set_minor_locator(AutoMinorLocator(4))\n", + "ax.xaxis.set_minor_formatter(FuncFormatter(minor_tick))\n", + "\n", + "ax.set_xlim(0, 4)\n", + "ax.set_ylim(0, 4)\n", + "\n", + "ax.tick_params(which='major', width=1.0)\n", + "ax.tick_params(which='major', length=10)\n", + "ax.tick_params(which='minor', width=1.0, labelsize=10)\n", + "ax.tick_params(which='minor', length=5, labelsize=10, labelcolor='0.25')\n", + "\n", + "ax.grid(linestyle=\"--\", linewidth=0.5, color='.25', zorder=-10)\n", + "\n", + "ax.plot(X, Y1, c=(0.25, 0.25, 1.00), lw=2, label=\"Blue signal\", zorder=10)\n", + "ax.plot(X, Y2, c=(1.00, 0.25, 0.25), lw=2, label=\"Red signal\")\n", + "ax.plot(X, Y3, linewidth=0,\n", + " marker='o', markerfacecolor='w', markeredgecolor='k')\n", + "\n", + "ax.set_title(\"Anatomy of a figure\", fontsize=20, verticalalignment='bottom')\n", + "ax.set_xlabel(\"X axis label\")\n", + "ax.set_ylabel(\"Y axis label\")\n", + "\n", + "ax.legend()\n", + "\n", + "\n", + "def circle(x, y, radius=0.15):\n", + " from matplotlib.patches import Circle\n", + " from matplotlib.patheffects import withStroke\n", + " circle = Circle((x, y), radius, clip_on=False, zorder=10, linewidth=1,\n", + " edgecolor='black', facecolor=(0, 0, 0, .0125),\n", + " path_effects=[withStroke(linewidth=5, foreground='w')])\n", + " ax.add_artist(circle)\n", + "\n", + "\n", + "def text(x, y, text):\n", + " ax.text(x, y, text, backgroundcolor=\"white\",\n", + " ha='center', va='top', weight='bold', color='blue')\n", + "\n", + "\n", + "# Minor tick\n", + "circle(0.50, -0.10)\n", + "text(0.50, -0.32, \"Minor tick label\")\n", + "\n", + "# Major tick\n", + "circle(-0.03, 4.00)\n", + "text(0.03, 3.80, \"Major tick\")\n", + "\n", + "# Minor tick\n", + "circle(0.00, 3.50)\n", + "text(0.00, 3.30, \"Minor tick\")\n", + "\n", + "# Major tick label\n", + "circle(-0.15, 3.00)\n", + "text(-0.15, 2.80, \"Major tick label\")\n", + "\n", + "# X Label\n", + "circle(1.80, -0.27)\n", + "text(1.80, -0.45, \"X axis label\")\n", + "\n", + "# Y Label\n", + "circle(-0.27, 1.80)\n", + "text(-0.27, 1.6, \"Y axis label\")\n", + "\n", + "# Title\n", + "circle(1.60, 4.13)\n", + "text(1.60, 3.93, \"Title\")\n", + "\n", + "# Blue plot\n", + "circle(1.75, 2.80)\n", + "text(1.75, 2.60, \"Line\\n(line plot)\")\n", + "\n", + "# Red plot\n", + "circle(1.20, 0.60)\n", + "text(1.20, 0.40, \"Line\\n(line plot)\")\n", + "\n", + "# Scatter plot\n", + "circle(3.20, 1.75)\n", + "text(3.20, 1.55, \"Markers\\n(scatter plot)\")\n", + "\n", + "# Grid\n", + "circle(3.00, 3.00)\n", + "text(3.00, 2.80, \"Grid\")\n", + "\n", + "# Legend\n", + "circle(3.70, 3.80)\n", + "text(3.70, 3.60, \"Legend\")\n", + "\n", + "# Axes\n", + "circle(0.5, 0.5)\n", + "text(0.5, 0.3, \"Axes\")\n", + "\n", + "# Figure\n", + "circle(-0.3, 0.65)\n", + "text(-0.3, 0.45, \"Figure\")\n", + "\n", + "color = 'blue'\n", + "ax.annotate('Spines', xy=(4.0, 0.35), xycoords='data',\n", + " xytext=(3.3, 0.5), textcoords='data',\n", + " weight='bold', color=color,\n", + " arrowprops=dict(arrowstyle='->',\n", + " connectionstyle=\"arc3\",\n", + " color=color))\n", + "\n", + "ax.annotate('', xy=(3.15, 0.0), xycoords='data',\n", + " xytext=(3.45, 0.45), textcoords='data',\n", + " weight='bold', color=color,\n", + " arrowprops=dict(arrowstyle='->',\n", + " connectionstyle=\"arc3\",\n", + " color=color))\n", + "\n", + "ax.text(4.0, -0.4, \"Made with http://matplotlib.org\",\n", + " fontsize=10, ha=\"right\", color='.5')\n", + "\n", + "plt.show()" + ] + }, + { + "attachments": { + "matplotlib_hierarchy.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## matplotlib Object Hierarchy: more\n", + "source: \n", + "- https://realpython.com/python-matplotlib-guide/\n", + "- https://www.janmeppe.com/blog/Plotting-with-Matplotlib/\n", + "\n", + "One important big-picture matplotlib concept is its _object hierarchy_: there is a tree-like structure of matplotlib objects underlying each plot. Basically, the hierarchy is as follows: Figure, Axes, Axis and all other stuff that goes on a plot. \n", + "\n", + "A Figure object is the outermost container for a matplotlib graphic, which can contain multiple Axes objects. One source of confusion is the name: an Axes actually translates into what we think of as an individual plot or graph (rather than the plural of “axis,” as we might expect).\n", + "\n", + "- A Figure object can have multiple Axes objects. This thing right here is what makes the terminology so confusing, think of the Axes object simply as an individual plot or graph.\n", + "- An Axes object has two Axis objects (x-axis and y-axis)\n", + "- Below the Axes in the hierarchy are smaller objects such as tick marks, individual lines, legends, and text boxes. \n", + "Almost every “element” of a chart is its own manipulable Python object, all the way down to the ticks and labels.\n", + "\n", + "![matplotlib_hierarchy.png](attachment:matplotlib_hierarchy.png)\n", + "Chart: object hierarchy (source: https://www.janmeppe.com/blog/Plotting-with-Matplotlib/)\n", + "\n", + "You can only manipulate one Axes object at a time. With pyplot, simple functions are used to add plot elements (lines, images, text, etc.) to the current Axes in the current figure.” \n", + "\n", + "Functions like gca() refer to getCurrentAxes() which basically grabs the current Axes so you can work on it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started\n", + "\n", + "imports are necessary to use matplotlib. Use some standard shorthands for Matplotlib imports\n", + "\n", + "```python\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# use some standard shorthands for Matplotlib imports\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A step by step example\n", + "\n", + "source: https://matplotlib.org/stable/tutorials/introductory/quick_start.html#sphx-glr-tutorials-introductory-quick-start-py\n", + "\n", + "matplotlib graphs your data on a `figure` each of which can contain one or more `Axes`, an\n", + "area where points can be specified in terms of coordinates \n", + "\n", + "The simplest way of creating a Figure with an Axes is using `pyplot.subplots`.\n", + "\n", + "Note: With subplot you can arrange plots in a regular grid. You need to specify the number of rows and columns and the number of the plot. Note that the `gridspec` command is a more powerful alternative.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parts of a Figure\n", + "\n", + "Here are the components of a matplotlib figure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `figure`\n", + "\n", + "The **whole** figure. The figure keeps\n", + "track of all the child `Axes`, a group of\n", + "'special' Artists (titles, figure legends, colorbars, etc), and\n", + "even nested subfigures.\n", + "\n", + "The easiest way to create a new Figure is with pyplot::\n", + "\n", + "```python\n", + " fig = plt.figure() # an empty figure with no Axes\n", + " fig, ax = plt.subplots() # a figure with a single Axes\n", + " fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes\n", + "```\n", + "\n", + "It is often convenient to create the Axes together with the Figure, but you\n", + "can also manually add Axes later on. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure() # an empty figure with no Axes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "print(type(fig))\n", + "print(type(ax))\n", + "print(dir(ax))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 2 axes columnwise\n", + "fig1, axs = plt.subplots(2,1)\n", + "\n", + "print(type(axs))\n", + "print(type(axs[1]))\n", + "\n", + "\n", + "fig2, (ax1, ax2) = plt.subplots(2,1)\n", + "\n", + "print(type(ax1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#2 axes rowwise\n", + "fig, (ax1, ax2) = plt.subplots(1,2)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `Axes`\n", + "\n", + "An Axes is an Artist attached to a Figure that contains a region for\n", + "plotting data, and usually includes two (or three in the case of 3D)\n", + "`Axis` objects (be aware of the difference\n", + "between **Axes** and **Axis**) that provide ticks and tick labels to\n", + "provide scales for the data in the Axes. Each `Axes` also\n", + "has a title\n", + "(set via `set_title`), an x-label (set via\n", + "`set_xlabel`), and a y-label set via\n", + "`set_ylabel`).\n", + "\n", + "The `Axes` class and its member functions are the primary\n", + "entry point to working with the OOP interface, and have most of the\n", + "plotting methods defined on them " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "time = [2, 4, 6, 8, 10]\n", + "distance = [1, 4, 9, 19, 39]\n", + "\n", + "print(type(time))\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(time, distance)\n", + "ax.set(xlabel='time',\n", + " ylabel='distance (m)',\n", + " title='Distance Graph')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `Axis`\n", + "\n", + "These objects set the scale and limits and generate ticks (the marks\n", + "on the Axis) and ticklabels (strings labeling the ticks). The location\n", + "of the ticks is determined by a `Locator` object and the\n", + "ticklabel strings are formatted by a `Formatter`. The\n", + "combination of the correct `Locator` and `Formatter` gives very fine\n", + "control over the tick locations and labels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `Artist`\n", + "\n", + "Basically, everything visible on the Figure is an Artist. When the Figure is rendered, all of the\n", + "Artists are drawn to the **canvas**. Most Artists are tied to an Axes; such\n", + "an Artist cannot be shared by multiple Axes, or moved from one to another." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input to plotting functions\n", + "\n", + "Plotting functions expect `numpy.array` or `numpy.ma.masked_array` as\n", + "input, or objects that can be passed to `numpy.asarray`(e.g. a Python list, pandas DataFrame). \n", + "Classes that are similar to arrays ('array-like') such as `pandas`\n", + "data objects and `numpy.matrix` may not work as intended. Common convention\n", + "is to convert these to `numpy.array` objects prior to plotting.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "use `Axes.plot` to draw some data on the Axes:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots() # Create a figure containing a single axes.\n", + "ax.plot([2, 4, 6, 8, 10], [1, 4, 9, 19, 39]); # Plot some data on the axes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(19680801) # seed the random number generator.\n", + "data = {'a': np.arange(50),\n", + " 'c': np.random.randint(0, 50, 50),\n", + " 'd': np.random.randn(50)}\n", + "data['b'] = data['a'] + 10 * np.random.randn(50)\n", + "data['d'] = np.abs(data['d']) * 100\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')\n", + "ax.scatter('a', 'b', c='c', s='d', data=data)\n", + "ax.set_xlabel('entry a')\n", + "ax.set_ylabel('entry b');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Styling Artists\n", + "\n", + "Most plotting methods have styling options for the Artists, accessible either\n", + "when a plotting method is called, or from a \"setter\" on the Artist. \n", + "\n", + "In the\n", + "plot below the *color*, *linewidth*, and *linestyle* of the\n", + "Artists created by `plot` are set, and we set the linestyle of the second line\n", + "after the fact with `set_linestyle`.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data1, data2 = np.random.randn(2, 100) # make 2 random data sets\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "x = np.arange(len(data1))\n", + "ax.plot(x, np.cumsum(data1), color='blue', linewidth=3, linestyle='--')\n", + "l, = ax.plot(x, np.cumsum(data2), color='orange', linewidth=2)\n", + "l.set_linestyle('dashdot');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Colors\n", + "\n", + "Check: https://matplotlib.org/stable/tutorials/colors/colors.html\n", + "\n", + "Matplotlib has a very flexible array of colors that are accepted for most\n", + "Artists.\n", + "\n", + "A single character shorthand notation for some basic colors (similar to Matlab)\n", + "\n", + "- 'b' as blue\n", + "- 'g' as green\n", + "- 'r' as red\n", + "- 'c' as cyan\n", + "- 'm' as magenta\n", + "- 'y' as yellow\n", + "- 'k' as black\n", + "- 'w' as white\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "ax.scatter(data1, data2, s=50, facecolor='c', edgecolor='k');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linewidths, linestyles, and markersizes\n", + "\n", + "Line widths are typically in typographic points (1 pt = 1/72 inch) and\n", + "available for Artists that have stroked lines. \n", + "\n", + "- 'solid' or '-'\n", + "- 'dotted' or ':'\n", + "- 'dashed' or '--'\n", + "- 'dashdot'or '-.'\n", + "\n", + "All possible markers: check https://matplotlib.org/stable/api/markers_api.html\n", + "\n", + "Marker size depends on the method being used. `~.Axes.plot` specifies\n", + "markersize in points, and is generally the \"diameter\" or width of the\n", + "marker. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data1, data2, data3, data4 = np.random.randn(4, 100) # make 4 random data sets\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "ax.plot(data1, 'o', label='data1')\n", + "ax.plot(data2, 'd', label='data2')\n", + "ax.plot(data3, 'v', label='data3')\n", + "ax.plot(data4, 's', label='data4')\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Labelling plots\n", + "\n", + "### Axes labels and text\n", + "\n", + "`set_xlabel`, `set_ylabel`, and `set_title` are used to\n", + "add text in the indicated locations. Text can also be directly added to plots using\n", + "`text`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mu, sigma = 115, 15\n", + "x = mu + sigma * np.random.randn(10000)\n", + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "# the histogram of the data\n", + "n, bins, patches = ax.hist(x, 50, density=True, facecolor='C0', alpha=0.75)\n", + "\n", + "ax.set_xlabel('Length [cm]')\n", + "ax.set_ylabel('Probability')\n", + "ax.set_title('Aardvark lengths\\n (not really)')\n", + "ax.text(75, .025, r'$\\mu=115,\\ \\sigma=15$')\n", + "ax.axis([55, 175, 0, 0.03])\n", + "ax.grid(True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using mathematical expressions in text\n", + "\n", + "Matplotlib accepts TeX equation expressions in any text expression.\n", + "For example to write the expression $\\sigma_i=15$ in the title,\n", + "you can write a TeX expression surrounded by dollar signs:\n", + "\n", + " ax.set_title(r'$\\sigma_i=15$')\n", + "\n", + "where the ``r`` preceding the title string signifies that the string is a\n", + "*raw* string and not to treat backslashes as python escapes.\n", + "Matplotlib has a built-in TeX expression parser and\n", + "layout engine, and ships its own math fonts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Annotations\n", + "\n", + "We can also annotate points on a plot, often by connecting an arrow pointing\n", + "to *xy*, to a piece of text at *xytext*:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "\n", + "t = np.arange(0.0, 5.0, 0.01)\n", + "s = np.cos(2 * np.pi * t)\n", + "line, = ax.plot(t, s, lw=2)\n", + "\n", + "ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),\n", + " arrowprops=dict(facecolor='black', shrink=0.05))\n", + "\n", + "ax.set_ylim(-2, 2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this basic example, both *xy* and *xytext* are in data coordinates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Legends\n", + "\n", + "Often we want to identify lines or markers with a `legend`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "ax.plot(np.arange(len(data1)), data1, label='data1')\n", + "ax.plot(np.arange(len(data2)), data2, label='data2')\n", + "ax.plot(np.arange(len(data3)), data3, 'd', label='data3')\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Legends in Matplotlib are quite flexible in layout, placement, and what\n", + "Artists they can represent. \n", + "\n", + "Check: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Axis scales and ticks\n", + "\n", + "Each Axes has two (or three) `Axis` objects representing the x- and\n", + "y-axis. These control the *scale* of the Axis, the tick *locators* and the\n", + "tick *formatters*. Additional Axes can be attached to display further Axis\n", + "objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scales\n", + "\n", + "In addition to the linear scale, Matplotlib supplies non-linear scales,\n", + "such as a log-scale. Since log-scales are used so much there are also\n", + "direct methods like `loglog`, `semilogx`, and\n", + "`semilogy`. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(5, 2.7))\n", + "xdata = np.arange(len(data1)) # make an ordinal for this\n", + "data = 10**data1\n", + "axs[0].plot(xdata, data)\n", + "\n", + "axs[1].set_yscale('log')\n", + "axs[1].plot(xdata, data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tick locators and formatters\n", + "\n", + "Each Axis has a tick *locator* and *formatter* that choose where along the\n", + "Axis objects to put tick marks. A simple interface to this is\n", + "`set_xticks`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2)\n", + "axs[0].plot(xdata, data1)\n", + "axs[0].set_title('Automatic ticks')\n", + "\n", + "axs[1].plot(xdata, data1)\n", + "axs[1].set_xticks(np.arange(0, 100, 30))\n", + "axs[1].set_xticklabels(['zero', '30', 'sixty', '90'])\n", + "axs[1].set_yticks([-1.5, 0, 1.5]) # note that we don't need to specify labels\n", + "axs[1].set_title('Manual ticks');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting dates and strings\n", + "\n", + "Matplotlib can handle plotting arrays of dates and arrays of strings, as\n", + "well as floating point numbers. These get special locators and formatters\n", + "as appropriate. For dates:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(5, 2.7))\n", + "dates = np.arange(np.datetime64('2021-11-15'), np.datetime64('2021-12-25'),\n", + " np.timedelta64(1, 'h'))\n", + "data = np.cumsum(np.random.randn(len(dates)))\n", + "ax.plot(dates, data)\n", + "cdf = mpl.dates.ConciseDateFormatter(ax.xaxis.get_major_locator())\n", + "ax.xaxis.set_major_formatter(cdf);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional Axis objects\n", + "\n", + "Plotting data of different magnitude in one chart may require\n", + "an additional y-axis. Such an Axis can be created by using\n", + "`twinx` to add a new Axes with an invisible x-axis and a y-axis\n", + "positioned at the right (analogously for `twiny`). \n", + "\n", + "Similarly, you can add a `secondary_xaxis` or\n", + "`secondary_yaxis` having a different scale than the main Axis to\n", + "represent the data in different scales or units. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Angle [°]')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(7, 2.7))\n", + "l1, = ax1.plot(t, s)\n", + "ax2 = ax1.twinx()\n", + "l2, = ax2.plot(t, range(len(t)), 'C1')\n", + "ax2.legend([l1, l2], ['Sine (left)', 'Straight (right)'])\n", + "\n", + "ax3.plot(t, s)\n", + "ax3.set_xlabel('Angle [rad]')\n", + "ax4 = ax3.secondary_xaxis('top', functions=(np.rad2deg, np.deg2rad))\n", + "ax4.set_xlabel('Angle [°]')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/set.ipynb b/notebooks/set.ipynb new file mode 100644 index 0000000..3b85aab --- /dev/null +++ b/notebooks/set.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*This notebook contains an excerpt from the [Whirlwind Tour of Python](http://www.oreilly.com/programming/free/a-whirlwind-tour-of-python.csp) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/WhirlwindTourOfPython).*\n", + "\n", + "*The text and code are released under the [CC0](https://github.com/jakevdp/WhirlwindTourOfPython/blob/master/LICENSE) license; see also the companion project, the [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook).*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)\n", + "\n", + "\n", + "## data containers\n", + "\n", + "Organize the data structure into four different families: \n", + "- Ordered data structure: list and tuple\n", + "- Unordered data structure: set and dictionary \n", + "- Mutable: list, set and dictionary \n", + "- Immutable: tuple \n", + "\n", + "\n", + "| Type | Example | Description |\n", + "|------|---------|---------|\n", + "| list | `[1, 2, 3]` | Ordered collection|\n", + "| tuple | `(1, 2, 3)` | Immutable ordered collection|\n", + "| dict | `{'a':1, 'b':2, 'c':3}` | Unordered (key:value) pair mapping|\n", + "| set | `{1, 2, 3}` | Unordered collection of unique values |\n", + "\n", + "\n", + "## operations on any sequence\n", + "\n", + "| Operation | Operator | Description |\n", + "|------|---------|---------|\n", + "| indexing | `[]` | Access an element of a sequence |\n", + "|concatenation | `+` | Combine sequences together|\n", + "| repetition | `*` | Concatenate a repeated number of times|\n", + "| membership | `in` | Ask whether an item is in a sequence |\n", + "| length | `len` | Ask the number of items in the sequence|\n", + "| slicing | `[:]` | Extract a part of a sequence |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sets\n", + "\n", + "The set contains unordered collections of unique items.\n", + "\n", + "- use the curly brackets `{}`\n", + "- **Unordered**\n", + "- **Set elements are unique**. Duplicate elements are not allowed.\n", + "- **Mutable**: a set itself may be modified, but the elements contained in the set must be of an immutable type.\n", + "- **No Indexing**: Set items cannot be referred to by index or key.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "primes = {2, 3, 5, 7}\n", + "odds = {1, 3, 5, 7, 9}\n", + "\n", + "myset = {'a', 'a', 'a', 'a', 'b', 'b', 'c'}\n", + "print(myset)\n", + "myset = {'a', 'all', 1, 2, (1, 2, 3)}\n", + "print(myset)\n", + "myset = {'a', 'all', 1, 2, [1, 2, 3]}\n", + "print(myset)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sets relate very closely to the sets we know from mathematics, the familiar operations like the union, intersection, difference, symmetric difference exist.\n", + "\n", + "\n", + "Python's sets have all of these operations built-in, via methods or operators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "primes = {2, 3, 5, 7}\n", + "odds = {1, 3, 5, 7, 9}\n", + "# union: items appearing in either\n", + "res_1o = primes | odds # with an operator\n", + "res_1m = primes.union(odds) # equivalently with a method\n", + "print(res_1o)\n", + "print(res_1m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# intersection: items appearing in both\n", + "res_2o = primes & odds # with an operator\n", + "res_2m = primes.intersection(odds) # equivalently with a method\n", + "print(res_2o)\n", + "print(res_2m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# difference: items in primes but not in odds\n", + "res_3o = primes - odds # with an operator\n", + "res_3m = primes.difference(odds) # equivalently with a method\n", + "print(res_3o)\n", + "print(res_3m)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# symmetric difference: items appearing in only one set\n", + "res_4o = primes ^ odds # with an operator\n", + "res_4m = primes.symmetric_difference(odds) # equivalently with a method\n", + "print(res_4o)\n", + "print(res_4m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building an set\n", + "\n", + "- an empty set with `set()`\n", + "- convert a list, tuple or string with `set()` will return a set with the elements" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "empty_set = set()\n", + "print(empty_set)\n", + "\n", + "str_1 = ' Hello World, this is a string'\n", + "str_set = set(str_1)\n", + "print(str_1)\n", + "print(str_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More set methods and operations are available.\n", + "\n", + "- add: add an item to the set\n", + "- discard: remove an item\n", + "- pop: removes a random item from the set and returns its value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(primes)\n", + "primes.add(13)\n", + "print(primes)\n", + "\n", + "guess_what = primes.pop()\n", + "print(guess_what)\n", + "\n", + "primes.discard(1)\n", + "print(primes)\n", + "\n", + "primes.remove(1)\n", + "print(primes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/tuple.ipynb b/notebooks/tuple.ipynb new file mode 100644 index 0000000..97c1f45 --- /dev/null +++ b/notebooks/tuple.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)\n", + "\n", + "\n", + "## data containers\n", + "\n", + "Organize the data structure into four different families: \n", + "- Ordered data structure: list and tuple\n", + "- Unordered data structure: set and dictionary \n", + "- Mutable: list, set and dictionary \n", + "- Immutable: tuple \n", + "\n", + "\n", + "| Type | Example | Description |\n", + "|------|---------|---------|\n", + "| list | `[1, 2, 3]` | Ordered collection|\n", + "| tuple | `(1, 2, 3)` | Immutable ordered collection|\n", + "| dict | `{'a':1, 'b':2, 'c':3}` | Unordered (key:value) pair mapping|\n", + "| set | `{1, 2, 3}` | Unordered collection of unique values |\n", + "\n", + "\n", + "## operations on any sequence\n", + "\n", + "| Operation | Operator | Description |\n", + "|------|---------|---------|\n", + "| indexing | `[]` | Access an element of a sequence |\n", + "|concatenation | `+` | Combine sequences together|\n", + "| repetition | `*` | Concatenate a repeated number of times|\n", + "| membership | `in` | Ask whether an item is in a sequence |\n", + "| length | `len` | Ask the number of items in the sequence|\n", + "| slicing | `[:]` | Extract a part of a sequence |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tuples\n", + "Tuples are in many ways similar to lists, but they are defined with parentheses rather than square brackets:\n", + "- ()\n", + "- Tuples are defined by specifying items separated by commas\n", + "- immutable objects, they are usually used when the list of values doesn't change\n", + "- same slicing rules apply as for lists\n", + "\n", + "Key features:\n", + " - **heterogeneous**: elements of a tuple don't have to be the same type. Each element of the tuple can be of any type.\n", + " - **indexing**: elements can be referenced by an index. \n", + " - **nesting**: tuples can be nested to arbitrary depth\n", + " - **immutable**: tuples are unchangeable\n", + " - **duplicates allowed**: tuples are indexed, items can have the same value\n", + "\n", + "Tuples?\n", + "- if data do not change, implementing it as tuple will guarantee that it remains write-protected.\n", + "- tuples are immutable, iterating through tuple is faster than with list. So there is a slight performance boost.\n", + "- tuples are frequently used to return multiple variables from functions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# tuple of strings\n", + "my_tuple1 = ('hi', 'hello', 'world')\n", + "print(my_tuple1)\n", + "\n", + "# tuple mixed data\n", + "my_tuple2 = (1, 'Hello', 3.14)\n", + "print(my_tuple2)\n", + "\n", + "# tuple of string and list\n", + "my_tuple3 = ('moving', [1, 2, 3, 4], ('v', 't', 'y'))\n", + "print(my_tuple3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Watch out: mutable elements from a tuple can be changed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#mutable elements from a tuple can be changed\n", + "my_tuple3[1][0] = 100\n", + "print('my_tuple3', my_tuple3)\n", + "\n", + "# tuple with 1 element\n", + "my_tuple4 = (1,)\n", + "print(my_tuple4)\n", + "\n", + "# empty tuple\n", + "my_tuple5 = ()\n", + "print(my_tuple5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "select items from a tuple with []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#select elements out of the tuples\n", + "print(my_tuple1[1])\n", + "print(my_tuple1[1][1])\n", + "print(my_tuple2[1])\n", + "print(my_tuple3[2])\n", + "print(my_tuple3[2][2])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### packing / unpacking\n", + "\n", + "Tuples can be used to assign multiple variables in a single operation (packing)\n", + "\n", + "Syntax: < tuple of variables > = < tuple of values >" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(a, b, c) = (1, 2, 3)\n", + "print(a, b, c)\n", + "print(type(c))\n", + "\n", + "(a, b, *c) = (1, 2, 3, 4, 5)\n", + "print(a, b, c)\n", + "print(type(c))\n", + "\n", + "(a, *b, c) = (1, 2, 3, 4, 5)\n", + "print(a, b, c)\n", + "print(type(c))\n", + "\n", + "(a, b, c) = (1, 2, 3, 4, 5)\n", + "print(a, b, c)\n", + "print(type(c))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t1 = (1, 2, 'help', [1, 2, 3])\n", + "print(t1)\n", + "print(dir(t1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Methods\n", + "\n", + "- index() searches an element in a tuple and returns its index.\n", + "- count() returns the number of items" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "t1 = (1, 2, 'help', [1, 2, 3])\n", + "print(t1)\n", + "print('count of 1: ', t1.count(1))\n", + "print('count of 3: ', t1.count(3))\n", + "\n", + "print('index of 1: ', t1.index(1))\n", + "print('index of 3: ', t1.index(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test membership with `in`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "3 in t1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete Tuple Elements?\n", + "\n", + "- Removing individual tuple elements is not possible. \n", + "- To explicitly remove an entire tuple, use the `del` statement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t1 = (1, 2, 'help', [1, 2, 3])\n", + "%who\n", + "del(t1)\n", + "%who" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### advanced\n", + "\n", + "named tuple \n", + "\n", + "* Convenient access the tuple's fields by name, rather than by index. \n", + "* Python's standard library: define named tuples through typing.NameTuple." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import NamedTuple\n", + "\n", + "class Car(NamedTuple):\n", + " name: str\n", + " year: int\n", + " weight: float\n", + " \n", + "\n", + "car1 = Car('Volvo', 2015, 48.9)\n", + "print('car = ', car1)\n", + "print('car name = ', car1.name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lcars = [Car('Volvo', 2015, 48.9), Car('VW', 2017, 4.9), Car('Opel', 2011, 58.9)]\n", + "print('car 1', lcars[0])\n", + "print('car 2', lcars[1].name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/variables.ipynb b/notebooks/variables.ipynb new file mode 100644 index 0000000..caf7606 --- /dev/null +++ b/notebooks/variables.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variables\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to ensure that all cells in this notebook can be evaluated without errors, we will use try-except to catch exceptions. To show the actual errors, we need to print the backtrace, hence the following import.\n", + "from traceback import print_exc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key concepts in programming\n", + "\n", + "- Variables (integers, strings, dates, etc.)\n", + "- Flow control (if then, loop, etc.)\n", + "- Functions (list of steps the code will follow)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Variables \n", + "* Variables are placeholders for locations in memory. \n", + " * Variables are names for values\n", + " * Created by use – no declaration necessary\n", + " * Python is dynamically typed, The Python interpreter infers the type based on the assigned value.\n", + "* Variables always have a type \n", + " * Variables only have data types after you use them\n", + " * Use the type function to determine variable type\n", + "* Variables have a name\n", + " * Python is case sensitive. \n", + " * myVar is different from Myvar\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Naming \n", + "Variable names in Python can contain alphanumeric characters (a-z, A-Z, 0-9) and underscores (_)\n", + "\n", + "Python is case-sensitive.\n", + "\n", + " * Avoid using names that differ only by case\n", + " * Choose meaningful names\n", + " * No leading numbers, no spaces\n", + " * Variables are generally lower case, functions are CapCase\n", + " * Don’t use Python keywords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "password1 = 'secret'\n", + "n00b = 9.07\n", + "un_der_scores = 9\n", + "case_sensitive = 'c' \n", + "CASE_SENSITIVE = 'cc'\n", + "Case_Sensitive = 'ccc'\n", + "%whos\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use keyword library to get the list of keywords\n", + "import keyword\n", + "keyword.kwlist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "76trombones = 'big parade'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "more@ = 1000000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for = 'Advanced Theoretical Physics'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assignment\n", + "\n", + "Variables must be created (assigned a value) before they can be used\n", + "\n", + "A variable is created through assignment:\n", + "`x = 4`\n", + "\n", + "What happens?\n", + "* Python creates the object 4 \n", + "* Everything in Python is an object, this object is stored somewhere in memory. \n", + "* Python binds a name to the object. x is a reference to the object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chamo = 4\n", + "id(chamo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(chamo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = 4\n", + "print(type(a))\n", + "print(id(a))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "st1 = 'string'\n", + "dir(st1)\n", + "st1.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 1 # x is an integer\n", + "print(x, id(x))\n", + "x = 'hello' # now x is a string\n", + "print(x, id(x))\n", + "x = [1, 2, 3] # now x is a list\n", + "print(x, id(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python also allows chained assignment, which makes it possible to assign the same value to several variables simultaneously:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = b = c = 300\n", + "print(a, b, c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!notepad check_variable_object.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%run check_variable_object.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Everything is an object:\n", + "* Some associated functionality (methods) and metadata (attributes). \n", + "* These methods and attributes are accessed via the dot(.) syntax.\n", + "* Use `type` to get information on the class\n", + "* Use `dir` to get an overview on the methods\n", + " * Many of the names in the list start and end with two underscores (dunder), like `__add__`. These are all associated with methods and pieces of data used internally by the Python interpreter. \n", + " * The remaining entries in the list are all user-level methods. \n", + "* Use `id`\n", + " * an integer (or long integer) _which is guaranteed to be unique and constant for this object during its lifetime._ (Python Standard Library - Built-in Functions) A unique number. Nothing more, and nothing less.\n", + " * in cpython implementation, it actually is the memory address of the corresponding C object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 1 # x is an integer\n", + "y = 15\n", + "print(x)\n", + "print(x, id(x))\n", + "print(type(x))\n", + "print(dir(x))\n", + "print(x+y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = '1' # now x is a string\n", + "y ='15'\n", + "z = 'this is a string'\n", + "print(x)\n", + "print(x, id(x))\n", + "print(type(x))\n", + "print(x+y)\n", + "\n", + "print(dir(z))\n", + "print(z)\n", + "print(z.upper())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = [1, 2, 3] # now x is a list\n", + "print(x)\n", + "print(x, id(x))\n", + "print(type(x))\n", + "print(dir(x))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = [1, 2, 3]\n", + "print(a)\n", + "b = a\n", + "print(b)\n", + "a.append(6)\n", + "print(a)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mutable vs Immutable\n", + "\n", + "* Immutability: Some of Python’s data types are immutable, which means they can’t be changed after they’re created. This includes `integers`, `floats`, `strings`, and `tuples`. If you try to change the value of an immutable variable, Python actually creates a new object with the new value.\n", + "* Mutable Types: Other data types in Python are mutable, which means they can be changed after they’re created. This includes `lists`, `dictionaries`, and `sets`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Immutable\n", + "num = 5\n", + "print(num)\n", + "print(id(num)) # Let's say this prints: 140703167764848\n", + "num += 1\n", + "print(num)\n", + "print(id(num)) # This will print a different id: 140703167764880\n", + "\n", + "# Mutable\n", + "lst = [1, 2, 3]\n", + "print(lst)\n", + "print(id(lst)) # Let's say this prints: 1856906640576\n", + "lst.append(4)\n", + "print(lst)\n", + "print(id(lst)) # This will print the same id: 1856906640576\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global and Local Variables\n", + "\n", + "* Variables defined inside a function are local to that function and cannot be accessed outside of it. \n", + "* Variables defined outside all functions are global and can be accessed anywhere in the program.\n", + "\n", + "* Local Variables:\n", + " * A local variable is declared inside a function.\n", + " * It is only accessible within the function where it is declared.\n", + " * Different functions can use the same name for different local variables.\n", + " * They are created when their function is called and destroyed when the function ends.\n", + "\n", + "* Global Variables:\n", + " * A global variable is declared outside all functions.\n", + " * It is accessible throughout the entire program, including within functions (unless a local variable with the same name exists).\n", + " * There can only be one global variable with a given name in a Python program.\n", + " * They are created when their line of code is executed and exist until the program ends." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# This is a global variable\n", + "x = 10\n", + "\n", + "def my_function():\n", + " # This is a local variable\n", + " y = 5\n", + " print(\"Inside the function, x = \", x, \"and y = \", y)\n", + "\n", + "my_function()\n", + "\n", + "# This will print: Inside the function, x = 10 and y = 5\n", + "print(\"Outside the function, x = \", x)\n", + "\n", + "# This will raise a NameError because y is not defined outside the function\n", + "print(\"Outside the function, y = \", y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/MOCK_DATA.csv b/scripts/MOCK_DATA.csv new file mode 100644 index 0000000..c318778 --- /dev/null +++ b/scripts/MOCK_DATA.csv @@ -0,0 +1,1001 @@ +id,first_name,last_name,email,gender,ip_address,date,parts,unit cost +1,Lurette,Rounsefull,lrounsefull0@cbsnews.com,Female,208.52.245.134,4/11/2019,1,1425.64 +2,Peder,Heineking,pheineking1@mac.com,Male,216.60.184.211,5/15/2019,79,2666.57 +3,Haydon,Birdsall,hbirdsall2@spotify.com,Male,161.74.94.92,10/18/2019,27,2377.64 +4,Barbe,Mouse,bmouse3@elegantthemes.com,Female,97.202.107.43,5/16/2019,20,3375.08 +5,Sherwin,Wealthall,swealthall4@whitehouse.gov,Male,155.14.42.127,3/5/2019,77,4147.29 +6,Ase,Pringle,apringle5@sfgate.com,Male,147.34.186.244,10/19/2019,33,4551.61 +7,Sibylla,Weiss,sweiss6@icq.com,Female,196.247.130.201,3/7/2019,21,2154.9 +8,Richy,Dougal,rdougal7@yelp.com,Male,91.176.172.223,12/27/2018,97,3403.75 +9,Kelsey,Ludlamme,kludlamme8@cyberchimps.com,Male,121.112.184.219,5/2/2019,81,4930.75 +10,Fae,Driffe,fdriffe9@friendfeed.com,Female,219.51.51.253,4/7/2019,57,3100.78 +11,Dannye,Sleit,dsleita@independent.co.uk,Female,160.208.93.210,10/4/2019,88,4401.37 +12,Kessia,Gilkes,kgilkesb@w3.org,Female,30.178.57.187,8/26/2019,82,2369.86 +13,Rickie,Persich,rpersichc@mapquest.com,Male,50.235.20.153,9/13/2019,94,4557.41 +14,Margaretta,Shearn,mshearnd@buzzfeed.com,Female,172.102.242.250,3/14/2019,72,2948.22 +15,Malcolm,Winnard,mwinnarde@yale.edu,Male,185.241.158.149,5/15/2019,82,4723.48 +16,Lazarus,Summerlie,lsummerlief@comsenz.com,Male,111.172.107.99,11/8/2019,89,2845.05 +17,Bord,Cato,bcatog@edublogs.org,Male,177.53.70.102,4/2/2019,32,4933.24 +18,Weber,Guslon,wguslonh@go.com,Male,115.2.137.252,12/10/2018,94,4348.69 +19,Stearne,MacPadene,smacpadenei@pbs.org,Male,35.83.181.137,8/4/2019,82,4950.67 +20,Bekki,Remirez,bremirezj@arstechnica.com,Female,12.111.119.238,2/17/2019,62,1510.53 +21,Desiri,Gaskins,dgaskinsk@prlog.org,Female,43.30.140.105,9/8/2019,64,4641.51 +22,Mata,Beaten,mbeatenl@constantcontact.com,Male,13.90.195.102,4/6/2019,84,1942.72 +23,Mattias,Bullas,mbullasm@fastcompany.com,Male,37.242.45.155,7/21/2019,95,3321.2 +24,Sloan,Graveson,sgravesonn@mozilla.com,Male,124.94.155.193,9/28/2019,7,1786.92 +25,Isak,Juste,ijusteo@google.ru,Male,249.233.28.164,8/18/2019,34,1682.19 +26,Teri,Thomann,tthomannp@wp.com,Female,168.237.145.237,11/16/2019,18,4480.64 +27,Rhoda,Gotcher,rgotcherq@businessinsider.com,Female,23.141.180.40,4/1/2019,19,4514.47 +28,Helen-elizabeth,McEniry,hmceniryr@oakley.com,Female,43.143.23.204,12/14/2018,40,4146.98 +29,Aguie,Brecknall,abrecknalls@nifty.com,Male,69.49.176.116,1/19/2019,96,4720.87 +30,Wildon,Keenlyside,wkeenlysidet@blogtalkradio.com,Male,245.109.45.71,3/26/2019,22,1835.57 +31,Ynez,Lots,ylotsu@abc.net.au,Female,172.185.2.14,7/17/2019,68,4872.13 +32,Norman,Worgen,nworgenv@constantcontact.com,Male,143.221.2.185,3/17/2019,29,4768.51 +33,Ker,Bown,kbownw@bandcamp.com,Male,30.5.53.61,6/14/2019,22,3091.25 +34,Ivy,Nibloe,inibloex@noaa.gov,Female,121.242.246.184,7/31/2019,15,4048.07 +35,Coop,Waiting,cwaitingy@prlog.org,Male,215.155.212.35,1/31/2019,59,1388.34 +36,Philipa,Bigglestone,pbigglestonez@networkadvertising.org,Female,235.113.203.193,1/11/2019,49,2571.05 +37,Konstantine,Reavell,kreavell10@pcworld.com,Male,212.115.157.70,1/15/2019,32,1521.5 +38,Neddie,Peckham,npeckham11@shareasale.com,Male,165.199.0.54,11/10/2019,4,4159.67 +39,Veronique,Crocetti,vcrocetti12@bravesites.com,Female,155.94.241.75,11/27/2019,70,1682.83 +40,Stanford,Folk,sfolk13@walmart.com,Male,115.78.90.21,8/20/2019,21,1049.63 +41,Tamera,Bacchus,tbacchus14@techcrunch.com,Female,125.75.125.6,2/3/2019,44,3804.48 +42,Andromache,Hakonsson,ahakonsson15@ft.com,Female,216.82.36.186,8/3/2019,38,3643.39 +43,Elnora,Amos,eamos16@domainmarket.com,Female,233.33.105.19,11/12/2019,97,1234.57 +44,Andrus,Hegge,ahegge17@arstechnica.com,Male,48.49.214.246,9/21/2019,79,4728.49 +45,Grady,Rodell,grodell18@ox.ac.uk,Male,69.64.12.243,4/22/2019,64,3514.69 +46,Maryann,Shaddick,mshaddick19@boston.com,Female,145.192.46.176,8/7/2019,43,1944.22 +47,Gaelan,Kirtley,gkirtley1a@topsy.com,Male,145.162.10.133,9/14/2019,39,2598.76 +48,Denver,Kennicott,dkennicott1b@amazon.com,Male,179.116.133.53,12/18/2018,10,2243.1 +49,Bordy,Saw,bsaw1c@cmu.edu,Male,6.22.237.36,1/27/2019,80,2196.95 +50,Gwenette,Iston,giston1d@multiply.com,Female,211.116.16.113,3/29/2019,34,2230.26 +51,Hedvig,Simmers,hsimmers1e@uol.com.br,Female,155.181.252.255,8/23/2019,7,3711.71 +52,Jannel,Sarre,jsarre1f@tuttocitta.it,Female,127.187.194.109,6/4/2019,82,2641.84 +53,Rusty,Saffill,rsaffill1g@mozilla.com,Male,152.175.79.114,3/24/2019,63,4482.27 +54,Shelden,Brandin,sbrandin1h@jugem.jp,Male,218.156.254.18,9/29/2019,12,1487.3 +55,Rusty,Dumbare,rdumbare1i@purevolume.com,Male,49.252.90.82,3/2/2019,26,4880.92 +56,Agace,Davidovitz,adavidovitz1j@mtv.com,Female,187.52.194.143,4/21/2019,59,4803.16 +57,Ross,McGarva,rmcgarva1k@upenn.edu,Male,41.191.56.78,8/23/2019,97,4860.07 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+81,Renaldo,Calow,rcalow28@cloudflare.com,Male,108.38.150.172,12/5/2018,98,3790.92 +82,Chrissie,Wynch,cwynch29@de.vu,Female,113.58.215.184,5/25/2019,54,3925.41 +83,Boris,Ilyas,bilyas2a@webeden.co.uk,Male,98.65.148.122,1/26/2019,4,2722.03 +84,Zach,McReynolds,zmcreynolds2b@about.com,Male,169.251.18.46,3/25/2019,33,2238.47 +85,Pip,Puttan,pputtan2c@multiply.com,Male,6.160.216.170,10/23/2019,20,1502.59 +86,Lenore,Shafto,lshafto2d@globo.com,Female,144.169.11.70,1/17/2019,5,2162.94 +87,Maggie,Dryden,mdryden2e@cpanel.net,Female,203.155.129.86,3/12/2019,76,3017.41 +88,Madeleine,Alflatt,malflatt2f@spotify.com,Female,38.43.105.230,6/3/2019,70,4950.07 +89,Jojo,Niblock,jniblock2g@usgs.gov,Female,241.182.83.206,6/20/2019,98,3224.06 +90,Chan,Lebbern,clebbern2h@about.me,Male,129.233.179.181,5/31/2019,14,3407.91 +91,Juanita,O'Neil,joneil2i@sina.com.cn,Female,196.212.243.128,4/21/2019,4,1238.45 +92,Albina,Nestoruk,anestoruk2j@rakuten.co.jp,Female,173.60.28.232,11/25/2019,2,3850.73 +93,Gerrard,Lowrance,glowrance2k@huffingtonpost.com,Male,65.158.65.49,5/30/2019,56,2495.36 +94,Enrique,Borrel,eborrel2l@slashdot.org,Male,234.74.144.119,11/11/2019,89,1230.11 +95,Sheppard,Millward,smillward2m@plala.or.jp,Male,30.207.143.30,9/13/2019,14,4656.55 +96,Karna,Zisneros,kzisneros2n@comcast.net,Female,207.85.89.67,4/30/2019,40,1087.58 +97,Mohandas,Abbitt,mabbitt2o@cocolog-nifty.com,Male,157.202.50.94,2/2/2019,18,4141.99 +98,Alena,McVee,amcvee2p@so-net.ne.jp,Female,35.120.251.203,9/28/2019,15,4863.32 +99,Lanita,Sommerlin,lsommerlin2q@mozilla.com,Female,63.103.18.73,2/17/2019,69,4424.01 +100,Celine,Huddlestone,chuddlestone2r@ask.com,Female,207.219.182.50,6/24/2019,92,4185.16 +101,Jan,Vorley,jvorley2s@bbc.co.uk,Male,24.122.66.16,10/7/2019,88,4856.43 +102,Gordon,Burness,gburness2t@wired.com,Male,251.54.167.76,7/28/2019,77,1249.9 +103,Doris,Dey,ddey2u@youtube.com,Female,105.167.122.156,5/18/2019,95,2541.59 +104,Colby,Ashurst,cashurst2v@wix.com,Male,93.195.66.138,9/14/2019,49,3987.84 +105,Phyllida,Brookson,pbrookson2w@apple.com,Female,15.169.21.114,4/8/2019,10,4565.43 +106,Jeramie,Crasswell,jcrasswell2x@posterous.com,Male,205.238.4.171,7/19/2019,10,1042.23 +107,Lynsey,Plum,lplum2y@blogtalkradio.com,Female,184.41.245.159,11/15/2019,27,1913.04 +108,Dorella,Dulanty,ddulanty2z@discuz.net,Female,29.143.190.208,3/15/2019,71,1939.76 +109,Ruthi,Rishworth,rrishworth30@liveinternet.ru,Female,107.240.252.87,11/14/2019,25,4378.32 +110,Petrina,Featherstonehaugh,pfeatherstonehaugh31@umich.edu,Female,139.60.87.163,5/3/2019,86,2273.95 +111,Karla,Dechelle,kdechelle32@google.cn,Female,147.220.216.41,11/22/2019,79,2940.0 +112,Joshia,Fishlock,jfishlock33@booking.com,Male,158.103.241.179,7/8/2019,68,3644.03 +113,Junia,Maryman,jmaryman34@jigsy.com,Female,216.105.242.205,5/15/2019,97,2466.85 +114,Mabelle,Warmisham,mwarmisham35@mysql.com,Female,246.108.73.60,2/5/2019,43,1904.03 +115,Phaidra,Fleet,pfleet36@1und1.de,Female,83.157.253.208,2/13/2019,21,1765.81 +116,Flo,Mardlin,fmardlin37@unicef.org,Female,31.252.115.185,12/11/2018,14,3875.57 +117,Crin,Aspinall,caspinall38@pen.io,Female,152.151.189.190,9/21/2019,76,3615.12 +118,Lorrie,Carnaman,lcarnaman39@sina.com.cn,Male,182.12.111.81,2/1/2019,66,1537.87 +119,Barnabas,Denslow,bdenslow3a@dell.com,Male,109.193.202.177,10/17/2019,71,1352.18 +120,Bennett,Saffen,bsaffen3b@cloudflare.com,Male,180.151.92.220,3/8/2019,19,3352.34 +121,Jonell,Menauteau,jmenauteau3c@yellowbook.com,Female,49.115.246.26,9/25/2019,26,4823.26 +122,Alicia,Jakubovicz,ajakubovicz3d@nps.gov,Female,239.158.188.213,3/27/2019,32,1051.47 +123,Nikolai,Raselles,nraselles3e@histats.com,Male,94.104.99.187,5/13/2019,43,1043.08 +124,Nikolaos,Loder,nloder3f@google.it,Male,19.118.58.253,3/17/2019,73,1254.57 +125,Sheffield,Muncey,smuncey3g@elpais.com,Male,77.244.5.146,7/28/2019,67,1704.55 +126,Marni,Cowin,mcowin3h@google.com.br,Female,61.232.91.222,7/8/2019,50,1367.66 +127,Storm,Kennedy,skennedy3i@wikispaces.com,Female,191.12.223.90,7/31/2019,77,3871.16 +128,Trent,Gutierrez,tgutierrez3j@sourceforge.net,Male,113.242.15.190,8/10/2019,81,4352.61 +129,Dodi,Tiller,dtiller3k@dailymail.co.uk,Female,0.240.169.55,11/21/2019,35,4607.1 +130,Vivie,Sture,vsture3l@yelp.com,Female,46.11.195.152,10/27/2019,82,2166.72 +131,Nerissa,Giddins,ngiddins3m@whitehouse.gov,Female,25.100.68.103,3/11/2019,81,4326.19 +132,Emalee,Huckerby,ehuckerby3n@europa.eu,Female,249.239.20.180,2/14/2019,15,1607.1 +133,Cesare,Deave,cdeave3o@google.es,Male,215.226.177.3,12/16/2018,64,3602.38 +134,Britni,Neagle,bneagle3p@is.gd,Female,238.87.102.252,5/1/2019,95,3254.36 +135,Andrej,Gandrich,agandrich3q@addtoany.com,Male,122.104.65.90,8/14/2019,41,3220.23 +136,Cody,Worssam,cworssam3r@chronoengine.com,Male,221.63.128.0,11/11/2019,7,4570.98 +137,Mord,Bradford,mbradford3s@utexas.edu,Male,168.172.15.29,2/8/2019,52,2842.69 +138,Madeline,Golledge,mgolledge3t@japanpost.jp,Female,180.54.218.245,8/1/2019,31,2426.19 +139,Clarie,Tunn,ctunn3u@sun.com,Female,170.49.242.95,3/30/2019,6,2375.77 +140,Seana,Gatherell,sgatherell3v@myspace.com,Female,229.46.37.246,7/1/2019,83,3626.95 +141,Veronica,Fielder,vfielder3w@hugedomains.com,Female,211.136.81.248,10/5/2019,76,4578.69 +142,Kris,Searby,ksearby3x@constantcontact.com,Male,75.40.234.246,5/14/2019,79,3491.43 +143,Dorita,Henze,dhenze3y@mozilla.com,Female,19.91.34.111,3/5/2019,23,3869.59 +144,Ada,Sydall,asydall3z@netlog.com,Female,63.122.73.224,9/28/2019,5,3118.74 +145,Josias,Vanichkov,jvanichkov40@amazon.co.jp,Male,111.18.27.20,3/11/2019,82,1807.86 +146,Payton,Chesney,pchesney41@forbes.com,Male,253.128.22.165,6/1/2019,93,1101.65 +147,Arabele,Marcome,amarcome42@bbb.org,Female,25.78.95.237,8/25/2019,20,3268.49 +148,Dick,Egre,degre43@discovery.com,Male,136.113.116.45,3/3/2019,35,3221.24 +149,Charleen,Eckh,ceckh44@shareasale.com,Female,251.215.112.50,6/30/2019,47,3524.18 +150,Minne,Lawfull,mlawfull45@shinystat.com,Female,15.159.68.230,7/31/2019,56,3915.18 +151,Timi,Pelchat,tpelchat46@issuu.com,Female,218.157.149.25,11/17/2019,53,1224.92 +152,Gabrila,Goosnell,ggoosnell47@hugedomains.com,Female,79.133.249.98,12/28/2018,30,4687.86 +153,Casie,Brimm,cbrimm48@4shared.com,Female,252.229.161.4,2/15/2019,98,1455.37 +154,Leeland,Dewi,ldewi49@multiply.com,Male,81.68.121.138,3/25/2019,63,4518.04 +155,Pascal,Garred,pgarred4a@facebook.com,Male,223.70.91.166,5/3/2019,71,2996.67 +156,Lazaro,Manktelow,lmanktelow4b@wikia.com,Male,185.80.62.190,7/21/2019,82,1094.16 +157,Sandi,Montgomery,smontgomery4c@java.com,Female,190.222.21.13,11/17/2019,21,2832.31 +158,Valentine,Jimes,vjimes4d@about.com,Female,74.198.243.183,5/1/2019,53,1644.56 +159,Erika,Ferraresi,eferraresi4e@google.fr,Female,54.193.54.25,5/19/2019,21,4436.52 +160,Nerta,Zaczek,nzaczek4f@spotify.com,Female,108.17.93.214,2/22/2019,81,2156.82 +161,Lucita,Harte,lharte4g@nasa.gov,Female,193.242.177.185,12/21/2018,64,3544.36 +162,Jedidiah,Danielsohn,jdanielsohn4h@accuweather.com,Male,245.98.253.52,9/16/2019,1,2115.11 +163,Lorettalorna,Quarrie,lquarrie4i@fc2.com,Female,76.173.212.93,6/13/2019,4,1466.75 +164,Rich,Tryhorn,rtryhorn4j@moonfruit.com,Male,143.167.201.182,10/7/2019,1,1661.04 +165,Gardie,Karlqvist,gkarlqvist4k@so-net.ne.jp,Male,249.11.192.35,8/12/2019,6,3746.95 +166,Xylia,Penna,xpenna4l@dailymotion.com,Female,180.58.51.110,11/28/2019,61,3441.57 +167,Axel,Renackowna,arenackowna4m@tripod.com,Male,146.67.140.217,3/7/2019,78,3550.93 +168,Chen,Fulton,cfulton4n@columbia.edu,Male,146.169.34.152,4/2/2019,74,2383.26 +169,Alon,Cufley,acufley4o@photobucket.com,Male,125.231.215.140,4/16/2019,65,1732.78 +170,Koo,Gumm,kgumm4p@php.net,Female,243.153.77.38,3/21/2019,48,3721.4 +171,Korey,Weatherdon,kweatherdon4q@blogger.com,Male,240.168.248.218,3/12/2019,77,3795.59 +172,Lena,Stannus,lstannus4r@lulu.com,Female,109.85.91.216,12/5/2018,46,2634.33 +173,Elijah,Westney,ewestney4s@noaa.gov,Male,158.146.220.98,8/7/2019,27,4530.75 +174,Eachelle,Schiefersten,eschiefersten4t@symantec.com,Female,54.128.102.166,9/15/2019,87,2940.88 +175,Georgeanne,Gotthard.sf,ggotthardsf4u@geocities.com,Female,86.43.67.105,1/9/2019,37,2319.01 +176,Frazier,Feldbrin,ffeldbrin4v@joomla.org,Male,184.234.191.22,10/14/2019,23,4596.74 +177,Bancroft,Alvarez,balvarez4w@cisco.com,Male,226.3.76.213,7/28/2019,21,3413.13 +178,Ricky,Lyles,rlyles4x@abc.net.au,Male,156.11.73.17,11/28/2019,6,1870.07 +179,Lorettalorna,Bomfield,lbomfield4y@virginia.edu,Female,37.210.36.178,6/20/2019,66,1127.34 +180,Catrina,Ruse,cruse4z@exblog.jp,Female,81.216.141.90,12/5/2018,34,2485.16 +181,Loleta,Pawels,lpawels50@merriam-webster.com,Female,176.42.193.141,6/10/2019,84,4221.89 +182,Hedvige,Commings,hcommings51@domainmarket.com,Female,138.3.249.198,1/29/2019,8,3910.06 +183,Dmitri,McCrory,dmccrory52@mit.edu,Male,135.37.103.219,7/28/2019,64,2804.83 +184,Hendrika,Shawyer,hshawyer53@xinhuanet.com,Female,16.30.34.147,2/20/2019,65,1706.29 +185,Lorilee,Dunlop,ldunlop54@ed.gov,Female,111.241.90.104,5/10/2019,75,4965.63 +186,Jerad,Faunch,jfaunch55@blogtalkradio.com,Male,139.35.31.167,3/18/2019,93,4404.83 +187,Gualterio,Eary,geary56@illinois.edu,Male,59.170.22.192,10/2/2019,49,3569.07 +188,Barby,Jikylls,bjikylls57@deviantart.com,Female,22.44.213.16,4/7/2019,75,4908.44 +189,Jabez,Mathiot,jmathiot58@w3.org,Male,93.194.63.80,5/5/2019,64,2744.04 +190,Worthy,Edland,wedland59@barnesandnoble.com,Male,4.66.208.56,11/12/2019,51,4272.28 +191,Doralyn,Braunstein,dbraunstein5a@youtube.com,Female,68.179.11.250,3/1/2019,37,3713.51 +192,Mallory,Eyres,meyres5b@google.com.hk,Female,222.214.3.63,7/22/2019,57,3522.06 +193,Louie,Dunkley,ldunkley5c@cdc.gov,Male,154.18.175.128,7/10/2019,51,2901.79 +194,Elbert,Kenvin,ekenvin5d@ftc.gov,Male,206.116.235.21,2/23/2019,75,4198.55 +195,Sean,Hankin,shankin5e@virginia.edu,Female,32.170.127.155,7/7/2019,76,4284.24 +196,Tamiko,O'Fogarty,tofogarty5f@comsenz.com,Female,249.124.65.230,2/27/2019,97,1816.59 +197,Dana,Caress,dcaress5g@bandcamp.com,Female,63.163.104.0,6/11/2019,70,4546.28 +198,Starla,Bicksteth,sbicksteth5h@deviantart.com,Female,93.41.109.63,3/6/2019,75,1536.56 +199,Olly,Chalmers,ochalmers5i@nature.com,Male,206.17.239.226,6/1/2019,99,2617.06 +200,Ermengarde,Laible,elaible5j@alibaba.com,Female,232.255.191.98,4/14/2019,7,2122.32 +201,Melloney,Stalf,mstalf5k@constantcontact.com,Female,254.159.238.133,3/11/2019,17,1829.95 +202,Aggy,Sandry,asandry5l@chicagotribune.com,Female,88.162.78.17,4/23/2019,82,1228.42 +203,Tuesday,Brunning,tbrunning5m@microsoft.com,Female,24.185.56.166,12/2/2018,25,2420.45 +204,Graehme,Marconi,gmarconi5n@forbes.com,Male,134.166.130.121,1/15/2019,29,3650.99 +205,Susi,Casillas,scasillas5o@jimdo.com,Female,130.75.119.191,4/6/2019,26,3164.59 +206,Bunny,Bollands,bbollands5p@privacy.gov.au,Female,73.155.139.92,7/20/2019,94,1459.89 +207,Corrine,Bythway,cbythway5q@ucsd.edu,Female,152.20.150.152,12/30/2018,74,1997.35 +208,Dannye,Adshede,dadshede5r@netlog.com,Female,210.46.12.79,8/9/2019,28,2915.74 +209,Gerald,Waiton,gwaiton5s@1und1.de,Male,47.243.251.9,5/18/2019,66,3698.92 +210,Belia,Hatherley,bhatherley5t@dot.gov,Female,3.253.140.62,10/2/2019,54,1089.69 +211,Terrell,Witherup,twitherup5u@ibm.com,Male,125.43.97.27,5/20/2019,97,3331.73 +212,Vic,Kliner,vkliner5v@cargocollective.com,Male,91.117.26.173,1/22/2019,12,1020.32 +213,Mayor,Drover,mdrover5w@comsenz.com,Male,165.45.244.175,9/18/2019,83,1282.01 +214,Didi,Kanwell,dkanwell5x@mysql.com,Female,249.71.28.189,1/25/2019,39,3401.56 +215,Lothario,Heyworth,lheyworth5y@patch.com,Male,204.254.193.144,6/27/2019,14,2392.22 +216,Adi,Bertome,abertome5z@washingtonpost.com,Female,247.98.53.39,3/19/2019,67,1188.17 +217,Zebadiah,Kryzhov,zkryzhov60@columbia.edu,Male,126.42.169.163,11/28/2019,85,2177.47 +218,Putnam,Wallworke,pwallworke61@sourceforge.net,Male,54.55.143.193,4/5/2019,72,4454.14 +219,Amery,Hitchens,ahitchens62@harvard.edu,Male,48.224.217.2,6/13/2019,64,3615.62 +220,Joana,Davitti,jdavitti63@ebay.co.uk,Female,62.165.254.131,3/4/2019,80,3629.81 +221,Kathleen,Tuer,ktuer64@biblegateway.com,Female,5.70.130.77,3/11/2019,13,1608.46 +222,Renaldo,Campbell-Dunlop,rcampbelldunlop65@skyrock.com,Male,26.131.233.63,12/29/2018,50,3202.62 +223,Titus,Ault,tault66@elpais.com,Male,165.158.147.201,7/2/2019,84,4835.57 +224,Gamaliel,Kilfedder,gkilfedder67@hostgator.com,Male,84.168.17.247,12/23/2018,9,4909.15 +225,Stewart,Boylund,sboylund68@liveinternet.ru,Male,225.118.153.204,6/28/2019,11,3319.59 +226,Jo,Kitchiner,jkitchiner69@skype.com,Female,59.124.146.200,2/6/2019,67,1600.09 +227,Elvis,Fosken,efosken6a@pcworld.com,Male,205.87.85.15,9/9/2019,6,3922.34 +228,Cammie,Danson,cdanson6b@narod.ru,Female,56.105.230.103,2/13/2019,65,1885.94 +229,Fabiano,Garaghan,fgaraghan6c@ezinearticles.com,Male,78.123.49.176,7/20/2019,51,3114.09 +230,Ailina,Givens,agivens6d@cbsnews.com,Female,60.180.138.180,7/5/2019,89,4535.53 +231,Armstrong,Dunkerly,adunkerly6e@independent.co.uk,Male,90.216.21.184,2/26/2019,14,1990.77 +232,Catharine,Matityahu,cmatityahu6f@alibaba.com,Female,57.63.241.97,3/10/2019,95,3363.25 +233,Sile,Glassman,sglassman6g@google.com.au,Female,69.8.60.158,9/2/2019,51,1557.41 +234,Benyamin,Scantleberry,bscantleberry6h@constantcontact.com,Male,130.141.87.136,2/28/2019,73,2950.29 +235,Mendel,Whether,mwhether6i@google.com,Male,241.33.68.87,8/7/2019,82,1090.67 +236,Marcos,Everitt,meveritt6j@yelp.com,Male,233.19.226.38,3/31/2019,37,1052.69 +237,Missie,Eastment,meastment6k@statcounter.com,Female,70.194.212.197,12/20/2018,78,4585.45 +238,Tim,Godfray,tgodfray6l@ucla.edu,Male,200.47.142.40,2/15/2019,5,1682.26 +239,Aguste,Pinney,apinney6m@posterous.com,Male,230.75.22.90,5/30/2019,41,2301.61 +240,Matthias,Francesconi,mfrancesconi6n@biglobe.ne.jp,Male,105.93.88.19,12/8/2018,67,4462.69 +241,Donna,Luckes,dluckes6o@over-blog.com,Female,213.3.126.62,7/2/2019,16,2107.3 +242,Nathanial,Spurden,nspurden6p@microsoft.com,Male,230.177.142.225,3/28/2019,78,1795.33 +243,Stanford,Keets,skeets6q@sbwire.com,Male,192.100.251.103,5/30/2019,60,1912.38 +244,Joly,Mont,jmont6r@technorati.com,Female,14.164.142.106,4/29/2019,23,4036.1 +245,Tyrus,Reasce,treasce6s@ibm.com,Male,12.231.0.1,12/15/2018,60,2707.42 +246,Lee,MacKeogh,lmackeogh6t@woothemes.com,Male,182.53.212.225,12/26/2018,49,1052.96 +247,Valeria,Le Hucquet,vlehucquet6u@cisco.com,Female,155.224.213.71,1/8/2019,67,1292.01 +248,Jillana,Breddy,jbreddy6v@fc2.com,Female,97.42.41.207,1/27/2019,79,2603.53 +249,Alic,Bergeau,abergeau6w@fema.gov,Male,247.140.30.46,10/2/2019,61,1209.85 +250,Cairistiona,Meredith,cmeredith6x@hhs.gov,Female,70.70.142.81,4/12/2019,17,3084.34 +251,Baird,Hollerin,bhollerin6y@issuu.com,Male,22.99.57.132,9/29/2019,50,2427.97 +252,Leonard,Caldecot,lcaldecot6z@vistaprint.com,Male,181.234.61.189,1/17/2019,4,4714.6 +253,Frants,Cochrane,fcochrane70@usgs.gov,Male,243.153.97.44,10/25/2019,7,2131.81 +254,Gertrud,Enticknap,genticknap71@csmonitor.com,Female,252.82.226.210,5/21/2019,11,2407.13 +255,Sullivan,Chattington,schattington72@pinterest.com,Male,155.193.236.102,1/10/2019,82,4204.53 +256,Taite,Sighard,tsighard73@dell.com,Male,201.139.42.108,7/13/2019,40,3983.56 +257,Rozina,Stannas,rstannas74@eventbrite.com,Female,175.179.96.178,1/6/2019,67,4645.75 +258,Sanson,Trevon,strevon75@patch.com,Male,40.224.2.112,5/22/2019,69,4383.12 +259,Torrey,Gilhoolie,tgilhoolie76@cdc.gov,Male,210.149.121.27,6/3/2019,44,2232.38 +260,Lauri,Lackeye,llackeye77@ted.com,Female,104.17.18.94,6/14/2019,21,3534.19 +261,Rica,Vineall,rvineall78@craigslist.org,Female,164.92.184.86,7/29/2019,67,2725.57 +262,Dane,Youell,dyouell79@flavors.me,Male,33.252.240.196,12/2/2018,78,2150.98 +263,Tricia,Singyard,tsingyard7a@facebook.com,Female,117.214.88.252,3/29/2019,46,1692.77 +264,Adrea,Rubinsohn,arubinsohn7b@sakura.ne.jp,Female,27.195.5.14,9/8/2019,72,3230.72 +265,Dotti,Brader,dbrader7c@addthis.com,Female,13.85.47.30,12/26/2018,100,2732.26 +266,Gabi,Fackrell,gfackrell7d@scientificamerican.com,Female,58.145.63.14,8/11/2019,95,1238.44 +267,Price,Moger,pmoger7e@deviantart.com,Male,19.116.110.91,8/15/2019,23,1473.89 +268,Jayme,Wakelin,jwakelin7f@cisco.com,Male,26.252.180.229,10/5/2019,56,4366.22 +269,Maurizio,Dunsmuir,mdunsmuir7g@clickbank.net,Male,120.22.85.145,11/3/2019,31,1877.99 +270,Carlos,Edmands,cedmands7h@redcross.org,Male,20.67.51.110,6/14/2019,79,1238.13 +271,Johan,McGinly,jmcginly7i@amazonaws.com,Male,12.205.191.101,3/20/2019,49,4095.82 +272,Albrecht,Rotchell,arotchell7j@google.es,Male,60.170.217.205,9/28/2019,92,2894.61 +273,Eli,De Lacey,edelacey7k@goo.ne.jp,Male,55.7.194.20,11/10/2019,78,4537.32 +274,Sutherlan,Mill,smill7l@i2i.jp,Male,130.65.164.97,1/16/2019,31,1681.32 +275,Nathalie,Willder,nwillder7m@usnews.com,Female,69.170.87.110,5/5/2019,1,1024.53 +276,Rem,Iczokvitz,riczokvitz7n@harvard.edu,Male,7.203.57.167,4/4/2019,39,3672.82 +277,Lorens,Denyukin,ldenyukin7o@uol.com.br,Male,203.70.155.205,2/14/2019,41,3600.8 +278,Mag,Pigden,mpigden7p@psu.edu,Female,239.194.157.251,6/14/2019,47,3599.97 +279,Petronia,Carvill,pcarvill7q@apple.com,Female,151.29.121.135,8/16/2019,68,4879.68 +280,Felicity,Capewell,fcapewell7r@patch.com,Female,176.136.185.249,9/14/2019,75,2448.16 +281,Hartwell,Zellner,hzellner7s@wordpress.org,Male,210.182.89.221,5/26/2019,68,4758.87 +282,Marcela,Geggie,mgeggie7t@samsung.com,Female,118.65.180.139,2/10/2019,15,4222.62 +283,Keith,Loxston,kloxston7u@etsy.com,Male,30.75.222.61,3/31/2019,90,1964.35 +284,Mattheus,Guinn,mguinn7v@virginia.edu,Male,157.141.233.64,2/5/2019,20,1509.77 +285,Cristy,Van den Bosch,cvandenbosch7w@yandex.ru,Female,106.186.220.48,2/10/2019,57,2650.92 +286,Pansie,Pull,ppull7x@tripod.com,Female,133.223.218.112,3/12/2019,16,1844.52 +287,Hyacinthie,Bools,hbools7y@phpbb.com,Female,6.136.111.144,6/24/2019,88,3753.3 +288,Cosimo,Humphris,chumphris7z@ftc.gov,Male,42.252.197.80,1/8/2019,5,1335.74 +289,Raquela,Ledbetter,rledbetter80@imdb.com,Female,25.84.71.62,12/13/2018,39,4474.51 +290,Domingo,McKelvey,dmckelvey81@globo.com,Male,139.34.201.250,9/6/2019,40,4039.82 +291,Britta,Grzesiak,bgrzesiak82@google.fr,Female,254.24.80.245,7/7/2019,73,1052.79 +292,Palmer,Gliddon,pgliddon83@example.com,Male,60.248.224.227,12/27/2018,33,4273.36 +293,Millard,Feore,mfeore84@mlb.com,Male,129.50.148.247,4/3/2019,60,2015.88 +294,Lanie,Blasgen,lblasgen85@ocn.ne.jp,Female,124.254.122.231,10/6/2019,35,1212.04 +295,Sherry,Jamrowicz,sjamrowicz86@newyorker.com,Female,8.187.192.111,6/12/2019,57,2819.04 +296,Payton,Loxton,ploxton87@ted.com,Male,20.185.112.233,5/16/2019,9,4976.7 +297,Anderea,Blandford,ablandford88@tinypic.com,Female,30.186.15.181,2/3/2019,54,3962.4 +298,Adele,Goldstein,agoldstein89@csmonitor.com,Female,101.24.151.157,8/29/2019,47,1300.26 +299,Christiana,Kybbye,ckybbye8a@soup.io,Female,164.124.255.193,8/24/2019,35,4524.75 +300,Bettine,Bryden,bbryden8b@tinyurl.com,Female,71.23.174.26,4/21/2019,27,3413.85 +301,Halsey,Deeble,hdeeble8c@gizmodo.com,Male,17.234.31.127,8/30/2019,74,2872.84 +302,Peggi,Gallier,pgallier8d@aol.com,Female,34.161.21.167,3/28/2019,26,2530.19 +303,Oby,Lemanu,olemanu8e@skyrock.com,Male,123.215.166.71,10/24/2019,90,2590.72 +304,Kelly,Limbert,klimbert8f@123-reg.co.uk,Male,225.87.194.99,7/15/2019,71,3252.4 +305,Edmon,Pittham,epittham8g@wufoo.com,Male,206.168.11.133,11/1/2019,84,4290.32 +306,Nicola,Hazelby,nhazelby8h@nih.gov,Male,248.203.87.61,8/14/2019,33,4439.66 +307,Norry,Cheesworth,ncheesworth8i@spotify.com,Male,188.191.145.160,9/14/2019,19,3777.37 +308,Madeleine,Colchett,mcolchett8j@goo.ne.jp,Female,49.89.233.117,10/26/2019,97,3215.79 +309,Alyse,Rilston,arilston8k@friendfeed.com,Female,178.80.192.158,4/26/2019,10,3704.99 +310,Aubrey,Rodrigues,arodrigues8l@clickbank.net,Female,110.27.123.138,2/26/2019,82,3312.78 +311,Wade,Fitzgerald,wfitzgerald8m@blogspot.com,Male,83.93.6.27,4/14/2019,18,4200.81 +312,Bar,Aspall,baspall8n@jimdo.com,Male,220.44.202.201,6/14/2019,31,3252.11 +313,Martynne,Jurczik,mjurczik8o@homestead.com,Female,45.95.32.123,4/16/2019,64,3091.17 +314,Rem,Binion,rbinion8p@spiegel.de,Male,194.45.225.156,10/13/2019,32,1460.2 +315,Brocky,Picker,bpicker8q@usda.gov,Male,43.227.235.110,6/26/2019,13,2705.18 +316,Caspar,Frankowski,cfrankowski8r@baidu.com,Male,77.43.200.240,4/9/2019,65,3908.27 +317,Fernanda,Jerman,fjerman8s@qq.com,Female,251.97.23.5,12/2/2018,98,2644.07 +318,Kirbie,Hickford,khickford8t@phoca.cz,Female,222.19.145.98,2/18/2019,75,2101.99 +319,Justen,Simonsson,jsimonsson8u@goodreads.com,Male,50.143.144.122,5/18/2019,23,3668.84 +320,Base,Quainton,bquainton8v@quantcast.com,Male,210.105.216.149,9/25/2019,17,3690.64 +321,Evonne,Biss,ebiss8w@blogspot.com,Female,151.218.87.78,6/20/2019,54,2157.7 +322,Desmund,Naris,dnaris8x@cafepress.com,Male,158.117.9.15,4/8/2019,55,3607.48 +323,Briant,Pulley,bpulley8y@rediff.com,Male,226.108.222.137,7/8/2019,4,2048.56 +324,Neddy,Wannop,nwannop8z@cisco.com,Male,183.31.92.122,8/27/2019,59,3925.77 +325,Trip,Eberst,teberst90@so-net.ne.jp,Male,147.174.43.177,3/9/2019,54,3125.57 +326,Feodor,Ccomini,fccomini91@patch.com,Male,101.243.251.93,9/13/2019,9,1769.13 +327,Lazar,Krinks,lkrinks92@infoseek.co.jp,Male,21.83.214.146,10/20/2019,24,4619.45 +328,Tate,Kedslie,tkedslie93@businesswire.com,Female,141.169.231.230,5/30/2019,49,1552.68 +329,Jessee,Boggon,jboggon94@kickstarter.com,Male,203.194.131.251,11/9/2019,24,1485.03 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+364,Yorke,Stickins,ystickinsa3@joomla.org,Male,43.66.244.157,9/2/2019,69,4537.3 +365,Niven,Carlow,ncarlowa4@berkeley.edu,Male,172.206.45.58,7/5/2019,65,3926.86 +366,Dexter,Roycraft,droycrafta5@bbb.org,Male,77.45.212.134,7/21/2019,55,3035.99 +367,Nicoline,Maryin,nmaryina6@google.com.au,Female,47.168.8.172,8/1/2019,41,4917.76 +368,Ryon,Manuaud,rmanuauda7@domainmarket.com,Male,47.123.90.232,2/9/2019,74,2439.17 +369,Yehudi,Paylie,ypayliea8@domainmarket.com,Male,191.251.55.194,1/27/2019,6,1923.42 +370,Griffith,Tincey,gtinceya9@oaic.gov.au,Male,25.66.148.134,9/12/2019,39,2655.81 +371,Rafa,McLeman,rmclemanaa@mac.com,Female,230.203.148.84,5/6/2019,15,1870.44 +372,Ethel,Deuss,edeussab@hp.com,Female,28.53.103.241,5/16/2019,32,1469.62 +373,Vasily,Sockell,vsockellac@istockphoto.com,Male,56.222.178.227,3/11/2019,69,1735.2 +374,Lorenza,Adelsberg,ladelsbergad@fda.gov,Female,11.94.242.174,1/22/2019,86,3213.96 +375,Colas,Wakerley,cwakerleyae@hostgator.com,Male,145.79.64.143,10/18/2019,32,3929.61 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+399,Broddie,Dunster,bdunsterb2@businesswire.com,Male,195.171.91.125,5/13/2019,35,1781.96 +400,Brigit,Brownsworth,bbrownsworthb3@yale.edu,Female,235.56.89.121,5/7/2019,11,2976.14 +401,Hillary,Daughtrey,hdaughtreyb4@zimbio.com,Male,171.99.161.95,3/31/2019,14,4683.28 +402,Rubetta,Ryding,rrydingb5@facebook.com,Female,121.209.254.88,2/16/2019,48,3181.31 +403,Darill,Giffin,dgiffinb6@so-net.ne.jp,Male,76.229.144.119,12/1/2019,20,1267.15 +404,Meredith,Leghorn,mleghornb7@google.co.jp,Female,88.130.30.36,3/6/2019,73,1165.58 +405,Godfrey,Kleinhandler,gkleinhandlerb8@auda.org.au,Male,202.128.125.132,2/14/2019,50,4151.4 +406,Andie,Tootell,atootellb9@netscape.com,Male,145.145.81.122,6/12/2019,41,1126.15 +407,Roselia,Craise,rcraiseba@java.com,Female,200.79.82.15,6/21/2019,56,4562.67 +408,Conny,Saice,csaicebb@baidu.com,Male,242.82.15.36,11/6/2019,20,4226.99 +409,Keary,Hartrick,khartrickbc@independent.co.uk,Male,189.126.222.123,12/13/2018,86,3150.61 +410,Salli,Scotchmer,sscotchmerbd@cloudflare.com,Female,229.204.119.25,1/15/2019,85,4684.69 +411,Verena,Everington,veveringtonbe@delicious.com,Female,49.206.228.25,4/25/2019,67,2869.88 +412,Bambi,Berzon,bberzonbf@dailymotion.com,Female,47.88.180.96,10/26/2019,67,4843.29 +413,Barbara,Kovacs,bkovacsbg@deliciousdays.com,Female,116.1.242.72,4/20/2019,43,2218.64 +414,Laurice,Adrianello,ladrianellobh@nbcnews.com,Female,69.69.82.208,6/21/2019,91,3331.25 +415,Nadine,Stenning,nstenningbi@tripadvisor.com,Female,105.204.235.18,9/2/2019,13,2037.16 +416,Cristina,Milsap,cmilsapbj@t-online.de,Female,36.13.98.63,12/4/2018,51,2933.37 +417,Kaleb,Easby,keasbybk@howstuffworks.com,Male,59.120.45.235,12/11/2018,4,1277.52 +418,Alister,Kyberd,akyberdbl@wsj.com,Male,184.221.78.151,5/1/2019,85,3931.28 +419,Roma,Uvedale,ruvedalebm@jigsy.com,Male,127.113.15.154,7/21/2019,71,4158.48 +420,Chrisse,Briggs,cbriggsbn@godaddy.com,Male,36.183.171.148,7/26/2019,63,1042.39 +421,Armin,Fidelus,afidelusbo@sogou.com,Male,207.188.30.35,1/13/2019,52,2565.26 +422,Helena,Harlow,hharlowbp@archive.org,Female,125.154.161.88,7/31/2019,11,3388.81 +423,Judy,Stone Fewings,jstonefewingsbq@youtube.com,Female,220.36.160.104,10/2/2019,54,2051.64 +424,Eddi,Gritsaev,egritsaevbr@sphinn.com,Female,36.194.183.230,6/1/2019,60,3020.44 +425,Royal,Juares,rjuaresbs@craigslist.org,Male,84.222.181.156,5/30/2019,30,2382.04 +426,Violette,Kopje,vkopjebt@blogs.com,Female,12.135.203.81,5/12/2019,61,3565.51 +427,Lesli,Franses,lfransesbu@histats.com,Female,124.138.107.148,11/22/2019,71,2644.08 +428,Alric,Barehead,abareheadbv@stumbleupon.com,Male,220.251.223.254,12/17/2018,6,2989.17 +429,Karin,Rosingdall,krosingdallbw@ucoz.ru,Female,83.135.106.197,8/9/2019,89,1199.79 +430,Margaretta,Hartles,mhartlesbx@google.com.br,Female,146.47.193.138,10/15/2019,82,1786.77 +431,Broderic,Adenot,badenotby@upenn.edu,Male,131.77.120.209,9/22/2019,15,4020.02 +432,Konstanze,Scintsbury,kscintsburybz@eepurl.com,Female,187.225.93.97,9/12/2019,91,3882.0 +433,Heriberto,Branchett,hbranchettc0@google.ca,Male,47.131.19.61,9/10/2019,30,3830.84 +434,Dulcy,Moxham,dmoxhamc1@pinterest.com,Female,139.65.244.96,3/31/2019,48,1930.97 +435,Kakalina,Pellamont,kpellamontc2@51.la,Female,46.138.21.69,3/29/2019,53,2265.45 +436,Durward,McAlester,dmcalesterc3@wisc.edu,Male,43.93.21.201,4/1/2019,19,4312.73 +437,Alfy,Dawby,adawbyc4@alibaba.com,Female,60.254.119.60,7/12/2019,53,1447.77 +438,Rafferty,Cullabine,rcullabinec5@acquirethisname.com,Male,107.140.224.21,8/31/2019,10,4627.05 +439,Catlaina,Laurenty,claurentyc6@tripod.com,Female,72.146.104.69,5/8/2019,77,1050.26 +440,Yves,Kinnock,ykinnockc7@narod.ru,Male,212.80.251.121,9/24/2019,26,4289.62 +441,Rustie,Runham,rrunhamc8@google.cn,Male,14.208.111.115,3/30/2019,17,3840.32 +442,Shannon,Teece,steecec9@issuu.com,Female,224.215.224.129,11/26/2019,87,3028.32 +443,Tawsha,Huggons,thuggonsca@oakley.com,Female,28.182.47.108,12/9/2018,80,3985.84 +444,Jozef,Ropcke,jropckecb@washington.edu,Male,71.34.133.206,10/5/2019,51,2177.5 +445,Brittany,Treadgold,btreadgoldcc@dell.com,Female,70.179.135.39,10/6/2019,35,1239.98 +446,Reynolds,Dungay,rdungaycd@taobao.com,Male,198.198.184.105,3/17/2019,84,1401.56 +447,Deeyn,MacNeely,dmacneelyce@google.nl,Female,129.131.12.166,9/24/2019,67,2584.15 +448,Opalina,McAndrew,omcandrewcf@imgur.com,Female,170.144.235.40,9/27/2019,32,1990.03 +449,Annabell,Bowne,abownecg@tamu.edu,Female,15.103.10.164,11/4/2019,82,4979.86 +450,Shepherd,Toye,stoyech@oakley.com,Male,28.94.51.150,5/4/2019,79,3581.94 +451,Dolorita,Rowatt,drowattci@yale.edu,Female,199.209.35.59,1/7/2019,46,2901.35 +452,Gale,Fenich,gfenichcj@seattletimes.com,Female,117.140.18.113,2/2/2019,95,1402.56 +453,Salvidor,Ruperti,srupertick@mashable.com,Male,253.18.192.61,6/10/2019,83,4161.11 +454,Griffie,Colthard,gcolthardcl@google.com.br,Male,125.41.227.143,5/28/2019,72,2219.96 +455,York,Rittmeyer,yrittmeyercm@redcross.org,Male,117.143.157.105,12/25/2018,69,2618.72 +456,Sarene,Brantzen,sbrantzencn@dion.ne.jp,Female,243.21.1.226,1/14/2019,99,1604.26 +457,Maximilien,Jouhan,mjouhanco@dailymotion.com,Male,135.19.146.201,8/12/2019,22,2904.57 +458,Valma,Chipping,vchippingcp@last.fm,Female,106.227.96.134,12/11/2018,21,2367.02 +459,Delmar,Kaas,dkaascq@histats.com,Male,149.43.164.167,8/29/2019,33,1178.21 +460,Dalli,MacBain,dmacbaincr@tripod.com,Male,179.253.105.225,6/28/2019,57,4678.73 +461,Griz,Macquire,gmacquirecs@google.cn,Male,131.224.85.128,8/28/2019,22,1605.86 +462,Far,Ogden,fogdenct@reverbnation.com,Male,164.236.27.67,12/10/2018,46,3121.44 +463,Eleen,Norree,enorreecu@telegraph.co.uk,Female,172.69.9.125,4/6/2019,18,4440.78 +464,Franzen,Izkoveski,fizkoveskicv@issuu.com,Male,92.192.132.174,9/19/2019,12,2651.29 +465,Rhiamon,Saladino,rsaladinocw@wix.com,Female,123.91.96.42,4/10/2019,88,4982.0 +466,Franny,Grinin,fgrinincx@booking.com,Male,43.0.247.215,3/26/2019,89,3774.54 +467,Colin,O'Donohue,codonohuecy@go.com,Male,88.162.185.175,10/22/2019,3,3491.68 +468,Wendye,Compson,wcompsoncz@hud.gov,Female,136.150.155.64,11/13/2019,74,4538.45 +469,Dyane,Raithmill,draithmilld0@ted.com,Female,235.21.43.42,5/16/2019,17,3995.57 +470,Lacie,Dumper,ldumperd1@ycombinator.com,Female,211.130.88.136,5/5/2019,21,4467.62 +471,Talbert,Jurca,tjurcad2@soup.io,Male,110.180.63.75,4/11/2019,72,1047.55 +472,Blondelle,Ingerfield,bingerfieldd3@bbc.co.uk,Female,21.57.56.111,10/18/2019,36,4305.36 +473,Huntington,Keele,hkeeled4@wired.com,Male,29.115.183.130,1/18/2019,90,3988.22 +474,Merle,Lissenden,mlissendend5@google.ru,Male,181.207.215.130,9/9/2019,14,3780.87 +475,Ahmed,Lowthorpe,alowthorped6@fotki.com,Male,170.252.164.227,7/24/2019,56,1887.16 +476,Melli,Monsey,mmonseyd7@house.gov,Female,226.80.98.178,5/13/2019,14,2671.62 +477,Shelly,Flay,sflayd8@epa.gov,Female,77.216.133.212,7/25/2019,13,4660.94 +478,Ryun,Maffey,rmaffeyd9@discovery.com,Male,130.172.183.105,12/18/2018,45,3261.01 +479,Archer,Monksfield,amonksfieldda@google.com.br,Male,182.48.50.111,8/2/2019,52,3036.93 +480,Rosemonde,Wragg,rwraggdb@icq.com,Female,195.223.97.213,8/25/2019,56,1193.62 +481,Halsey,Robbeke,hrobbekedc@cnet.com,Male,232.48.110.118,2/3/2019,42,1298.35 +482,Paten,Paslow,ppaslowdd@instagram.com,Male,29.24.97.210,5/13/2019,98,2545.85 +483,Dora,Ghidoli,dghidolide@digg.com,Female,58.13.173.228,8/13/2019,89,2796.74 +484,Arlene,Dronsfield,adronsfielddf@t.co,Female,79.160.187.181,3/18/2019,55,2539.18 +485,Blakelee,Lipp,blippdg@sakura.ne.jp,Female,19.124.37.230,8/29/2019,1,4628.15 +486,Cecelia,Grinham,cgrinhamdh@twitter.com,Female,234.55.104.182,2/3/2019,48,1693.35 +487,Onofredo,Andreazzi,oandreazzidi@google.ca,Male,117.52.172.57,7/29/2019,53,2615.64 +488,Kiley,Burdekin,kburdekindj@shop-pro.jp,Female,201.52.16.189,8/24/2019,95,3929.48 +489,Bink,Kestin,bkestindk@chron.com,Male,100.111.135.87,7/13/2019,21,1958.77 +490,Mitch,Cagan,mcagandl@pagesperso-orange.fr,Male,25.244.113.46,10/16/2019,82,2492.12 +491,Charlton,Hughes,chughesdm@dmoz.org,Male,29.138.111.115,11/12/2019,10,3339.82 +492,Doria,Ivatts,divattsdn@nih.gov,Female,242.17.53.201,5/29/2019,72,2228.99 +493,Coriss,Lathan,clathando@domainmarket.com,Female,25.75.179.134,10/4/2019,23,4848.67 +494,Elmore,Reside,eresidedp@diigo.com,Male,79.112.126.164,9/21/2019,59,1610.62 +495,Ezekiel,Scherer,eschererdq@telegraph.co.uk,Male,79.39.63.134,8/28/2019,69,1597.98 +496,Werner,Cobson,wcobsondr@artisteer.com,Male,227.194.178.190,3/13/2019,53,4233.49 +497,Randee,Ornillos,rornillosds@youtube.com,Female,67.192.57.128,12/22/2018,59,2404.97 +498,Gottfried,Carloni,gcarlonidt@google.ca,Male,176.184.59.246,7/25/2019,33,4007.86 +499,Findlay,Ferrieroi,fferrieroidu@edublogs.org,Male,164.15.3.146,6/15/2019,29,1439.89 +500,Anestassia,Caldron,acaldrondv@imdb.com,Female,73.5.249.139,12/5/2018,77,4450.98 +501,Ignazio,Prinnett,iprinnettdw@merriam-webster.com,Male,254.189.253.253,5/26/2019,53,3955.94 +502,Debi,Sedgebeer,dsedgebeerdx@msn.com,Female,90.91.234.210,11/26/2019,96,1951.45 +503,Beverlee,Colliard,bcolliarddy@stanford.edu,Female,112.73.182.216,8/1/2019,60,4097.93 +504,Cinnamon,McFadin,cmcfadindz@blogs.com,Female,149.198.138.102,4/3/2019,80,3719.88 +505,Walden,Bruinemann,wbruinemanne0@360.cn,Male,21.212.132.5,11/7/2019,92,2275.38 +506,Quinton,Glanfield,qglanfielde1@amazon.de,Male,213.175.246.203,12/13/2018,8,4630.05 +507,Cello,Potkins,cpotkinse2@github.com,Male,135.169.51.63,3/16/2019,63,2813.83 +508,Percival,Coley,pcoleye3@photobucket.com,Male,205.135.254.197,1/14/2019,93,3766.81 +509,Brockie,Siddens,bsiddense4@usda.gov,Male,166.107.18.144,7/26/2019,11,4226.07 +510,Emmerich,Eglise,eeglisee5@istockphoto.com,Male,39.239.19.196,11/3/2019,23,1444.76 +511,Theo,Olivetta,tolivettae6@yellowpages.com,Male,135.95.4.136,8/1/2019,59,3670.72 +512,Gabie,Blasius,gblasiuse7@vistaprint.com,Male,176.138.63.24,3/12/2019,100,3276.6 +513,Tiebold,Wharmby,twharmbye8@chicagotribune.com,Male,204.219.78.122,6/13/2019,73,3542.76 +514,Tann,Duke,tdukee9@irs.gov,Male,153.153.136.24,3/14/2019,70,2338.35 +515,Westley,Ottery,wotteryea@soundcloud.com,Male,35.239.11.15,9/18/2019,61,2876.26 +516,Noll,Birkmyre,nbirkmyreeb@histats.com,Male,45.218.226.8,5/4/2019,3,3180.04 +517,Angelle,Canizares,acanizaresec@usgs.gov,Female,79.98.22.161,11/16/2019,57,1414.53 +518,Maddy,Rawlins,mrawlinsed@ocn.ne.jp,Female,194.212.205.87,8/24/2019,9,2037.3 +519,Gonzales,Digle,gdigleee@tinyurl.com,Male,188.81.253.229,2/11/2019,31,1096.61 +520,Matias,Arrighetti,marrighettief@jimdo.com,Male,119.210.95.197,10/13/2019,59,1525.12 +521,Ag,Almon,aalmoneg@discuz.net,Female,211.209.145.71,5/9/2019,5,1832.58 +522,Romona,Keppel,rkeppeleh@auda.org.au,Female,180.44.222.236,12/5/2018,69,1470.18 +523,Aeriel,Seear,aseearei@over-blog.com,Female,132.233.16.148,6/8/2019,20,4592.62 +524,Terri,Trulocke,ttrulockeej@clickbank.net,Female,138.176.80.221,10/1/2019,5,1787.96 +525,Linn,Stonestreet,lstonestreetek@un.org,Male,74.243.5.94,11/27/2019,31,1779.4 +526,Dmitri,Coode,dcoodeel@ameblo.jp,Male,201.81.71.70,1/25/2019,94,1400.26 +527,Alane,Sabattier,asabattierem@scribd.com,Female,32.109.38.132,10/10/2019,10,1416.04 +528,Sadye,Esplin,sesplinen@tiny.cc,Female,76.85.72.29,3/14/2019,45,3626.01 +529,Alexia,Iorizzo,aiorizzoeo@themeforest.net,Female,172.234.209.31,7/10/2019,46,4064.06 +530,Gayelord,Kalewe,gkaleweep@over-blog.com,Male,83.169.129.240,9/10/2019,53,1508.96 +531,Clive,Stuchbery,cstuchberyeq@dell.com,Male,9.182.133.41,12/29/2018,21,4980.91 +532,Derron,Lober,dloberer@epa.gov,Male,184.138.78.88,2/14/2019,24,2245.42 +533,Aleda,Addison,aaddisones@wsj.com,Female,181.181.179.67,6/17/2019,81,4493.79 +534,Marshall,Ugolotti,mugolottiet@aboutads.info,Male,159.50.251.20,7/3/2019,35,1104.16 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+559,Shae,Potteril,spotterilfi@cyberchimps.com,Male,135.218.100.211,9/30/2019,43,2747.3 +560,Kim,Kittel,kkittelfj@buzzfeed.com,Male,224.125.75.142,12/12/2018,17,4109.17 +561,Alexei,Morecomb,amorecombfk@independent.co.uk,Male,172.224.245.105,12/9/2018,33,2899.08 +562,Cathryn,Finnan,cfinnanfl@ask.com,Female,27.110.235.165,5/28/2019,94,4841.66 +563,Maryjane,Duffus,mduffusfm@eventbrite.com,Female,3.255.15.114,10/22/2019,54,1514.19 +564,Violetta,Reisk,vreiskfn@typepad.com,Female,39.227.35.28,4/18/2019,92,4609.62 +565,Vicky,Lots,vlotsfo@cisco.com,Female,231.114.228.254,7/16/2019,5,2747.65 +566,Odilia,Shemmans,oshemmansfp@phpbb.com,Female,173.16.238.214,7/15/2019,64,2509.13 +567,Doralynn,Borne,dbornefq@vistaprint.com,Female,182.193.106.25,9/27/2019,58,2388.86 +568,Gallagher,Tinsey,gtinseyfr@wsj.com,Male,222.169.135.174,8/26/2019,9,2587.26 +569,Ulberto,Hellyar,uhellyarfs@yellowbook.com,Male,248.76.37.74,7/15/2019,30,1922.6 +570,Carilyn,Skones,cskonesft@liveinternet.ru,Female,156.207.111.147,5/19/2019,37,4692.05 +571,Sarine,Rubinovitch,srubinovitchfu@networksolutions.com,Female,80.120.49.55,8/19/2019,20,1594.95 +572,Jemmy,Marlowe,jmarlowefv@engadget.com,Female,75.14.237.58,4/14/2019,51,1904.61 +573,Gaby,Demelt,gdemeltfw@infoseek.co.jp,Male,229.88.216.80,2/3/2019,51,1648.97 +574,Jacob,Lamberton,jlambertonfx@hugedomains.com,Male,47.215.55.112,11/23/2019,98,2245.77 +575,Emile,Bullivent,ebulliventfy@is.gd,Male,222.149.227.61,1/14/2019,28,2610.31 +576,Reidar,Gierardi,rgierardifz@flickr.com,Male,137.20.90.27,7/14/2019,44,1963.44 +577,Dwain,De Mars,ddemarsg0@theglobeandmail.com,Male,29.126.92.147,5/8/2019,83,2977.28 +578,Seth,Hebron,shebrong1@github.io,Male,97.93.183.184,2/17/2019,17,2725.06 +579,Shep,Rabjohn,srabjohng2@sfgate.com,Male,118.230.96.220,4/5/2019,75,4150.25 +580,Georgine,Martill,gmartillg3@who.int,Female,89.160.34.190,1/8/2019,37,4712.71 +581,Theodora,Mitchenson,tmitchensong4@delicious.com,Female,140.245.239.155,4/11/2019,58,1505.59 +582,Geri,Reedman,greedmang5@mac.com,Male,149.218.213.221,6/22/2019,29,3919.37 +583,Hamel,Ruddick,hruddickg6@uiuc.edu,Male,74.103.5.12,3/19/2019,32,2242.44 +584,Marylou,Baggott,mbaggottg7@goo.gl,Female,67.211.254.135,11/7/2019,35,4631.99 +585,Ignacius,Croisdall,icroisdallg8@imdb.com,Male,194.136.32.103,4/9/2019,22,4505.82 +586,Agnes,Pentercost,apentercostg9@yolasite.com,Female,201.109.22.156,4/7/2019,6,2659.75 +587,Phillipe,O'Henery,poheneryga@baidu.com,Male,169.210.213.194,4/28/2019,100,3089.16 +588,Mersey,Bilston,mbilstongb@mozilla.org,Female,212.118.21.202,6/6/2019,47,1546.62 +589,Carie,Pawelski,cpawelskigc@examiner.com,Female,185.32.131.13,12/23/2018,13,1607.17 +590,Cassandra,Zywicki,czywickigd@ask.com,Female,58.164.239.126,10/6/2019,86,3316.82 +591,Myrtia,Avrasin,mavrasinge@tripod.com,Female,23.46.205.131,6/2/2019,28,1376.97 +592,Stanford,Greatrakes,sgreatrakesgf@europa.eu,Male,110.99.123.217,12/31/2018,89,4653.75 +593,Giulietta,Hinkens,ghinkensgg@list-manage.com,Female,146.42.27.152,3/17/2019,90,4663.89 +594,Dame,Eam,deamgh@jalbum.net,Male,93.210.79.222,12/3/2018,99,4955.03 +595,Claresta,Spradbery,cspradberygi@wunderground.com,Female,177.74.189.41,5/7/2019,69,2660.67 +596,Charisse,Janos,cjanosgj@yellowbook.com,Female,107.1.160.123,11/30/2019,27,3260.09 +597,Mufi,Letterick,mletterickgk@apache.org,Female,230.153.229.231,6/16/2019,59,2986.56 +598,Fabe,Sabberton,fsabbertongl@mapquest.com,Male,219.168.121.194,7/18/2019,70,3658.28 +599,Nikolas,Gildersleaves,ngildersleavesgm@ox.ac.uk,Male,162.123.231.93,7/20/2019,78,1233.59 +600,Justin,Vigours,jvigoursgn@sourceforge.net,Male,154.142.42.26,6/28/2019,31,4990.95 +601,Kile,Le Clercq,kleclercqgo@indiatimes.com,Male,239.28.13.21,12/13/2018,84,1001.19 +602,Isidoro,Tregonna,itregonnagp@webeden.co.uk,Male,171.67.239.44,1/16/2019,41,4417.17 +603,Mab,Ironside,mironsidegq@google.cn,Female,42.162.241.202,7/23/2019,91,2259.53 +604,Dimitry,Manass,dmanassgr@51.la,Male,45.173.17.98,1/25/2019,46,1586.27 +605,Rossy,Arden,rardengs@bigcartel.com,Male,214.115.174.58,4/28/2019,51,1823.4 +606,Daniele,Barnard,dbarnardgt@so-net.ne.jp,Female,135.128.233.112,2/1/2019,71,3995.81 +607,Helene,Westphalen,hwestphalengu@addthis.com,Female,106.111.55.197,11/15/2019,95,2675.0 +608,Zeke,Mixer,zmixergv@europa.eu,Male,70.187.67.163,2/1/2019,26,1019.63 +609,Modesta,Cock,mcockgw@berkeley.edu,Female,16.132.149.179,1/19/2019,79,2922.43 +610,Bard,Vieyra,bvieyragx@shinystat.com,Male,192.107.97.186,10/5/2019,17,4998.16 +611,Boote,Geall,bgeallgy@networksolutions.com,Male,54.164.158.9,1/1/2019,95,3385.89 +612,Ninnetta,Loveman,nlovemangz@histats.com,Female,25.150.239.54,9/19/2019,24,3487.97 +613,Sigismund,Fitzer,sfitzerh0@163.com,Male,93.203.46.222,9/7/2019,26,3405.11 +614,Perren,Haquin,phaquinh1@networkadvertising.org,Male,27.195.139.40,7/13/2019,95,4472.37 +615,Ramsay,Galland,rgallandh2@sohu.com,Male,64.157.166.62,2/15/2019,30,3256.71 +616,Silva,Strete,sstreteh3@yellowpages.com,Female,28.120.167.41,10/30/2019,67,4109.49 +617,Dee dee,Poate,dpoateh4@github.io,Female,169.234.44.240,1/26/2019,87,2487.52 +618,Bianca,Martell,bmartellh5@bloomberg.com,Female,17.155.34.89,12/5/2018,88,2523.49 +619,Alan,Wallwork,awallworkh6@bloglines.com,Male,136.41.225.203,11/17/2019,32,2593.63 +620,Alysa,Lembrick,alembrickh7@github.io,Female,4.186.245.140,3/12/2019,42,4087.94 +621,Yoshiko,Alans,yalansh8@list-manage.com,Female,113.124.38.19,1/8/2019,5,3091.72 +622,Chelsy,Watford,cwatfordh9@yellowpages.com,Female,10.18.35.232,5/4/2019,86,4661.23 +623,Bill,Gotthard,bgotthardha@moonfruit.com,Male,169.67.131.116,5/21/2019,11,1287.77 +624,Cad,Kirtlan,ckirtlanhb@xrea.com,Male,40.152.181.2,1/24/2019,24,3155.78 +625,Ahmed,McClenan,amcclenanhc@webmd.com,Male,20.99.191.248,8/13/2019,36,3301.08 +626,Tobit,Barthelemy,tbarthelemyhd@bizjournals.com,Male,35.143.211.133,9/10/2019,98,1382.31 +627,Kacie,Lurner,klurnerhe@illinois.edu,Female,4.70.92.52,3/14/2019,58,3754.13 +628,Keith,Skeech,kskeechhf@pbs.org,Male,14.243.98.120,8/22/2019,99,2840.36 +629,Loy,Brettoner,lbrettonerhg@marriott.com,Male,244.228.186.208,5/28/2019,83,1322.53 +630,Ring,Keppy,rkeppyhh@mediafire.com,Male,63.223.185.137,10/29/2019,49,3414.88 +631,Jennee,Saltsberg,jsaltsberghi@angelfire.com,Female,168.154.55.70,2/20/2019,89,1792.13 +632,Leslie,Sunley,lsunleyhj@furl.net,Female,142.154.216.195,1/14/2019,85,1049.36 +633,Serge,Brightman,sbrightmanhk@nsw.gov.au,Male,218.24.194.199,8/25/2019,17,3821.08 +634,Thadeus,Janosevic,tjanosevichl@auda.org.au,Male,117.42.23.253,6/4/2019,12,3771.8 +635,Spense,Ribchester,sribchesterhm@google.cn,Male,60.125.96.255,3/20/2019,10,4152.59 +636,Cordelia,Petrashov,cpetrashovhn@jalbum.net,Female,4.255.192.99,5/3/2019,42,4778.71 +637,Rhea,Jest,rjestho@hao123.com,Female,60.250.169.224,11/1/2019,88,3984.23 +638,Patricia,Feehery,pfeeheryhp@dot.gov,Female,238.226.108.19,1/3/2019,72,2436.71 +639,Griffie,Haswell,ghaswellhq@linkedin.com,Male,141.208.249.19,3/30/2019,54,4591.33 +640,Isahella,Brockett,ibrocketthr@webnode.com,Female,187.222.253.93,8/18/2019,40,2025.08 +641,Rhea,Castiglioni,rcastiglionihs@cpanel.net,Female,59.223.205.19,3/28/2019,8,4878.09 +642,Stu,Gladman,sgladmanht@yandex.ru,Male,148.174.89.129,2/7/2019,6,4658.63 +643,Meade,Penn,mpennhu@tinypic.com,Female,159.237.205.132,7/14/2019,67,1741.76 +644,Analise,Teesdale,ateesdalehv@istockphoto.com,Female,112.255.159.227,4/27/2019,43,4943.97 +645,Gabbie,Gawith,ggawithhw@bing.com,Male,156.88.177.49,9/1/2019,30,2127.26 +646,Fancie,Matura,fmaturahx@prweb.com,Female,245.199.208.176,10/14/2019,48,3611.12 +647,Oneida,Klee,okleehy@merriam-webster.com,Female,76.241.235.4,4/24/2019,75,3574.83 +648,Patty,Deadman,pdeadmanhz@deviantart.com,Female,119.238.140.122,12/3/2018,78,4990.77 +649,Rica,Habbershon,rhabbershoni0@imdb.com,Female,235.54.40.9,8/21/2019,72,4008.1 +650,Dominick,Morphey,dmorpheyi1@state.gov,Male,26.182.126.198,9/27/2019,32,2803.47 +651,Lin,Elvey,lelveyi2@qq.com,Female,217.142.5.50,5/2/2019,75,1382.58 +652,Candra,Ellershaw,cellershawi3@liveinternet.ru,Female,37.103.239.8,11/5/2019,64,1785.17 +653,Petr,Blamey,pblameyi4@cornell.edu,Male,102.165.209.59,11/16/2019,23,4146.8 +654,Starlene,Shoebrook,sshoebrooki5@un.org,Female,168.143.167.147,3/14/2019,4,4991.14 +655,Rochelle,Inggall,ringgalli6@biglobe.ne.jp,Female,102.127.212.247,9/12/2019,81,1583.69 +656,Ezmeralda,Jubert,ejuberti7@icio.us,Female,81.4.17.157,7/22/2019,52,4205.34 +657,Berny,Crufts,bcruftsi8@jigsy.com,Female,244.211.38.231,1/6/2019,46,4968.82 +658,Philis,Hitzke,phitzkei9@google.es,Female,206.218.221.24,10/5/2019,21,2082.32 +659,Dacey,Beck,dbeckia@netscape.com,Female,52.22.157.97,4/17/2019,22,3785.01 +660,Lammond,Esmead,lesmeadib@slate.com,Male,216.172.89.83,4/11/2019,87,4186.81 +661,Bevin,Pattullo,bpattulloic@yelp.com,Male,96.237.177.242,9/30/2019,9,3862.46 +662,Abbie,Melsom,amelsomid@businesswire.com,Female,226.28.218.124,8/28/2019,98,4520.86 +663,Hilarius,Weagener,hweagenerie@google.nl,Male,6.108.117.111,9/10/2019,80,4920.12 +664,Onida,Dewberry,odewberryif@chron.com,Female,95.189.17.251,7/12/2019,27,1206.75 +665,Sawyer,Behne,sbehneig@who.int,Male,232.40.190.187,6/26/2019,92,2730.93 +666,Ingaborg,Benoit,ibenoitih@diigo.com,Female,45.171.74.30,2/26/2019,79,3169.39 +667,Christyna,Beardow,cbeardowii@hibu.com,Female,212.112.66.197,8/15/2019,22,1664.25 +668,Chrysa,Bouda,cboudaij@posterous.com,Female,182.254.214.92,6/20/2019,58,4842.58 +669,Skell,Janny,sjannyik@statcounter.com,Male,117.115.79.57,8/25/2019,64,3147.82 +670,Willie,Gisbye,wgisbyeil@smh.com.au,Male,163.74.14.6,12/24/2018,60,2181.71 +671,Loydie,MacGiolla Pheadair,lmacgiollapheadairim@ftc.gov,Male,66.165.25.118,12/24/2018,17,4234.43 +672,Millisent,Warwick,mwarwickin@google.co.uk,Female,11.125.80.152,5/27/2019,57,2770.04 +673,Lutero,Campelli,lcampelliio@alibaba.com,Male,218.66.248.154,10/4/2019,29,4169.81 +674,Waverly,Kroch,wkrochip@google.co.uk,Male,117.138.8.158,10/6/2019,55,2248.55 +675,Jorge,Tipple,jtippleiq@google.cn,Male,75.32.226.30,1/5/2019,20,2316.81 +676,Rycca,Markham,rmarkhamir@posterous.com,Female,108.165.80.206,4/1/2019,37,3733.9 +677,Kendell,Tabourel,ktabourelis@ucsd.edu,Male,31.106.135.186,1/27/2019,41,2767.65 +678,Xymenes,Christofol,xchristofolit@slashdot.org,Male,205.98.142.54,4/17/2019,75,3761.36 +679,Willamina,Gluyas,wgluyasiu@patch.com,Female,208.141.12.123,9/10/2019,62,4055.56 +680,Missy,Chaudron,mchaudroniv@ox.ac.uk,Female,253.225.93.169,5/17/2019,54,3188.83 +681,Harrie,Levee,hleveeiw@dropbox.com,Female,104.60.96.6,9/4/2019,73,3779.89 +682,Nickie,Awdry,nawdryix@deviantart.com,Female,186.254.251.36,1/3/2019,76,2909.63 +683,Wash,Mapletoft,wmapletoftiy@baidu.com,Male,176.231.204.160,2/12/2019,3,1072.43 +684,Thea,Crankshaw,tcrankshawiz@bluehost.com,Female,240.218.215.50,12/5/2018,41,2503.95 +685,Bryna,Clemmen,bclemmenj0@slate.com,Female,117.149.65.242,6/24/2019,33,2635.47 +686,Fiann,Dipple,fdipplej1@usda.gov,Female,13.222.48.15,5/1/2019,97,4530.95 +687,Hilliary,Nowell,hnowellj2@jigsy.com,Female,237.64.236.63,9/8/2019,4,3931.64 +688,Francene,Perfitt,fperfittj3@upenn.edu,Female,35.17.66.62,3/5/2019,24,4353.24 +689,Adair,Foucher,afoucherj4@google.com,Male,184.228.99.56,5/7/2019,75,3261.23 +690,Evyn,Cristou,ecristouj5@xrea.com,Male,114.91.151.161,7/17/2019,95,3655.93 +691,Carleton,Mooreed,cmooreedj6@walmart.com,Male,17.30.55.141,8/31/2019,66,1486.95 +692,Ashley,Wallage,awallagej7@dyndns.org,Male,214.116.89.237,5/6/2019,17,3179.86 +693,Corney,Healeas,chealeasj8@phoca.cz,Male,36.170.221.28,12/24/2018,29,3068.73 +694,Marabel,Abbado,mabbadoj9@google.nl,Female,56.208.153.166,4/15/2019,4,4988.67 +695,Barr,Gregr,bgregrja@mozilla.com,Male,118.210.102.100,5/28/2019,31,2310.92 +696,Ulrika,Verni,uvernijb@hhs.gov,Female,252.208.223.205,3/13/2019,34,4254.15 +697,Carri,McWilliam,cmcwilliamjc@unblog.fr,Female,244.144.133.38,2/26/2019,57,4082.44 +698,Ciro,Burnet,cburnetjd@simplemachines.org,Male,56.117.3.236,11/7/2019,71,2943.82 +699,Eryn,Lindblom,elindblomje@paginegialle.it,Female,218.110.190.80,10/26/2019,98,3327.13 +700,Tandi,Jenney,tjenneyjf@sciencedirect.com,Female,61.111.94.217,8/9/2019,63,3161.01 +701,Karissa,McGeachy,kmcgeachyjg@themeforest.net,Female,97.151.85.120,3/15/2019,75,2793.19 +702,Daisie,Gosling,dgoslingjh@yale.edu,Female,80.132.185.177,12/24/2018,99,2564.95 +703,Web,Waters,wwatersji@icio.us,Male,138.42.236.158,11/26/2019,12,3133.25 +704,Beulah,Saltrese,bsaltresejj@rakuten.co.jp,Female,111.246.77.69,5/10/2019,85,1562.53 +705,Shea,Bansal,sbansaljk@timesonline.co.uk,Male,45.157.189.79,6/13/2019,64,4095.48 +706,Granger,Maddyson,gmaddysonjl@cdbaby.com,Male,4.174.208.225,10/22/2019,90,2312.31 +707,Frederic,Ferras,fferrasjm@europa.eu,Male,93.89.74.109,4/21/2019,1,2688.0 +708,Terri-jo,Blockwell,tblockwelljn@diigo.com,Female,139.206.39.244,10/28/2019,77,2032.65 +709,Portia,Messum,pmessumjo@cpanel.net,Female,205.8.231.154,7/5/2019,7,3703.36 +710,Fay,Bleakley,fbleakleyjp@fema.gov,Female,51.78.72.116,11/14/2019,97,4827.37 +711,Blayne,Marnane,bmarnanejq@nasa.gov,Male,254.10.160.105,11/17/2019,59,4393.22 +712,Brooke,Dreinan,bdreinanjr@yandex.ru,Male,101.190.49.170,7/13/2019,91,3896.42 +713,Dannie,Butten,dbuttenjs@w3.org,Male,254.183.249.58,7/3/2019,49,4087.49 +714,Niki,Gooch,ngoochjt@huffingtonpost.com,Male,14.126.59.16,2/13/2019,81,3541.85 +715,Penny,Bovis,pbovisju@macromedia.com,Female,194.73.1.154,9/15/2019,22,3343.77 +716,Agna,Wellings,awellingsjv@economist.com,Female,18.191.109.188,6/20/2019,59,3995.96 +717,Kelsi,Binestead,kbinesteadjw@weibo.com,Female,209.183.105.9,6/22/2019,10,2251.08 +718,Lucio,MacLennan,lmaclennanjx@oracle.com,Male,138.238.128.41,6/18/2019,76,4327.17 +719,Leonora,Shailer,lshailerjy@illinois.edu,Female,119.146.115.106,12/20/2018,29,4657.75 +720,Fonsie,O'Doogan,fodooganjz@cbsnews.com,Male,130.108.253.158,8/21/2019,94,1870.33 +721,Stearne,Claire,sclairek0@booking.com,Male,131.59.84.18,4/11/2019,98,3369.19 +722,Maye,Howis,mhowisk1@buzzfeed.com,Female,22.69.128.140,9/8/2019,85,1974.16 +723,Orton,Kiffe,okiffek2@github.com,Male,81.226.255.158,10/22/2019,52,3864.03 +724,Barty,Leadbeater,bleadbeaterk3@amazon.de,Male,208.66.100.248,4/24/2019,28,4214.56 +725,Sidnee,Coxhead,scoxheadk4@cam.ac.uk,Male,62.75.3.110,2/16/2019,8,4882.21 +726,Worden,Quan,wquank5@telegraph.co.uk,Male,86.238.192.197,3/13/2019,3,4136.64 +727,Brad,Marlor,bmarlork6@chron.com,Male,205.102.239.46,1/10/2019,27,3958.67 +728,Jefferson,Fanning,jfanningk7@google.de,Male,139.239.121.50,10/19/2019,26,3356.51 +729,North,Leidecker,nleideckerk8@nyu.edu,Male,240.19.0.156,4/1/2019,27,4866.45 +730,Chrissy,Rounsefull,crounsefullk9@topsy.com,Female,181.133.238.56,12/31/2018,19,1456.66 +731,Jennine,D'eathe,jdeatheka@ow.ly,Female,82.172.76.47,10/8/2019,87,1326.34 +732,Nedda,Hyland,nhylandkb@unesco.org,Female,85.174.186.120,10/9/2019,67,4967.34 +733,Ripley,Bavester,rbavesterkc@facebook.com,Male,200.70.120.11,7/30/2019,67,3364.31 +734,Amandi,Angrove,aangrovekd@hud.gov,Female,51.90.237.193,10/18/2019,98,3962.5 +735,Jandy,Fritchley,jfritchleyke@youtu.be,Female,236.202.35.177,11/17/2019,79,4641.47 +736,Kevin,Tozer,ktozerkf@godaddy.com,Male,92.185.81.191,9/20/2019,75,2652.18 +737,Erinna,Bucke,ebuckekg@hugedomains.com,Female,134.19.86.195,1/16/2019,19,4117.83 +738,Melania,Margach,mmargachkh@pagesperso-orange.fr,Female,57.86.172.205,1/22/2019,34,4369.1 +739,Darryl,Besset,dbessetki@canalblog.com,Female,150.168.219.97,10/15/2019,85,2449.44 +740,Urbano,Loy,uloykj@vistaprint.com,Male,192.95.90.230,1/29/2019,66,1697.74 +741,Shanan,Titcombe,stitcombekk@addtoany.com,Male,78.104.122.110,1/2/2019,31,2712.55 +742,North,Kropp,nkroppkl@huffingtonpost.com,Male,123.193.124.129,7/19/2019,32,2129.1 +743,Dario,Mennithorp,dmennithorpkm@nifty.com,Male,10.142.173.99,11/4/2019,64,2962.06 +744,Sally,Clerk,sclerkkn@youtu.be,Female,67.123.50.70,11/27/2019,32,4948.27 +745,Phillida,Grishkov,pgrishkovko@hatena.ne.jp,Female,105.59.57.150,5/19/2019,78,2513.36 +746,Warren,Gauthorpp,wgauthorppkp@dailymotion.com,Male,109.33.209.101,3/24/2019,74,3859.6 +747,Yurik,McArthur,ymcarthurkq@time.com,Male,177.53.197.246,6/4/2019,25,1927.47 +748,Lucine,Pritchett,lpritchettkr@adobe.com,Female,214.120.212.72,5/5/2019,44,3376.03 +749,Reeba,Facchini,rfacchiniks@fotki.com,Female,62.203.183.86,3/16/2019,29,2413.06 +750,Marys,MacGaughey,mmacgaugheykt@blogger.com,Female,241.223.207.143,7/28/2019,84,2694.2 +751,Abdul,Rollett,arollettku@intel.com,Male,224.183.67.157,5/16/2019,100,3327.64 +752,Lorry,Wolstenholme,lwolstenholmekv@facebook.com,Female,54.19.245.157,4/24/2019,43,4692.53 +753,Kore,Illingworth,killingworthkw@1und1.de,Female,4.3.169.154,11/2/2019,94,2378.36 +754,Mikel,Welbrock,mwelbrockkx@so-net.ne.jp,Male,94.86.96.2,5/16/2019,74,3938.38 +755,Debby,Largan,dlarganky@mozilla.org,Female,99.67.157.53,10/14/2019,62,2417.72 +756,Brana,Wabe,bwabekz@eepurl.com,Female,243.219.60.35,9/27/2019,36,3520.16 +757,Dyana,Dagworthy,ddagworthyl0@statcounter.com,Female,248.115.199.134,1/29/2019,10,1238.39 +758,Jobi,McAndie,jmcandiel1@alibaba.com,Female,76.175.25.123,9/8/2019,27,2305.18 +759,Karlene,Jery,kjeryl2@va.gov,Female,135.147.68.149,10/20/2019,58,1166.14 +760,Reinhold,Lepoidevin,rlepoidevinl3@baidu.com,Male,31.102.77.143,11/13/2019,22,2344.24 +761,Quent,Screen,qscreenl4@opera.com,Male,12.112.10.35,2/10/2019,53,4842.57 +762,Samuel,Lindberg,slindbergl5@github.io,Male,168.90.162.31,12/12/2018,89,1308.19 +763,Haleigh,Raubenheimer,hraubenheimerl6@quantcast.com,Female,105.212.55.253,1/14/2019,37,3535.98 +764,Shela,Croutear,scroutearl7@istockphoto.com,Female,91.117.195.180,2/8/2019,38,3265.27 +765,Kellyann,Capron,kcapronl8@example.com,Female,124.152.40.23,5/29/2019,50,4056.87 +766,Garey,enzley,genzleyl9@latimes.com,Male,90.152.83.190,9/28/2019,97,1906.66 +767,Mick,Schroter,mschroterla@google.fr,Male,7.60.95.15,5/31/2019,3,1395.4 +768,Alfy,Amdohr,aamdohrlb@google.com,Male,224.77.126.110,3/1/2019,50,4548.48 +769,Mirabelle,Staresmeare,mstaresmearelc@netvibes.com,Female,11.204.171.148,1/11/2019,55,4544.46 +770,Opal,Fincken,ofinckenld@paypal.com,Female,163.158.79.29,9/30/2019,16,4021.93 +771,Donielle,Philliphs,dphilliphsle@g.co,Female,141.55.105.178,7/20/2019,49,1131.35 +772,Stephie,Whitaker,swhitakerlf@skyrock.com,Female,225.117.77.187,3/27/2019,10,4871.35 +773,Elisa,Reddell,ereddelllg@cargocollective.com,Female,74.40.71.217,9/11/2019,9,4241.01 +774,Lars,Bunner,lbunnerlh@si.edu,Male,169.187.187.173,3/18/2019,30,4554.14 +775,Travers,Prinnett,tprinnettli@artisteer.com,Male,105.11.246.207,5/19/2019,60,1589.02 +776,Laurens,Held,lheldlj@delicious.com,Male,124.146.44.210,8/19/2019,96,2202.45 +777,Francesco,Florence,fflorencelk@army.mil,Male,220.136.205.22,7/26/2019,20,3108.6 +778,Lorenzo,Sandhill,lsandhillll@cbc.ca,Male,59.175.249.79,9/30/2019,75,1913.25 +779,Eddy,Duchart,educhartlm@blinklist.com,Male,45.9.255.250,9/3/2019,23,2272.21 +780,Melisse,Corpe,mcorpeln@biblegateway.com,Female,177.26.210.63,10/20/2019,52,4203.4 +781,Clementia,Raggett,craggettlo@wikispaces.com,Female,66.66.204.21,10/17/2019,35,3945.21 +782,Constantine,Spaducci,cspaduccilp@upenn.edu,Male,63.181.82.150,8/21/2019,46,2543.36 +783,Durant,Andreix,dandreixlq@desdev.cn,Male,196.76.252.7,9/20/2019,14,4613.73 +784,Dilan,Jesty,djestylr@nature.com,Male,121.131.244.90,3/19/2019,89,4665.71 +785,Geralda,Zotto,gzottols@samsung.com,Female,203.26.209.205,4/15/2019,76,2964.31 +786,Greg,Goodings,ggoodingslt@instagram.com,Male,64.38.244.127,9/12/2019,92,4711.1 +787,Bailey,Stuchberry,bstuchberrylu@indiatimes.com,Male,26.71.81.206,12/16/2018,30,1848.55 +788,Mata,Wilkowski,mwilkowskilv@google.co.uk,Male,155.242.58.56,11/26/2019,85,4335.11 +789,Hamnet,Phillpotts,hphillpottslw@paypal.com,Male,7.182.159.232,6/16/2019,71,1440.95 +790,Dimitry,Capeloff,dcapelofflx@pinterest.com,Male,109.227.224.121,7/10/2019,80,3282.33 +791,Rafaello,Coggles,rcogglesly@prweb.com,Male,111.224.235.85,12/10/2018,16,4340.53 +792,Judith,Witul,jwitullz@issuu.com,Female,175.239.78.176,3/2/2019,1,4375.79 +793,Addy,Symson,asymsonm0@xing.com,Female,21.208.246.221,2/11/2019,82,1009.31 +794,Joey,Troak,jtroakm1@hhs.gov,Male,134.87.130.194,1/28/2019,60,1454.05 +795,Goldi,Currell,gcurrellm2@xinhuanet.com,Female,93.106.48.19,10/16/2019,65,1228.04 +796,Joaquin,Teodori,jteodorim3@github.com,Male,240.116.96.102,7/31/2019,71,1091.62 +797,Rollo,Embling,remblingm4@un.org,Male,56.201.58.245,3/29/2019,20,2170.46 +798,Mina,Blondel,mblondelm5@weibo.com,Female,98.87.116.35,7/18/2019,58,1320.41 +799,Meridel,Aries,mariesm6@technorati.com,Female,142.95.106.58,11/15/2019,87,2705.6 +800,Bret,Perrelle,bperrellem7@360.cn,Male,118.67.219.42,6/16/2019,87,3500.22 +801,Augustine,Hamments,ahammentsm8@oracle.com,Male,55.61.38.103,3/26/2019,8,1526.07 +802,Billie,Roo,broom9@tuttocitta.it,Female,219.152.107.102,3/14/2019,97,3539.6 +803,Susann,Pargiter,spargiterma@umich.edu,Female,147.205.25.119,6/27/2019,22,1160.81 +804,Glennis,Penella,gpenellamb@posterous.com,Female,192.52.234.179,11/2/2019,36,2548.63 +805,Mozelle,Densumbe,mdensumbemc@phpbb.com,Female,72.69.188.204,7/10/2019,51,4208.3 +806,Bernarr,Congrave,bcongravemd@gizmodo.com,Male,54.147.238.56,6/24/2019,10,3573.77 +807,Humfrid,Glauber,hglauberme@adobe.com,Male,88.115.210.97,2/26/2019,18,1504.77 +808,Suzie,Awmack,sawmackmf@com.com,Female,25.246.10.228,9/22/2019,58,1734.63 +809,Tanitansy,McDill,tmcdillmg@apache.org,Female,176.212.121.232,9/10/2019,91,3442.09 +810,Astrid,O'Henery,aohenerymh@cnbc.com,Female,114.120.10.56,5/9/2019,8,3084.41 +811,Pepita,Estrella,pestrellami@1688.com,Female,32.233.56.52,11/12/2019,14,4239.8 +812,Cornie,Jennaway,cjennawaymj@amazon.de,Male,193.210.211.129,6/10/2019,19,3412.2 +813,Barbette,Rudgard,brudgardmk@myspace.com,Female,176.253.137.53,2/4/2019,1,1136.15 +814,Timofei,Donner,tdonnerml@ed.gov,Male,123.83.191.246,3/26/2019,1,1133.51 +815,Vasily,Jeary,vjearymm@nationalgeographic.com,Male,98.99.97.55,5/7/2019,92,1187.51 +816,Jamey,Windsor,jwindsormn@nih.gov,Male,196.125.95.56,6/29/2019,71,3115.9 +817,Evangelina,Gorrie,egorriemo@theglobeandmail.com,Female,35.130.99.82,1/14/2019,79,2252.03 +818,Odie,Titchmarsh,otitchmarshmp@sina.com.cn,Male,172.30.187.143,6/12/2019,63,3327.2 +819,Hestia,Fresson,hfressonmq@hp.com,Female,195.31.239.116,7/16/2019,39,3541.73 +820,Mortimer,Bestwerthick,mbestwerthickmr@microsoft.com,Male,217.124.4.132,10/24/2019,19,4855.38 +821,Chaunce,Hechlin,chechlinms@goo.ne.jp,Male,197.140.52.246,7/29/2019,1,2219.75 +822,Odella,Casson,ocassonmt@sciencedaily.com,Female,40.211.128.59,3/16/2019,93,3085.53 +823,Shellysheldon,Pallent,spallentmu@youtu.be,Male,153.196.220.186,2/19/2019,19,2695.0 +824,Emelyne,Childes,echildesmv@livejournal.com,Female,126.181.2.21,11/6/2019,13,1095.75 +825,Fred,Calf,fcalfmw@chicagotribune.com,Female,240.193.197.180,11/17/2019,79,3914.36 +826,Dag,Mulliss,dmullissmx@4shared.com,Male,238.137.20.149,8/6/2019,4,3539.27 +827,Georgeanna,Pitcock,gpitcockmy@comsenz.com,Female,102.167.151.225,10/12/2019,84,4585.02 +828,Sunny,Panther,spanthermz@unicef.org,Male,151.172.118.190,1/12/2019,91,4343.08 +829,Belita,Alderson,baldersonn0@blogtalkradio.com,Female,137.76.226.221,1/10/2019,42,3569.01 +830,Ame,Forte,aforten1@sourceforge.net,Female,147.157.177.133,1/8/2019,21,1365.56 +831,Timmie,Rodden,troddenn2@businessweek.com,Male,130.254.151.248,3/26/2019,96,1881.36 +832,Ole,Schafer,oschafern3@about.com,Male,127.222.178.16,3/17/2019,17,2401.6 +833,Patty,Cooling,pcoolingn4@seesaa.net,Female,61.215.62.193,2/11/2019,48,2474.66 +834,Collin,Stute,cstuten5@goo.ne.jp,Male,130.45.80.94,9/10/2019,4,3847.7 +835,Yorgo,Swancott,yswancottn6@intel.com,Male,219.62.66.145,2/25/2019,65,2423.66 +836,Ron,Routley,rroutleyn7@spotify.com,Male,56.197.234.81,8/23/2019,33,2148.92 +837,Stefania,McSkin,smcskinn8@dot.gov,Female,98.31.14.146,3/16/2019,62,1990.6 +838,Salem,Lergan,slergann9@usnews.com,Male,15.104.246.221,6/13/2019,38,2449.21 +839,Dunstan,Kenzie,dkenziena@networksolutions.com,Male,87.134.108.52,11/9/2019,44,4224.03 +840,Hunt,Ledamun,hledamunnb@yahoo.com,Male,176.159.246.215,3/10/2019,86,3822.13 +841,Farrand,Callam,fcallamnc@paypal.com,Female,226.107.7.0,8/27/2019,74,2378.51 +842,Nada,Catlin,ncatlinnd@furl.net,Female,47.100.217.78,9/6/2019,80,4344.19 +843,Carlita,Glavis,cglavisne@thetimes.co.uk,Female,51.186.12.78,12/12/2018,10,1226.08 +844,Lianna,McColm,lmccolmnf@smh.com.au,Female,11.59.43.151,12/24/2018,75,2920.03 +845,Darill,Klempke,dklempkeng@bloomberg.com,Male,130.161.53.81,10/30/2019,26,1470.72 +846,Laughton,Whithalgh,lwhithalghnh@booking.com,Male,245.52.162.163,5/13/2019,95,3958.01 +847,Cammie,Izhakov,cizhakovni@admin.ch,Female,124.164.33.27,4/7/2019,43,2040.2 +848,Marline,Duddell,mduddellnj@opensource.org,Female,170.97.255.200,6/14/2019,35,2449.64 +849,Hillier,Caulder,hcauldernk@npr.org,Male,83.220.85.148,8/30/2019,1,3069.76 +850,Coral,Raffles,crafflesnl@friendfeed.com,Female,149.92.2.58,1/20/2019,13,1301.57 +851,Barb,Dudny,bdudnynm@fc2.com,Female,243.249.246.96,2/19/2019,2,4053.62 +852,Arch,Bente,abentenn@mapy.cz,Male,229.210.219.135,9/9/2019,41,4556.35 +853,Alix,Sheriff,asheriffno@jigsy.com,Female,203.28.108.229,1/30/2019,70,2074.89 +854,Vaughn,Schwaiger,vschwaigernp@webmd.com,Male,112.58.20.107,5/25/2019,5,2348.46 +855,Stearn,Linggood,slinggoodnq@multiply.com,Male,247.71.219.146,11/23/2019,5,3348.25 +856,Doralin,Kleinschmidt,dkleinschmidtnr@ca.gov,Female,30.114.11.63,9/21/2019,29,3475.53 +857,Ali,Treweke,atrewekens@xrea.com,Female,181.217.116.253,1/19/2019,50,4184.66 +858,Brandon,Ogger,boggernt@example.com,Male,19.9.219.173,12/21/2018,53,3270.85 +859,Garth,Sterre,gsterrenu@sina.com.cn,Male,7.170.103.142,12/30/2018,60,4285.48 +860,Elberta,Stooke,estookenv@goo.gl,Female,104.12.213.185,4/7/2019,60,1570.75 +861,Winfield,Peacock,wpeacocknw@liveinternet.ru,Male,214.134.25.83,2/21/2019,76,4428.25 +862,Ernest,Allabarton,eallabartonnx@home.pl,Male,148.170.100.93,4/25/2019,54,2410.98 +863,Florette,Pendle,fpendleny@google.co.uk,Female,163.75.98.87,3/3/2019,25,1877.39 +864,Enrica,Smogur,esmogurnz@jiathis.com,Female,212.86.14.17,10/30/2019,90,1736.18 +865,Selle,Meece,smeeceo0@va.gov,Female,216.204.181.172,12/31/2018,9,4254.26 +866,Leighton,Behninck,lbehnincko1@ted.com,Male,128.137.145.34,7/28/2019,8,4817.49 +867,Dallis,Kildahl,dkildahlo2@ucsd.edu,Male,57.50.158.153,11/29/2019,80,3940.06 +868,Carr,McKeachie,cmckeachieo3@cbsnews.com,Male,155.169.255.5,10/25/2019,92,1248.52 +869,Gwendolin,Twinborough,gtwinborougho4@engadget.com,Female,126.69.215.135,10/31/2019,60,1790.54 +870,Katha,Mains,kmainso5@wired.com,Female,108.80.188.101,9/5/2019,90,1953.27 +871,Anabella,Hawkridge,ahawkridgeo6@elegantthemes.com,Female,194.91.70.106,11/7/2019,33,1067.92 +872,Carney,Moxon,cmoxono7@about.me,Male,243.35.248.83,5/25/2019,46,3541.93 +873,Cynthy,Minette,cminetteo8@unesco.org,Female,198.115.128.127,11/13/2019,17,1202.6 +874,Humfrey,Doogue,hdoogueo9@businesswire.com,Male,118.100.123.208,9/24/2019,21,1883.88 +875,Rosabella,Frowen,rfrowenoa@who.int,Female,168.21.241.150,3/26/2019,12,3747.05 +876,Inger,Benoiton,ibenoitonob@bing.com,Female,62.18.91.144,2/5/2019,55,3668.36 +877,Aldus,McHardy,amchardyoc@google.com,Male,217.43.238.237,2/7/2019,25,3733.76 +878,Thea,Klemke,tklemkeod@aboutads.info,Female,13.193.63.194,9/1/2019,15,2199.75 +879,Nathaniel,Alesio,nalesiooe@prweb.com,Male,45.77.38.94,8/21/2019,88,1398.05 +880,Jo-ann,Thring,jthringof@plala.or.jp,Female,67.45.206.82,3/28/2019,23,1119.69 +881,Betteann,Andreone,bandreoneog@google.it,Female,28.46.236.211,9/21/2019,68,2637.19 +882,Leland,Boulding,lbouldingoh@booking.com,Male,223.0.133.33,6/26/2019,95,1645.04 +883,Rosalia,Sieve,rsieveoi@opera.com,Female,146.244.116.76,11/14/2019,20,3684.84 +884,Pietra,Lillyman,plillymanoj@sphinn.com,Female,241.84.132.180,1/28/2019,100,4262.03 +885,Cosme,Guest,cguestok@chronoengine.com,Male,200.190.189.24,4/16/2019,70,2178.29 +886,Wallie,Telega,wtelegaol@ebay.com,Male,121.243.189.159,4/30/2019,52,1182.3 +887,Lishe,Skillett,lskillettom@umn.edu,Female,195.58.225.2,3/5/2019,71,2012.1 +888,Arleta,Manley,amanleyon@linkedin.com,Female,51.84.250.183,10/15/2019,15,4912.75 +889,Ravi,Willder,rwillderoo@clickbank.net,Male,172.59.196.252,11/3/2019,61,1209.86 +890,Averell,Towner,atownerop@twitpic.com,Male,39.252.161.23,5/12/2019,64,4531.62 +891,Patrice,Dedman,pdedmanoq@marriott.com,Male,210.154.94.25,6/13/2019,74,4735.98 +892,Shem,Girtin,sgirtinor@is.gd,Male,104.118.74.161,11/10/2019,100,4267.43 +893,Francyne,Bransby,fbransbyos@g.co,Female,44.30.38.123,2/4/2019,46,4181.62 +894,Christye,Farbrace,cfarbraceot@hibu.com,Female,112.150.190.191,2/16/2019,23,2095.36 +895,Nadean,Given,ngivenou@spiegel.de,Female,151.241.243.171,4/25/2019,81,2443.82 +896,Cesya,Clutheram,cclutheramov@smh.com.au,Female,165.200.216.167,7/4/2019,31,3937.84 +897,Bax,Goodwins,bgoodwinsow@nhs.uk,Male,11.52.15.206,4/9/2019,91,2252.61 +898,Lewiss,Lamblot,llamblotox@hubpages.com,Male,47.131.77.115,8/30/2019,24,2400.36 +899,Red,Wick,rwickoy@photobucket.com,Male,83.184.219.168,10/21/2019,15,4178.01 +900,Salvatore,Pimblett,spimblettoz@cmu.edu,Male,167.151.230.214,5/15/2019,42,1341.9 +901,Hughie,Bryceson,hbrycesonp0@princeton.edu,Male,196.234.12.37,6/21/2019,62,1336.58 +902,Clayson,Hankinson,chankinsonp1@over-blog.com,Male,23.197.178.40,8/12/2019,99,1615.05 +903,Lilas,Liccardo,lliccardop2@wisc.edu,Female,122.30.29.218,11/8/2019,18,2646.47 +904,Orville,Durrant,odurrantp3@clickbank.net,Male,72.40.168.148,8/12/2019,37,1473.73 +905,Orrin,Skelly,oskellyp4@a8.net,Male,105.220.135.4,5/6/2019,50,1525.97 +906,Tallou,Sandyford,tsandyfordp5@homestead.com,Female,227.225.220.181,2/6/2019,58,4786.43 +907,Quincy,Roseman,qrosemanp6@biglobe.ne.jp,Male,54.163.190.213,7/1/2019,24,4037.23 +908,Tabbie,Kiraly,tkiralyp7@newsvine.com,Male,224.190.131.109,3/13/2019,100,1323.77 +909,Sandy,Matskevich,smatskevichp8@simplemachines.org,Female,2.231.87.52,12/29/2018,82,3517.0 +910,Duane,Dye,ddyep9@nasa.gov,Male,104.68.110.228,10/11/2019,72,3764.03 +911,Vassily,McNeill,vmcneillpa@eepurl.com,Male,224.20.3.98,5/2/2019,93,2130.67 +912,Donalt,Astlet,dastletpb@chron.com,Male,114.210.201.171,5/7/2019,75,2025.04 +913,Nickey,Stobie,nstobiepc@icio.us,Male,185.166.110.244,4/16/2019,71,1045.07 +914,Alejandra,Evenett,aevenettpd@spotify.com,Female,53.71.229.172,3/31/2019,68,3272.6 +915,Malia,Turpey,mturpeype@slashdot.org,Female,100.204.158.195,7/24/2019,23,1887.0 +916,Sancho,Coy,scoypf@unicef.org,Male,172.2.26.180,7/3/2019,57,3139.02 +917,Gabey,Skoyles,gskoylespg@epa.gov,Female,194.148.9.101,3/13/2019,84,2292.4 +918,Bertrando,Ivankin,bivankinph@fda.gov,Male,118.157.82.229,9/22/2019,3,1287.51 +919,Tannie,MacCosty,tmaccostypi@digg.com,Male,72.202.202.95,12/24/2018,82,2588.07 +920,Ranee,Bessent,rbessentpj@imgur.com,Female,127.24.169.67,11/10/2019,54,3444.15 +921,Tiffanie,Trawin,ttrawinpk@marketwatch.com,Female,17.85.226.126,5/24/2019,65,2725.46 +922,Almeria,Gillan,agillanpl@geocities.com,Female,122.181.145.74,11/21/2019,9,4518.15 +923,Myron,Cornuau,mcornuaupm@fema.gov,Male,119.235.159.166,8/23/2019,71,2151.88 +924,Julie,Garmston,jgarmstonpn@istockphoto.com,Female,239.238.200.150,1/14/2019,64,2838.43 +925,Piggy,Gobel,pgobelpo@fastcompany.com,Male,118.158.61.149,2/6/2019,34,3108.47 +926,Gib,Featherstone,gfeatherstonepp@infoseek.co.jp,Male,90.82.112.24,7/12/2019,72,1446.28 +927,Ezri,Roome,eroomepq@bandcamp.com,Male,162.179.9.53,11/8/2019,89,4420.45 +928,Mattias,Bilbie,mbilbiepr@jugem.jp,Male,26.98.219.102,6/29/2019,53,3055.7 +929,Tremain,Puleston,tpulestonps@newsvine.com,Male,57.241.217.97,2/19/2019,74,4113.34 +930,Penrod,Human,phumanpt@amazon.co.jp,Male,191.23.222.227,8/12/2019,14,3914.95 +931,Helyn,Ladell,hladellpu@a8.net,Female,20.100.134.221,4/17/2019,94,2293.8 +932,Enid,Rough,eroughpv@imageshack.us,Female,107.222.42.7,6/17/2019,21,1914.06 +933,Eloise,Rumbold,erumboldpw@meetup.com,Female,43.79.13.253,12/16/2018,59,4639.52 +934,Sol,Merricks,smerrickspx@ow.ly,Male,123.39.195.179,11/1/2019,57,2899.49 +935,Mahmud,Keeney,mkeeneypy@wikispaces.com,Male,72.102.171.49,3/11/2019,6,4225.54 +936,Asia,Bartlosz,abartloszpz@mtv.com,Female,243.106.58.70,12/21/2018,69,1884.34 +937,Christian,Stirman,cstirmanq0@discuz.net,Female,107.80.178.46,8/29/2019,55,2956.9 +938,Alfredo,Adnams,aadnamsq1@washington.edu,Male,37.204.15.227,12/6/2018,15,2349.12 +939,Rudy,Diggins,rdigginsq2@nsw.gov.au,Male,136.105.142.55,10/22/2019,56,4720.66 +940,Valaria,Paulus,vpaulusq3@etsy.com,Female,109.100.3.110,1/2/2019,26,1610.79 +941,Conny,Roels,croelsq4@mozilla.com,Female,208.213.210.29,1/31/2019,54,4096.32 +942,Bucky,Kennan,bkennanq5@pinterest.com,Male,172.225.189.229,8/19/2019,87,4628.59 +943,Iago,Samwyse,isamwyseq6@github.io,Male,89.18.109.205,11/10/2019,60,4746.45 +944,Pietrek,Gori,pgoriq7@reverbnation.com,Male,92.121.178.154,11/5/2019,33,1692.9 +945,Tabina,Kears,tkearsq8@theglobeandmail.com,Female,144.45.170.150,10/3/2019,52,3611.17 +946,Wendye,Sollis,wsollisq9@walmart.com,Female,238.248.64.91,5/1/2019,5,3395.62 +947,Rafferty,Waycot,rwaycotqa@yellowbook.com,Male,56.12.230.243,10/22/2019,65,3819.8 +948,Bendix,Heamus,bheamusqb@adobe.com,Male,249.250.105.13,1/13/2019,1,3871.34 +949,Elfrieda,Gierardi,egierardiqc@uiuc.edu,Female,70.40.147.33,5/15/2019,33,3013.33 +950,Malachi,Sweet,msweetqd@soup.io,Male,45.89.78.169,12/12/2018,80,3668.82 +951,Lanae,Pollendine,lpollendineqe@nasa.gov,Female,95.61.177.183,2/13/2019,1,4889.44 +952,Ryley,Lawry,rlawryqf@scientificamerican.com,Male,125.229.131.211,1/14/2019,2,3286.34 +953,Terri-jo,Jamblin,tjamblinqg@epa.gov,Female,188.157.97.186,6/5/2019,47,2943.87 +954,Leland,Hildred,lhildredqh@scribd.com,Male,225.216.250.143,6/17/2019,56,4864.96 +955,Gabe,Olyff,golyffqi@nba.com,Male,132.213.9.245,4/23/2019,98,1702.76 +956,Madelena,Astling,mastlingqj@samsung.com,Female,30.58.29.118,12/18/2018,84,4673.11 +957,Reinaldo,Kitchingman,rkitchingmanqk@csmonitor.com,Male,188.111.118.234,5/23/2019,75,4450.61 +958,Padgett,Spacey,pspaceyql@google.de,Male,36.237.235.122,11/27/2019,56,1279.87 +959,Rachel,Chezelle,rchezelleqm@domainmarket.com,Female,96.79.189.100,11/24/2019,91,3136.13 +960,Pollyanna,Eggerton,peggertonqn@nsw.gov.au,Female,18.201.58.237,12/19/2018,20,3493.5 +961,Marris,Wincott,mwincottqo@bandcamp.com,Female,184.31.11.215,6/12/2019,8,1515.01 +962,Shae,Mair,smairqp@friendfeed.com,Male,62.3.75.126,5/26/2019,17,1886.62 +963,Rik,Citrine,rcitrineqq@ameblo.jp,Male,185.79.147.211,7/2/2019,34,4809.78 +964,Clare,Bohey,cboheyqr@wunderground.com,Female,107.176.103.42,5/11/2019,88,4309.81 +965,Hayes,Brushfield,hbrushfieldqs@i2i.jp,Male,2.5.51.42,11/26/2019,76,1551.99 +966,Misty,Domico,mdomicoqt@google.pl,Female,39.47.212.202,12/6/2018,70,4670.57 +967,Arthur,Elstub,aelstubqu@independent.co.uk,Male,176.248.255.191,12/30/2018,46,2705.52 +968,Berkeley,Hapgood,bhapgoodqv@technorati.com,Male,178.208.39.27,10/14/2019,22,3380.97 +969,Brose,Kynton,bkyntonqw@arstechnica.com,Male,46.20.210.218,3/5/2019,56,4943.64 +970,Tarah,Deguara,tdeguaraqx@netvibes.com,Female,218.157.37.87,10/15/2019,52,4735.44 +971,Lorie,Tooting,ltootingqy@e-recht24.de,Female,180.29.70.96,5/13/2019,2,3053.29 +972,Consuela,Lyndon,clyndonqz@nature.com,Female,33.99.22.111,10/26/2019,16,1782.96 +973,Heloise,Loges,hlogesr0@ebay.co.uk,Female,65.91.147.32,8/6/2019,91,3905.33 +974,Lamond,Hamnet,lhamnetr1@tripod.com,Male,59.5.218.194,3/21/2019,78,3783.64 +975,Matilda,Clemencet,mclemencetr2@livejournal.com,Female,50.223.87.130,10/4/2019,23,2543.68 +976,Bayard,Roskruge,broskruger3@sitemeter.com,Male,69.240.99.87,7/3/2019,80,1587.29 +977,Benn,Jirak,bjirakr4@delicious.com,Male,128.35.209.194,12/2/2018,44,3277.87 +978,Nahum,Attle,nattler5@tiny.cc,Male,112.43.114.140,6/8/2019,7,2273.81 +979,Evangeline,Ribey,eribeyr6@google.fr,Female,81.105.199.64,3/29/2019,67,4510.15 +980,Ryann,Ellul,rellulr7@ning.com,Female,240.217.190.216,12/13/2018,84,1668.81 +981,Ermina,Venn,evennr8@de.vu,Female,133.154.240.217,2/13/2019,97,1524.47 +982,Toby,Durward,tdurwardr9@state.tx.us,Male,121.122.162.100,6/18/2019,93,2462.12 +983,Frederic,Scraney,fscraneyra@gnu.org,Male,169.185.152.85,9/1/2019,5,4899.03 +984,Marty,Phebey,mphebeyrb@tripadvisor.com,Male,168.107.107.230,12/6/2018,84,4793.66 +985,Harris,Lockney,hlockneyrc@nifty.com,Male,93.129.49.110,8/4/2019,49,1798.63 +986,Eugen,Leverton,elevertonrd@instagram.com,Male,159.232.54.24,9/21/2019,60,3305.1 +987,Gabie,Beardwood,gbeardwoodre@theatlantic.com,Male,20.18.125.82,1/24/2019,5,1946.11 +988,Myrwyn,Pesterfield,mpesterfieldrf@scientificamerican.com,Male,145.133.177.91,8/13/2019,25,3808.27 +989,Mercy,Chadd,mchaddrg@51.la,Female,185.164.11.233,6/3/2019,40,4001.54 +990,Cornelle,Conford,cconfordrh@behance.net,Female,21.225.190.101,1/23/2019,37,3044.81 +991,Maynord,Till,mtillri@youtube.com,Male,152.132.239.240,7/13/2019,100,1128.62 +992,Evelin,Dmitrichenko,edmitrichenkorj@weibo.com,Male,209.57.214.249,7/17/2019,97,1964.24 +993,Quincy,Rupel,qrupelrk@github.io,Male,64.185.162.129,7/17/2019,98,3247.38 +994,Althea,Ternault,aternaultrl@hao123.com,Female,67.166.119.252,9/5/2019,71,1679.32 +995,Tracy,Usmar,tusmarrm@archive.org,Female,218.147.181.108,4/29/2019,13,4937.95 +996,Frank,Thebeaud,fthebeaudrn@phpbb.com,Male,80.246.35.156,3/21/2019,91,3229.53 +997,Kerk,Feathersby,kfeathersbyro@i2i.jp,Male,216.252.23.125,4/2/2019,38,2610.64 +998,Brigid,Worvell,bworvellrp@cnn.com,Female,48.50.178.158,5/19/2019,35,1974.9 +999,Ike,Van der Merwe,ivandermerwerq@craigslist.org,Male,103.61.116.181,8/14/2019,56,2115.14 +1000,Mark,Dallison,mdallisonrr@cdc.gov,Male,187.245.203.40,8/8/2019,73,1286.22 diff --git a/scripts/MOCK_DATA_out.csv b/scripts/MOCK_DATA_out.csv new file mode 100644 index 0000000..613a3d3 --- /dev/null +++ b/scripts/MOCK_DATA_out.csv @@ -0,0 +1,1001 @@ +id,first_name,last_name,email,gender,ip_address,date,parts,unit cost +1,Lurette,Rounsefull,lrounsefull0@cbsnews.com,Female,208.52.245.134,4/11/2019,1,1425.64 +2,Peder,Heineking,pheineking1@mac.com,Male,216.60.184.211,5/15/2019,79,2666.57 +3,Haydon,Birdsall,hbirdsall2@spotify.com,Male,161.74.94.92,10/18/2019,27,2377.64 +4,Barbe,Mouse,bmouse3@elegantthemes.com,Female,97.202.107.43,5/16/2019,20,3375.08 +5,Sherwin,Wealthall,swealthall4@whitehouse.gov,Male,155.14.42.127,3/5/2019,77,4147.29 +6,Ase,Pringle,apringle5@sfgate.com,Male,147.34.186.244,10/19/2019,33,4551.61 +7,Sibylla,Weiss,sweiss6@icq.com,Female,196.247.130.201,3/7/2019,21,2154.9 +8,Richy,Dougal,rdougal7@yelp.com,Male,91.176.172.223,12/27/2018,97,3403.75 +9,Kelsey,Ludlamme,kludlamme8@cyberchimps.com,Male,121.112.184.219,5/2/2019,81,4930.75 +10,Fae,Driffe,fdriffe9@friendfeed.com,Female,219.51.51.253,4/7/2019,57,3100.78 +11,Dannye,Sleit,dsleita@independent.co.uk,Female,160.208.93.210,10/4/2019,88,4401.37 +12,Kessia,Gilkes,kgilkesb@w3.org,Female,30.178.57.187,8/26/2019,82,2369.86 +13,Rickie,Persich,rpersichc@mapquest.com,Male,50.235.20.153,9/13/2019,94,4557.41 +14,Margaretta,Shearn,mshearnd@buzzfeed.com,Female,172.102.242.250,3/14/2019,72,2948.22 +15,Malcolm,Winnard,mwinnarde@yale.edu,Male,185.241.158.149,5/15/2019,82,4723.48 +16,Lazarus,Summerlie,lsummerlief@comsenz.com,Male,111.172.107.99,11/8/2019,89,2845.05 +17,Bord,Cato,bcatog@edublogs.org,Male,177.53.70.102,4/2/2019,32,4933.24 +18,Weber,Guslon,wguslonh@go.com,Male,115.2.137.252,12/10/2018,94,4348.69 +19,Stearne,MacPadene,smacpadenei@pbs.org,Male,35.83.181.137,8/4/2019,82,4950.67 +20,Bekki,Remirez,bremirezj@arstechnica.com,Female,12.111.119.238,2/17/2019,62,1510.53 +21,Desiri,Gaskins,dgaskinsk@prlog.org,Female,43.30.140.105,9/8/2019,64,4641.51 +22,Mata,Beaten,mbeatenl@constantcontact.com,Male,13.90.195.102,4/6/2019,84,1942.72 +23,Mattias,Bullas,mbullasm@fastcompany.com,Male,37.242.45.155,7/21/2019,95,3321.2 +24,Sloan,Graveson,sgravesonn@mozilla.com,Male,124.94.155.193,9/28/2019,7,1786.92 +25,Isak,Juste,ijusteo@google.ru,Male,249.233.28.164,8/18/2019,34,1682.19 +26,Teri,Thomann,tthomannp@wp.com,Female,168.237.145.237,11/16/2019,18,4480.64 +27,Rhoda,Gotcher,rgotcherq@businessinsider.com,Female,23.141.180.40,4/1/2019,19,4514.47 +28,Helen-elizabeth,McEniry,hmceniryr@oakley.com,Female,43.143.23.204,12/14/2018,40,4146.98 +29,Aguie,Brecknall,abrecknalls@nifty.com,Male,69.49.176.116,1/19/2019,96,4720.87 +30,Wildon,Keenlyside,wkeenlysidet@blogtalkradio.com,Male,245.109.45.71,3/26/2019,22,1835.57 +31,Ynez,Lots,ylotsu@abc.net.au,Female,172.185.2.14,7/17/2019,68,4872.13 +32,Norman,Worgen,nworgenv@constantcontact.com,Male,143.221.2.185,3/17/2019,29,4768.51 +33,Ker,Bown,kbownw@bandcamp.com,Male,30.5.53.61,6/14/2019,22,3091.25 +34,Ivy,Nibloe,inibloex@noaa.gov,Female,121.242.246.184,7/31/2019,15,4048.07 +35,Coop,Waiting,cwaitingy@prlog.org,Male,215.155.212.35,1/31/2019,59,1388.34 +36,Philipa,Bigglestone,pbigglestonez@networkadvertising.org,Female,235.113.203.193,1/11/2019,49,2571.05 +37,Konstantine,Reavell,kreavell10@pcworld.com,Male,212.115.157.70,1/15/2019,32,1521.5 +38,Neddie,Peckham,npeckham11@shareasale.com,Male,165.199.0.54,11/10/2019,4,4159.67 +39,Veronique,Crocetti,vcrocetti12@bravesites.com,Female,155.94.241.75,11/27/2019,70,1682.83 +40,Stanford,Folk,sfolk13@walmart.com,Male,115.78.90.21,8/20/2019,21,1049.63 +41,Tamera,Bacchus,tbacchus14@techcrunch.com,Female,125.75.125.6,2/3/2019,44,3804.48 +42,Andromache,Hakonsson,ahakonsson15@ft.com,Female,216.82.36.186,8/3/2019,38,3643.39 +43,Elnora,Amos,eamos16@domainmarket.com,Female,233.33.105.19,11/12/2019,97,1234.57 +44,Andrus,Hegge,ahegge17@arstechnica.com,Male,48.49.214.246,9/21/2019,79,4728.49 +45,Grady,Rodell,grodell18@ox.ac.uk,Male,69.64.12.243,4/22/2019,64,3514.69 +46,Maryann,Shaddick,mshaddick19@boston.com,Female,145.192.46.176,8/7/2019,43,1944.22 +47,Gaelan,Kirtley,gkirtley1a@topsy.com,Male,145.162.10.133,9/14/2019,39,2598.76 +48,Denver,Kennicott,dkennicott1b@amazon.com,Male,179.116.133.53,12/18/2018,10,2243.1 +49,Bordy,Saw,bsaw1c@cmu.edu,Male,6.22.237.36,1/27/2019,80,2196.95 +50,Gwenette,Iston,giston1d@multiply.com,Female,211.116.16.113,3/29/2019,34,2230.26 +51,Hedvig,Simmers,hsimmers1e@uol.com.br,Female,155.181.252.255,8/23/2019,7,3711.71 +52,Jannel,Sarre,jsarre1f@tuttocitta.it,Female,127.187.194.109,6/4/2019,82,2641.84 +53,Rusty,Saffill,rsaffill1g@mozilla.com,Male,152.175.79.114,3/24/2019,63,4482.27 +54,Shelden,Brandin,sbrandin1h@jugem.jp,Male,218.156.254.18,9/29/2019,12,1487.3 +55,Rusty,Dumbare,rdumbare1i@purevolume.com,Male,49.252.90.82,3/2/2019,26,4880.92 +56,Agace,Davidovitz,adavidovitz1j@mtv.com,Female,187.52.194.143,4/21/2019,59,4803.16 +57,Ross,McGarva,rmcgarva1k@upenn.edu,Male,41.191.56.78,8/23/2019,97,4860.07 +58,Kayne,Maruszewski,kmaruszewski1l@stanford.edu,Male,41.228.104.199,7/18/2019,23,1358.8 +59,Stavros,Comar,scomar1m@google.ru,Male,5.91.47.215,6/20/2019,66,1423.4 +60,Ruperto,Proudlock,rproudlock1n@hostgator.com,Male,105.156.116.198,1/20/2019,28,4631.21 +61,Milka,Marvel,mmarvel1o@devhub.com,Female,9.251.162.16,5/26/2019,80,3446.13 +62,Zared,Pallesen,zpallesen1p@ted.com,Male,246.159.163.241,6/13/2019,25,2949.72 +63,Sharona,Muscat,smuscat1q@youtube.com,Female,192.207.68.239,1/17/2019,53,4522.67 +64,Annabella,Persitt,apersitt1r@china.com.cn,Female,5.217.19.64,7/26/2019,52,2113.55 +65,Tawsha,Constantine,tconstantine1s@4shared.com,Female,6.89.103.116,6/15/2019,38,2727.92 +66,Leonard,Pavlasek,lpavlasek1t@theglobeandmail.com,Male,42.64.175.217,2/16/2019,85,2694.45 +67,Kalvin,Caudle,kcaudle1u@baidu.com,Male,193.11.181.80,4/23/2019,18,2549.05 +68,Jessica,Cumbridge,jcumbridge1v@hugedomains.com,Female,238.81.101.205,12/18/2018,80,2877.37 +69,Iorgo,Hampshaw,ihampshaw1w@youtu.be,Male,35.99.25.88,4/15/2019,100,4485.79 +70,Sherlocke,Kerswell,skerswell1x@zdnet.com,Male,201.53.40.240,9/5/2019,6,4147.46 +71,Rikki,Blodg,rblodg1y@admin.ch,Male,233.254.145.162,9/20/2019,41,4950.16 +72,Leoine,MacInnes,lmacinnes1z@joomla.org,Female,76.138.109.238,7/23/2019,45,3870.58 +73,Leicester,O'Haire,lohaire20@nba.com,Male,51.179.242.230,8/7/2019,23,2243.91 +74,Bennett,Jankowski,bjankowski21@jugem.jp,Male,248.133.185.229,6/4/2019,67,2448.41 +75,Josias,Wardingley,jwardingley22@bigcartel.com,Male,128.111.46.189,8/22/2019,2,4954.97 +76,Farrand,Edon,fedon23@networkadvertising.org,Female,181.161.32.177,3/30/2019,95,3728.89 +77,Tirrell,Daugherty,tdaugherty24@miitbeian.gov.cn,Male,202.140.213.72,11/9/2019,36,3436.25 +78,Celeste,Odby,codby25@answers.com,Female,152.36.106.124,6/16/2019,12,1327.35 +79,Jory,Roomes,jroomes26@usa.gov,Male,124.197.56.246,7/2/2019,62,3910.3 +80,Saudra,Woodham,swoodham27@smh.com.au,Female,109.122.138.13,4/17/2019,22,3553.65 +81,Renaldo,Calow,rcalow28@cloudflare.com,Male,108.38.150.172,12/5/2018,98,3790.92 +82,Chrissie,Wynch,cwynch29@de.vu,Female,113.58.215.184,5/25/2019,54,3925.41 +83,Boris,Ilyas,bilyas2a@webeden.co.uk,Male,98.65.148.122,1/26/2019,4,2722.03 +84,Zach,McReynolds,zmcreynolds2b@about.com,Male,169.251.18.46,3/25/2019,33,2238.47 +85,Pip,Puttan,pputtan2c@multiply.com,Male,6.160.216.170,10/23/2019,20,1502.59 +86,Lenore,Shafto,lshafto2d@globo.com,Female,144.169.11.70,1/17/2019,5,2162.94 +87,Maggie,Dryden,mdryden2e@cpanel.net,Female,203.155.129.86,3/12/2019,76,3017.41 +88,Madeleine,Alflatt,malflatt2f@spotify.com,Female,38.43.105.230,6/3/2019,70,4950.07 +89,Jojo,Niblock,jniblock2g@usgs.gov,Female,241.182.83.206,6/20/2019,98,3224.06 +90,Chan,Lebbern,clebbern2h@about.me,Male,129.233.179.181,5/31/2019,14,3407.91 +91,Juanita,O'Neil,joneil2i@sina.com.cn,Female,196.212.243.128,4/21/2019,4,1238.45 +92,Albina,Nestoruk,anestoruk2j@rakuten.co.jp,Female,173.60.28.232,11/25/2019,2,3850.73 +93,Gerrard,Lowrance,glowrance2k@huffingtonpost.com,Male,65.158.65.49,5/30/2019,56,2495.36 +94,Enrique,Borrel,eborrel2l@slashdot.org,Male,234.74.144.119,11/11/2019,89,1230.11 +95,Sheppard,Millward,smillward2m@plala.or.jp,Male,30.207.143.30,9/13/2019,14,4656.55 +96,Karna,Zisneros,kzisneros2n@comcast.net,Female,207.85.89.67,4/30/2019,40,1087.58 +97,Mohandas,Abbitt,mabbitt2o@cocolog-nifty.com,Male,157.202.50.94,2/2/2019,18,4141.99 +98,Alena,McVee,amcvee2p@so-net.ne.jp,Female,35.120.251.203,9/28/2019,15,4863.32 +99,Lanita,Sommerlin,lsommerlin2q@mozilla.com,Female,63.103.18.73,2/17/2019,69,4424.01 +100,Celine,Huddlestone,chuddlestone2r@ask.com,Female,207.219.182.50,6/24/2019,92,4185.16 +101,Jan,Vorley,jvorley2s@bbc.co.uk,Male,24.122.66.16,10/7/2019,88,4856.43 +102,Gordon,Burness,gburness2t@wired.com,Male,251.54.167.76,7/28/2019,77,1249.9 +103,Doris,Dey,ddey2u@youtube.com,Female,105.167.122.156,5/18/2019,95,2541.59 +104,Colby,Ashurst,cashurst2v@wix.com,Male,93.195.66.138,9/14/2019,49,3987.84 +105,Phyllida,Brookson,pbrookson2w@apple.com,Female,15.169.21.114,4/8/2019,10,4565.43 +106,Jeramie,Crasswell,jcrasswell2x@posterous.com,Male,205.238.4.171,7/19/2019,10,1042.23 +107,Lynsey,Plum,lplum2y@blogtalkradio.com,Female,184.41.245.159,11/15/2019,27,1913.04 +108,Dorella,Dulanty,ddulanty2z@discuz.net,Female,29.143.190.208,3/15/2019,71,1939.76 +109,Ruthi,Rishworth,rrishworth30@liveinternet.ru,Female,107.240.252.87,11/14/2019,25,4378.32 +110,Petrina,Featherstonehaugh,pfeatherstonehaugh31@umich.edu,Female,139.60.87.163,5/3/2019,86,2273.95 +111,Karla,Dechelle,kdechelle32@google.cn,Female,147.220.216.41,11/22/2019,79,2940.0 +112,Joshia,Fishlock,jfishlock33@booking.com,Male,158.103.241.179,7/8/2019,68,3644.03 +113,Junia,Maryman,jmaryman34@jigsy.com,Female,216.105.242.205,5/15/2019,97,2466.85 +114,Mabelle,Warmisham,mwarmisham35@mysql.com,Female,246.108.73.60,2/5/2019,43,1904.03 +115,Phaidra,Fleet,pfleet36@1und1.de,Female,83.157.253.208,2/13/2019,21,1765.81 +116,Flo,Mardlin,fmardlin37@unicef.org,Female,31.252.115.185,12/11/2018,14,3875.57 +117,Crin,Aspinall,caspinall38@pen.io,Female,152.151.189.190,9/21/2019,76,3615.12 +118,Lorrie,Carnaman,lcarnaman39@sina.com.cn,Male,182.12.111.81,2/1/2019,66,1537.87 +119,Barnabas,Denslow,bdenslow3a@dell.com,Male,109.193.202.177,10/17/2019,71,1352.18 +120,Bennett,Saffen,bsaffen3b@cloudflare.com,Male,180.151.92.220,3/8/2019,19,3352.34 +121,Jonell,Menauteau,jmenauteau3c@yellowbook.com,Female,49.115.246.26,9/25/2019,26,4823.26 +122,Alicia,Jakubovicz,ajakubovicz3d@nps.gov,Female,239.158.188.213,3/27/2019,32,1051.47 +123,Nikolai,Raselles,nraselles3e@histats.com,Male,94.104.99.187,5/13/2019,43,1043.08 +124,Nikolaos,Loder,nloder3f@google.it,Male,19.118.58.253,3/17/2019,73,1254.57 +125,Sheffield,Muncey,smuncey3g@elpais.com,Male,77.244.5.146,7/28/2019,67,1704.55 +126,Marni,Cowin,mcowin3h@google.com.br,Female,61.232.91.222,7/8/2019,50,1367.66 +127,Storm,Kennedy,skennedy3i@wikispaces.com,Female,191.12.223.90,7/31/2019,77,3871.16 +128,Trent,Gutierrez,tgutierrez3j@sourceforge.net,Male,113.242.15.190,8/10/2019,81,4352.61 +129,Dodi,Tiller,dtiller3k@dailymail.co.uk,Female,0.240.169.55,11/21/2019,35,4607.1 +130,Vivie,Sture,vsture3l@yelp.com,Female,46.11.195.152,10/27/2019,82,2166.72 +131,Nerissa,Giddins,ngiddins3m@whitehouse.gov,Female,25.100.68.103,3/11/2019,81,4326.19 +132,Emalee,Huckerby,ehuckerby3n@europa.eu,Female,249.239.20.180,2/14/2019,15,1607.1 +133,Cesare,Deave,cdeave3o@google.es,Male,215.226.177.3,12/16/2018,64,3602.38 +134,Britni,Neagle,bneagle3p@is.gd,Female,238.87.102.252,5/1/2019,95,3254.36 +135,Andrej,Gandrich,agandrich3q@addtoany.com,Male,122.104.65.90,8/14/2019,41,3220.23 +136,Cody,Worssam,cworssam3r@chronoengine.com,Male,221.63.128.0,11/11/2019,7,4570.98 +137,Mord,Bradford,mbradford3s@utexas.edu,Male,168.172.15.29,2/8/2019,52,2842.69 +138,Madeline,Golledge,mgolledge3t@japanpost.jp,Female,180.54.218.245,8/1/2019,31,2426.19 +139,Clarie,Tunn,ctunn3u@sun.com,Female,170.49.242.95,3/30/2019,6,2375.77 +140,Seana,Gatherell,sgatherell3v@myspace.com,Female,229.46.37.246,7/1/2019,83,3626.95 +141,Veronica,Fielder,vfielder3w@hugedomains.com,Female,211.136.81.248,10/5/2019,76,4578.69 +142,Kris,Searby,ksearby3x@constantcontact.com,Male,75.40.234.246,5/14/2019,79,3491.43 +143,Dorita,Henze,dhenze3y@mozilla.com,Female,19.91.34.111,3/5/2019,23,3869.59 +144,Ada,Sydall,asydall3z@netlog.com,Female,63.122.73.224,9/28/2019,5,3118.74 +145,Josias,Vanichkov,jvanichkov40@amazon.co.jp,Male,111.18.27.20,3/11/2019,82,1807.86 +146,Payton,Chesney,pchesney41@forbes.com,Male,253.128.22.165,6/1/2019,93,1101.65 +147,Arabele,Marcome,amarcome42@bbb.org,Female,25.78.95.237,8/25/2019,20,3268.49 +148,Dick,Egre,degre43@discovery.com,Male,136.113.116.45,3/3/2019,35,3221.24 +149,Charleen,Eckh,ceckh44@shareasale.com,Female,251.215.112.50,6/30/2019,47,3524.18 +150,Minne,Lawfull,mlawfull45@shinystat.com,Female,15.159.68.230,7/31/2019,56,3915.18 +151,Timi,Pelchat,tpelchat46@issuu.com,Female,218.157.149.25,11/17/2019,53,1224.92 +152,Gabrila,Goosnell,ggoosnell47@hugedomains.com,Female,79.133.249.98,12/28/2018,30,4687.86 +153,Casie,Brimm,cbrimm48@4shared.com,Female,252.229.161.4,2/15/2019,98,1455.37 +154,Leeland,Dewi,ldewi49@multiply.com,Male,81.68.121.138,3/25/2019,63,4518.04 +155,Pascal,Garred,pgarred4a@facebook.com,Male,223.70.91.166,5/3/2019,71,2996.67 +156,Lazaro,Manktelow,lmanktelow4b@wikia.com,Male,185.80.62.190,7/21/2019,82,1094.16 +157,Sandi,Montgomery,smontgomery4c@java.com,Female,190.222.21.13,11/17/2019,21,2832.31 +158,Valentine,Jimes,vjimes4d@about.com,Female,74.198.243.183,5/1/2019,53,1644.56 +159,Erika,Ferraresi,eferraresi4e@google.fr,Female,54.193.54.25,5/19/2019,21,4436.52 +160,Nerta,Zaczek,nzaczek4f@spotify.com,Female,108.17.93.214,2/22/2019,81,2156.82 +161,Lucita,Harte,lharte4g@nasa.gov,Female,193.242.177.185,12/21/2018,64,3544.36 +162,Jedidiah,Danielsohn,jdanielsohn4h@accuweather.com,Male,245.98.253.52,9/16/2019,1,2115.11 +163,Lorettalorna,Quarrie,lquarrie4i@fc2.com,Female,76.173.212.93,6/13/2019,4,1466.75 +164,Rich,Tryhorn,rtryhorn4j@moonfruit.com,Male,143.167.201.182,10/7/2019,1,1661.04 +165,Gardie,Karlqvist,gkarlqvist4k@so-net.ne.jp,Male,249.11.192.35,8/12/2019,6,3746.95 +166,Xylia,Penna,xpenna4l@dailymotion.com,Female,180.58.51.110,11/28/2019,61,3441.57 +167,Axel,Renackowna,arenackowna4m@tripod.com,Male,146.67.140.217,3/7/2019,78,3550.93 +168,Chen,Fulton,cfulton4n@columbia.edu,Male,146.169.34.152,4/2/2019,74,2383.26 +169,Alon,Cufley,acufley4o@photobucket.com,Male,125.231.215.140,4/16/2019,65,1732.78 +170,Koo,Gumm,kgumm4p@php.net,Female,243.153.77.38,3/21/2019,48,3721.4 +171,Korey,Weatherdon,kweatherdon4q@blogger.com,Male,240.168.248.218,3/12/2019,77,3795.59 +172,Lena,Stannus,lstannus4r@lulu.com,Female,109.85.91.216,12/5/2018,46,2634.33 +173,Elijah,Westney,ewestney4s@noaa.gov,Male,158.146.220.98,8/7/2019,27,4530.75 +174,Eachelle,Schiefersten,eschiefersten4t@symantec.com,Female,54.128.102.166,9/15/2019,87,2940.88 +175,Georgeanne,Gotthard.sf,ggotthardsf4u@geocities.com,Female,86.43.67.105,1/9/2019,37,2319.01 +176,Frazier,Feldbrin,ffeldbrin4v@joomla.org,Male,184.234.191.22,10/14/2019,23,4596.74 +177,Bancroft,Alvarez,balvarez4w@cisco.com,Male,226.3.76.213,7/28/2019,21,3413.13 +178,Ricky,Lyles,rlyles4x@abc.net.au,Male,156.11.73.17,11/28/2019,6,1870.07 +179,Lorettalorna,Bomfield,lbomfield4y@virginia.edu,Female,37.210.36.178,6/20/2019,66,1127.34 +180,Catrina,Ruse,cruse4z@exblog.jp,Female,81.216.141.90,12/5/2018,34,2485.16 +181,Loleta,Pawels,lpawels50@merriam-webster.com,Female,176.42.193.141,6/10/2019,84,4221.89 +182,Hedvige,Commings,hcommings51@domainmarket.com,Female,138.3.249.198,1/29/2019,8,3910.06 +183,Dmitri,McCrory,dmccrory52@mit.edu,Male,135.37.103.219,7/28/2019,64,2804.83 +184,Hendrika,Shawyer,hshawyer53@xinhuanet.com,Female,16.30.34.147,2/20/2019,65,1706.29 +185,Lorilee,Dunlop,ldunlop54@ed.gov,Female,111.241.90.104,5/10/2019,75,4965.63 +186,Jerad,Faunch,jfaunch55@blogtalkradio.com,Male,139.35.31.167,3/18/2019,93,4404.83 +187,Gualterio,Eary,geary56@illinois.edu,Male,59.170.22.192,10/2/2019,49,3569.07 +188,Barby,Jikylls,bjikylls57@deviantart.com,Female,22.44.213.16,4/7/2019,75,4908.44 +189,Jabez,Mathiot,jmathiot58@w3.org,Male,93.194.63.80,5/5/2019,64,2744.04 +190,Worthy,Edland,wedland59@barnesandnoble.com,Male,4.66.208.56,11/12/2019,51,4272.28 +191,Doralyn,Braunstein,dbraunstein5a@youtube.com,Female,68.179.11.250,3/1/2019,37,3713.51 +192,Mallory,Eyres,meyres5b@google.com.hk,Female,222.214.3.63,7/22/2019,57,3522.06 +193,Louie,Dunkley,ldunkley5c@cdc.gov,Male,154.18.175.128,7/10/2019,51,2901.79 +194,Elbert,Kenvin,ekenvin5d@ftc.gov,Male,206.116.235.21,2/23/2019,75,4198.55 +195,Sean,Hankin,shankin5e@virginia.edu,Female,32.170.127.155,7/7/2019,76,4284.24 +196,Tamiko,O'Fogarty,tofogarty5f@comsenz.com,Female,249.124.65.230,2/27/2019,97,1816.59 +197,Dana,Caress,dcaress5g@bandcamp.com,Female,63.163.104.0,6/11/2019,70,4546.28 +198,Starla,Bicksteth,sbicksteth5h@deviantart.com,Female,93.41.109.63,3/6/2019,75,1536.56 +199,Olly,Chalmers,ochalmers5i@nature.com,Male,206.17.239.226,6/1/2019,99,2617.06 +200,Ermengarde,Laible,elaible5j@alibaba.com,Female,232.255.191.98,4/14/2019,7,2122.32 +201,Melloney,Stalf,mstalf5k@constantcontact.com,Female,254.159.238.133,3/11/2019,17,1829.95 +202,Aggy,Sandry,asandry5l@chicagotribune.com,Female,88.162.78.17,4/23/2019,82,1228.42 +203,Tuesday,Brunning,tbrunning5m@microsoft.com,Female,24.185.56.166,12/2/2018,25,2420.45 +204,Graehme,Marconi,gmarconi5n@forbes.com,Male,134.166.130.121,1/15/2019,29,3650.99 +205,Susi,Casillas,scasillas5o@jimdo.com,Female,130.75.119.191,4/6/2019,26,3164.59 +206,Bunny,Bollands,bbollands5p@privacy.gov.au,Female,73.155.139.92,7/20/2019,94,1459.89 +207,Corrine,Bythway,cbythway5q@ucsd.edu,Female,152.20.150.152,12/30/2018,74,1997.35 +208,Dannye,Adshede,dadshede5r@netlog.com,Female,210.46.12.79,8/9/2019,28,2915.74 +209,Gerald,Waiton,gwaiton5s@1und1.de,Male,47.243.251.9,5/18/2019,66,3698.92 +210,Belia,Hatherley,bhatherley5t@dot.gov,Female,3.253.140.62,10/2/2019,54,1089.69 +211,Terrell,Witherup,twitherup5u@ibm.com,Male,125.43.97.27,5/20/2019,97,3331.73 +212,Vic,Kliner,vkliner5v@cargocollective.com,Male,91.117.26.173,1/22/2019,12,1020.32 +213,Mayor,Drover,mdrover5w@comsenz.com,Male,165.45.244.175,9/18/2019,83,1282.01 +214,Didi,Kanwell,dkanwell5x@mysql.com,Female,249.71.28.189,1/25/2019,39,3401.56 +215,Lothario,Heyworth,lheyworth5y@patch.com,Male,204.254.193.144,6/27/2019,14,2392.22 +216,Adi,Bertome,abertome5z@washingtonpost.com,Female,247.98.53.39,3/19/2019,67,1188.17 +217,Zebadiah,Kryzhov,zkryzhov60@columbia.edu,Male,126.42.169.163,11/28/2019,85,2177.47 +218,Putnam,Wallworke,pwallworke61@sourceforge.net,Male,54.55.143.193,4/5/2019,72,4454.14 +219,Amery,Hitchens,ahitchens62@harvard.edu,Male,48.224.217.2,6/13/2019,64,3615.62 +220,Joana,Davitti,jdavitti63@ebay.co.uk,Female,62.165.254.131,3/4/2019,80,3629.81 +221,Kathleen,Tuer,ktuer64@biblegateway.com,Female,5.70.130.77,3/11/2019,13,1608.46 +222,Renaldo,Campbell-Dunlop,rcampbelldunlop65@skyrock.com,Male,26.131.233.63,12/29/2018,50,3202.62 +223,Titus,Ault,tault66@elpais.com,Male,165.158.147.201,7/2/2019,84,4835.57 +224,Gamaliel,Kilfedder,gkilfedder67@hostgator.com,Male,84.168.17.247,12/23/2018,9,4909.15 +225,Stewart,Boylund,sboylund68@liveinternet.ru,Male,225.118.153.204,6/28/2019,11,3319.59 +226,Jo,Kitchiner,jkitchiner69@skype.com,Female,59.124.146.200,2/6/2019,67,1600.09 +227,Elvis,Fosken,efosken6a@pcworld.com,Male,205.87.85.15,9/9/2019,6,3922.34 +228,Cammie,Danson,cdanson6b@narod.ru,Female,56.105.230.103,2/13/2019,65,1885.94 +229,Fabiano,Garaghan,fgaraghan6c@ezinearticles.com,Male,78.123.49.176,7/20/2019,51,3114.09 +230,Ailina,Givens,agivens6d@cbsnews.com,Female,60.180.138.180,7/5/2019,89,4535.53 +231,Armstrong,Dunkerly,adunkerly6e@independent.co.uk,Male,90.216.21.184,2/26/2019,14,1990.77 +232,Catharine,Matityahu,cmatityahu6f@alibaba.com,Female,57.63.241.97,3/10/2019,95,3363.25 +233,Sile,Glassman,sglassman6g@google.com.au,Female,69.8.60.158,9/2/2019,51,1557.41 +234,Benyamin,Scantleberry,bscantleberry6h@constantcontact.com,Male,130.141.87.136,2/28/2019,73,2950.29 +235,Mendel,Whether,mwhether6i@google.com,Male,241.33.68.87,8/7/2019,82,1090.67 +236,Marcos,Everitt,meveritt6j@yelp.com,Male,233.19.226.38,3/31/2019,37,1052.69 +237,Missie,Eastment,meastment6k@statcounter.com,Female,70.194.212.197,12/20/2018,78,4585.45 +238,Tim,Godfray,tgodfray6l@ucla.edu,Male,200.47.142.40,2/15/2019,5,1682.26 +239,Aguste,Pinney,apinney6m@posterous.com,Male,230.75.22.90,5/30/2019,41,2301.61 +240,Matthias,Francesconi,mfrancesconi6n@biglobe.ne.jp,Male,105.93.88.19,12/8/2018,67,4462.69 +241,Donna,Luckes,dluckes6o@over-blog.com,Female,213.3.126.62,7/2/2019,16,2107.3 +242,Nathanial,Spurden,nspurden6p@microsoft.com,Male,230.177.142.225,3/28/2019,78,1795.33 +243,Stanford,Keets,skeets6q@sbwire.com,Male,192.100.251.103,5/30/2019,60,1912.38 +244,Joly,Mont,jmont6r@technorati.com,Female,14.164.142.106,4/29/2019,23,4036.1 +245,Tyrus,Reasce,treasce6s@ibm.com,Male,12.231.0.1,12/15/2018,60,2707.42 +246,Lee,MacKeogh,lmackeogh6t@woothemes.com,Male,182.53.212.225,12/26/2018,49,1052.96 +247,Valeria,Le Hucquet,vlehucquet6u@cisco.com,Female,155.224.213.71,1/8/2019,67,1292.01 +248,Jillana,Breddy,jbreddy6v@fc2.com,Female,97.42.41.207,1/27/2019,79,2603.53 +249,Alic,Bergeau,abergeau6w@fema.gov,Male,247.140.30.46,10/2/2019,61,1209.85 +250,Cairistiona,Meredith,cmeredith6x@hhs.gov,Female,70.70.142.81,4/12/2019,17,3084.34 +251,Baird,Hollerin,bhollerin6y@issuu.com,Male,22.99.57.132,9/29/2019,50,2427.97 +252,Leonard,Caldecot,lcaldecot6z@vistaprint.com,Male,181.234.61.189,1/17/2019,4,4714.6 +253,Frants,Cochrane,fcochrane70@usgs.gov,Male,243.153.97.44,10/25/2019,7,2131.81 +254,Gertrud,Enticknap,genticknap71@csmonitor.com,Female,252.82.226.210,5/21/2019,11,2407.13 +255,Sullivan,Chattington,schattington72@pinterest.com,Male,155.193.236.102,1/10/2019,82,4204.53 +256,Taite,Sighard,tsighard73@dell.com,Male,201.139.42.108,7/13/2019,40,3983.56 +257,Rozina,Stannas,rstannas74@eventbrite.com,Female,175.179.96.178,1/6/2019,67,4645.75 +258,Sanson,Trevon,strevon75@patch.com,Male,40.224.2.112,5/22/2019,69,4383.12 +259,Torrey,Gilhoolie,tgilhoolie76@cdc.gov,Male,210.149.121.27,6/3/2019,44,2232.38 +260,Lauri,Lackeye,llackeye77@ted.com,Female,104.17.18.94,6/14/2019,21,3534.19 +261,Rica,Vineall,rvineall78@craigslist.org,Female,164.92.184.86,7/29/2019,67,2725.57 +262,Dane,Youell,dyouell79@flavors.me,Male,33.252.240.196,12/2/2018,78,2150.98 +263,Tricia,Singyard,tsingyard7a@facebook.com,Female,117.214.88.252,3/29/2019,46,1692.77 +264,Adrea,Rubinsohn,arubinsohn7b@sakura.ne.jp,Female,27.195.5.14,9/8/2019,72,3230.72 +265,Dotti,Brader,dbrader7c@addthis.com,Female,13.85.47.30,12/26/2018,100,2732.26 +266,Gabi,Fackrell,gfackrell7d@scientificamerican.com,Female,58.145.63.14,8/11/2019,95,1238.44 +267,Price,Moger,pmoger7e@deviantart.com,Male,19.116.110.91,8/15/2019,23,1473.89 +268,Jayme,Wakelin,jwakelin7f@cisco.com,Male,26.252.180.229,10/5/2019,56,4366.22 +269,Maurizio,Dunsmuir,mdunsmuir7g@clickbank.net,Male,120.22.85.145,11/3/2019,31,1877.99 +270,Carlos,Edmands,cedmands7h@redcross.org,Male,20.67.51.110,6/14/2019,79,1238.13 +271,Johan,McGinly,jmcginly7i@amazonaws.com,Male,12.205.191.101,3/20/2019,49,4095.82 +272,Albrecht,Rotchell,arotchell7j@google.es,Male,60.170.217.205,9/28/2019,92,2894.61 +273,Eli,De Lacey,edelacey7k@goo.ne.jp,Male,55.7.194.20,11/10/2019,78,4537.32 +274,Sutherlan,Mill,smill7l@i2i.jp,Male,130.65.164.97,1/16/2019,31,1681.32 +275,Nathalie,Willder,nwillder7m@usnews.com,Female,69.170.87.110,5/5/2019,1,1024.53 +276,Rem,Iczokvitz,riczokvitz7n@harvard.edu,Male,7.203.57.167,4/4/2019,39,3672.82 +277,Lorens,Denyukin,ldenyukin7o@uol.com.br,Male,203.70.155.205,2/14/2019,41,3600.8 +278,Mag,Pigden,mpigden7p@psu.edu,Female,239.194.157.251,6/14/2019,47,3599.97 +279,Petronia,Carvill,pcarvill7q@apple.com,Female,151.29.121.135,8/16/2019,68,4879.68 +280,Felicity,Capewell,fcapewell7r@patch.com,Female,176.136.185.249,9/14/2019,75,2448.16 +281,Hartwell,Zellner,hzellner7s@wordpress.org,Male,210.182.89.221,5/26/2019,68,4758.87 +282,Marcela,Geggie,mgeggie7t@samsung.com,Female,118.65.180.139,2/10/2019,15,4222.62 +283,Keith,Loxston,kloxston7u@etsy.com,Male,30.75.222.61,3/31/2019,90,1964.35 +284,Mattheus,Guinn,mguinn7v@virginia.edu,Male,157.141.233.64,2/5/2019,20,1509.77 +285,Cristy,Van den Bosch,cvandenbosch7w@yandex.ru,Female,106.186.220.48,2/10/2019,57,2650.92 +286,Pansie,Pull,ppull7x@tripod.com,Female,133.223.218.112,3/12/2019,16,1844.52 +287,Hyacinthie,Bools,hbools7y@phpbb.com,Female,6.136.111.144,6/24/2019,88,3753.3 +288,Cosimo,Humphris,chumphris7z@ftc.gov,Male,42.252.197.80,1/8/2019,5,1335.74 +289,Raquela,Ledbetter,rledbetter80@imdb.com,Female,25.84.71.62,12/13/2018,39,4474.51 +290,Domingo,McKelvey,dmckelvey81@globo.com,Male,139.34.201.250,9/6/2019,40,4039.82 +291,Britta,Grzesiak,bgrzesiak82@google.fr,Female,254.24.80.245,7/7/2019,73,1052.79 +292,Palmer,Gliddon,pgliddon83@example.com,Male,60.248.224.227,12/27/2018,33,4273.36 +293,Millard,Feore,mfeore84@mlb.com,Male,129.50.148.247,4/3/2019,60,2015.88 +294,Lanie,Blasgen,lblasgen85@ocn.ne.jp,Female,124.254.122.231,10/6/2019,35,1212.04 +295,Sherry,Jamrowicz,sjamrowicz86@newyorker.com,Female,8.187.192.111,6/12/2019,57,2819.04 +296,Payton,Loxton,ploxton87@ted.com,Male,20.185.112.233,5/16/2019,9,4976.7 +297,Anderea,Blandford,ablandford88@tinypic.com,Female,30.186.15.181,2/3/2019,54,3962.4 +298,Adele,Goldstein,agoldstein89@csmonitor.com,Female,101.24.151.157,8/29/2019,47,1300.26 +299,Christiana,Kybbye,ckybbye8a@soup.io,Female,164.124.255.193,8/24/2019,35,4524.75 +300,Bettine,Bryden,bbryden8b@tinyurl.com,Female,71.23.174.26,4/21/2019,27,3413.85 +301,Halsey,Deeble,hdeeble8c@gizmodo.com,Male,17.234.31.127,8/30/2019,74,2872.84 +302,Peggi,Gallier,pgallier8d@aol.com,Female,34.161.21.167,3/28/2019,26,2530.19 +303,Oby,Lemanu,olemanu8e@skyrock.com,Male,123.215.166.71,10/24/2019,90,2590.72 +304,Kelly,Limbert,klimbert8f@123-reg.co.uk,Male,225.87.194.99,7/15/2019,71,3252.4 +305,Edmon,Pittham,epittham8g@wufoo.com,Male,206.168.11.133,11/1/2019,84,4290.32 +306,Nicola,Hazelby,nhazelby8h@nih.gov,Male,248.203.87.61,8/14/2019,33,4439.66 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+318,Kirbie,Hickford,khickford8t@phoca.cz,Female,222.19.145.98,2/18/2019,75,2101.99 +319,Justen,Simonsson,jsimonsson8u@goodreads.com,Male,50.143.144.122,5/18/2019,23,3668.84 +320,Base,Quainton,bquainton8v@quantcast.com,Male,210.105.216.149,9/25/2019,17,3690.64 +321,Evonne,Biss,ebiss8w@blogspot.com,Female,151.218.87.78,6/20/2019,54,2157.7 +322,Desmund,Naris,dnaris8x@cafepress.com,Male,158.117.9.15,4/8/2019,55,3607.48 +323,Briant,Pulley,bpulley8y@rediff.com,Male,226.108.222.137,7/8/2019,4,2048.56 +324,Neddy,Wannop,nwannop8z@cisco.com,Male,183.31.92.122,8/27/2019,59,3925.77 +325,Trip,Eberst,teberst90@so-net.ne.jp,Male,147.174.43.177,3/9/2019,54,3125.57 +326,Feodor,Ccomini,fccomini91@patch.com,Male,101.243.251.93,9/13/2019,9,1769.13 +327,Lazar,Krinks,lkrinks92@infoseek.co.jp,Male,21.83.214.146,10/20/2019,24,4619.45 +328,Tate,Kedslie,tkedslie93@businesswire.com,Female,141.169.231.230,5/30/2019,49,1552.68 +329,Jessee,Boggon,jboggon94@kickstarter.com,Male,203.194.131.251,11/9/2019,24,1485.03 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+352,Ardeen,Loveman,aloveman9r@ow.ly,Female,156.219.46.63,2/10/2019,22,2964.62 +353,Raychel,Bilbie,rbilbie9s@cbslocal.com,Female,17.168.95.135,7/30/2019,77,2114.06 +354,Aylmer,Ortsmann,aortsmann9t@wufoo.com,Male,38.11.85.41,7/7/2019,93,3751.57 +355,Cassey,Ivanilov,civanilov9u@infoseek.co.jp,Female,55.185.103.167,8/19/2019,31,2292.8 +356,Hedwiga,Mabbitt,hmabbitt9v@hostgator.com,Female,138.225.43.83,5/18/2019,34,3855.85 +357,Andy,Selwin,aselwin9w@php.net,Female,125.221.82.185,11/12/2019,33,3992.33 +358,Alfonso,Waiton,awaiton9x@cam.ac.uk,Male,229.52.201.138,12/12/2018,79,4822.9 +359,Falito,Waugh,fwaugh9y@behance.net,Male,139.179.83.249,2/9/2019,58,4262.13 +360,Goldia,Devennie,gdevennie9z@globo.com,Female,201.225.234.254,6/29/2019,57,4846.5 +361,Charmaine,Craney,ccraneya0@tinypic.com,Female,225.240.75.169,7/12/2019,84,1316.62 +362,Zeke,Laidlow,zlaidlowa1@ehow.com,Male,166.40.176.121,7/4/2019,35,3356.21 +363,Brok,Drayton,bdraytona2@livejournal.com,Male,144.40.40.68,8/13/2019,11,4276.45 +364,Yorke,Stickins,ystickinsa3@joomla.org,Male,43.66.244.157,9/2/2019,69,4537.3 +365,Niven,Carlow,ncarlowa4@berkeley.edu,Male,172.206.45.58,7/5/2019,65,3926.86 +366,Dexter,Roycraft,droycrafta5@bbb.org,Male,77.45.212.134,7/21/2019,55,3035.99 +367,Nicoline,Maryin,nmaryina6@google.com.au,Female,47.168.8.172,8/1/2019,41,4917.76 +368,Ryon,Manuaud,rmanuauda7@domainmarket.com,Male,47.123.90.232,2/9/2019,74,2439.17 +369,Yehudi,Paylie,ypayliea8@domainmarket.com,Male,191.251.55.194,1/27/2019,6,1923.42 +370,Griffith,Tincey,gtinceya9@oaic.gov.au,Male,25.66.148.134,9/12/2019,39,2655.81 +371,Rafa,McLeman,rmclemanaa@mac.com,Female,230.203.148.84,5/6/2019,15,1870.44 +372,Ethel,Deuss,edeussab@hp.com,Female,28.53.103.241,5/16/2019,32,1469.62 +373,Vasily,Sockell,vsockellac@istockphoto.com,Male,56.222.178.227,3/11/2019,69,1735.2 +374,Lorenza,Adelsberg,ladelsbergad@fda.gov,Female,11.94.242.174,1/22/2019,86,3213.96 +375,Colas,Wakerley,cwakerleyae@hostgator.com,Male,145.79.64.143,10/18/2019,32,3929.61 +376,Corbie,Suthworth,csuthworthaf@so-net.ne.jp,Male,185.110.156.111,11/20/2019,59,1791.8 +377,Raviv,Burdon,rburdonag@google.cn,Male,199.130.230.228,8/31/2019,56,2672.48 +378,Willabella,Braybrook,wbraybrookah@wired.com,Female,21.168.228.152,8/23/2019,81,3055.15 +379,Stavro,Culley,sculleyai@rambler.ru,Male,200.89.13.168,7/13/2019,53,4163.02 +380,Even,Scargill,escargillaj@boston.com,Male,239.83.129.85,4/1/2019,3,3610.96 +381,Estell,Feltham,efelthamak@amazon.com,Female,15.107.128.8,12/17/2018,47,4775.21 +382,Babette,Vel,bvelal@spotify.com,Female,175.148.241.38,1/10/2019,41,3884.25 +383,Artair,Sonschein,asonscheinam@paginegialle.it,Male,127.94.90.52,9/23/2019,34,3579.98 +384,Pegeen,Taynton,ptayntonan@mozilla.org,Female,237.12.237.200,8/17/2019,83,4949.73 +385,Rubina,Yarnley,ryarnleyao@bravesites.com,Female,163.161.0.128,2/1/2019,48,2608.0 +386,Imojean,Swadlen,iswadlenap@behance.net,Female,187.101.162.242,11/26/2019,25,3891.41 +387,Remington,Hunnam,rhunnamaq@xrea.com,Male,155.26.3.155,3/2/2019,98,4085.69 +388,Cart,Symson,csymsonar@arstechnica.com,Male,65.122.8.87,12/12/2018,10,1780.3 +389,Kate,Fitzsimons,kfitzsimonsas@i2i.jp,Female,43.231.183.143,9/5/2019,8,4589.81 +390,Derril,Rimmington,drimmingtonat@homestead.com,Male,6.22.197.94,5/8/2019,63,3112.81 +391,Alyson,McCue,amccueau@wikia.com,Female,23.46.105.252,8/28/2019,70,3955.82 +392,Magdalen,Huncoot,mhuncootav@spiegel.de,Female,148.37.230.51,10/31/2019,2,4091.18 +393,Quincey,Tumilson,qtumilsonaw@webeden.co.uk,Male,56.32.172.37,4/8/2019,42,1930.92 +394,Patrick,Causer,pcauserax@ucsd.edu,Male,246.121.43.120,5/6/2019,93,4121.69 +395,Nadia,Vannuccini,nvannucciniay@ifeng.com,Female,136.227.239.1,6/28/2019,9,2309.55 +396,Grethel,Briggs,gbriggsaz@opensource.org,Female,74.158.54.139,12/18/2018,93,1324.43 +397,Carmelina,Witson,cwitsonb0@slate.com,Female,0.189.23.96,9/17/2019,95,2043.81 +398,Stanford,Gariff,sgariffb1@latimes.com,Male,30.67.52.138,11/28/2019,23,3754.58 +399,Broddie,Dunster,bdunsterb2@businesswire.com,Male,195.171.91.125,5/13/2019,35,1781.96 +400,Brigit,Brownsworth,bbrownsworthb3@yale.edu,Female,235.56.89.121,5/7/2019,11,2976.14 +401,Hillary,Daughtrey,hdaughtreyb4@zimbio.com,Male,171.99.161.95,3/31/2019,14,4683.28 +402,Rubetta,Ryding,rrydingb5@facebook.com,Female,121.209.254.88,2/16/2019,48,3181.31 +403,Darill,Giffin,dgiffinb6@so-net.ne.jp,Male,76.229.144.119,12/1/2019,20,1267.15 +404,Meredith,Leghorn,mleghornb7@google.co.jp,Female,88.130.30.36,3/6/2019,73,1165.58 +405,Godfrey,Kleinhandler,gkleinhandlerb8@auda.org.au,Male,202.128.125.132,2/14/2019,50,4151.4 +406,Andie,Tootell,atootellb9@netscape.com,Male,145.145.81.122,6/12/2019,41,1126.15 +407,Roselia,Craise,rcraiseba@java.com,Female,200.79.82.15,6/21/2019,56,4562.67 +408,Conny,Saice,csaicebb@baidu.com,Male,242.82.15.36,11/6/2019,20,4226.99 +409,Keary,Hartrick,khartrickbc@independent.co.uk,Male,189.126.222.123,12/13/2018,86,3150.61 +410,Salli,Scotchmer,sscotchmerbd@cloudflare.com,Female,229.204.119.25,1/15/2019,85,4684.69 +411,Verena,Everington,veveringtonbe@delicious.com,Female,49.206.228.25,4/25/2019,67,2869.88 +412,Bambi,Berzon,bberzonbf@dailymotion.com,Female,47.88.180.96,10/26/2019,67,4843.29 +413,Barbara,Kovacs,bkovacsbg@deliciousdays.com,Female,116.1.242.72,4/20/2019,43,2218.64 +414,Laurice,Adrianello,ladrianellobh@nbcnews.com,Female,69.69.82.208,6/21/2019,91,3331.25 +415,Nadine,Stenning,nstenningbi@tripadvisor.com,Female,105.204.235.18,9/2/2019,13,2037.16 +416,Cristina,Milsap,cmilsapbj@t-online.de,Female,36.13.98.63,12/4/2018,51,2933.37 +417,Kaleb,Easby,keasbybk@howstuffworks.com,Male,59.120.45.235,12/11/2018,4,1277.52 +418,Alister,Kyberd,akyberdbl@wsj.com,Male,184.221.78.151,5/1/2019,85,3931.28 +419,Roma,Uvedale,ruvedalebm@jigsy.com,Male,127.113.15.154,7/21/2019,71,4158.48 +420,Chrisse,Briggs,cbriggsbn@godaddy.com,Male,36.183.171.148,7/26/2019,63,1042.39 +421,Armin,Fidelus,afidelusbo@sogou.com,Male,207.188.30.35,1/13/2019,52,2565.26 +422,Helena,Harlow,hharlowbp@archive.org,Female,125.154.161.88,7/31/2019,11,3388.81 +423,Judy,Stone Fewings,jstonefewingsbq@youtube.com,Female,220.36.160.104,10/2/2019,54,2051.64 +424,Eddi,Gritsaev,egritsaevbr@sphinn.com,Female,36.194.183.230,6/1/2019,60,3020.44 +425,Royal,Juares,rjuaresbs@craigslist.org,Male,84.222.181.156,5/30/2019,30,2382.04 +426,Violette,Kopje,vkopjebt@blogs.com,Female,12.135.203.81,5/12/2019,61,3565.51 +427,Lesli,Franses,lfransesbu@histats.com,Female,124.138.107.148,11/22/2019,71,2644.08 +428,Alric,Barehead,abareheadbv@stumbleupon.com,Male,220.251.223.254,12/17/2018,6,2989.17 +429,Karin,Rosingdall,krosingdallbw@ucoz.ru,Female,83.135.106.197,8/9/2019,89,1199.79 +430,Margaretta,Hartles,mhartlesbx@google.com.br,Female,146.47.193.138,10/15/2019,82,1786.77 +431,Broderic,Adenot,badenotby@upenn.edu,Male,131.77.120.209,9/22/2019,15,4020.02 +432,Konstanze,Scintsbury,kscintsburybz@eepurl.com,Female,187.225.93.97,9/12/2019,91,3882.0 +433,Heriberto,Branchett,hbranchettc0@google.ca,Male,47.131.19.61,9/10/2019,30,3830.84 +434,Dulcy,Moxham,dmoxhamc1@pinterest.com,Female,139.65.244.96,3/31/2019,48,1930.97 +435,Kakalina,Pellamont,kpellamontc2@51.la,Female,46.138.21.69,3/29/2019,53,2265.45 +436,Durward,McAlester,dmcalesterc3@wisc.edu,Male,43.93.21.201,4/1/2019,19,4312.73 +437,Alfy,Dawby,adawbyc4@alibaba.com,Female,60.254.119.60,7/12/2019,53,1447.77 +438,Rafferty,Cullabine,rcullabinec5@acquirethisname.com,Male,107.140.224.21,8/31/2019,10,4627.05 +439,Catlaina,Laurenty,claurentyc6@tripod.com,Female,72.146.104.69,5/8/2019,77,1050.26 +440,Yves,Kinnock,ykinnockc7@narod.ru,Male,212.80.251.121,9/24/2019,26,4289.62 +441,Rustie,Runham,rrunhamc8@google.cn,Male,14.208.111.115,3/30/2019,17,3840.32 +442,Shannon,Teece,steecec9@issuu.com,Female,224.215.224.129,11/26/2019,87,3028.32 +443,Tawsha,Huggons,thuggonsca@oakley.com,Female,28.182.47.108,12/9/2018,80,3985.84 +444,Jozef,Ropcke,jropckecb@washington.edu,Male,71.34.133.206,10/5/2019,51,2177.5 +445,Brittany,Treadgold,btreadgoldcc@dell.com,Female,70.179.135.39,10/6/2019,35,1239.98 +446,Reynolds,Dungay,rdungaycd@taobao.com,Male,198.198.184.105,3/17/2019,84,1401.56 +447,Deeyn,MacNeely,dmacneelyce@google.nl,Female,129.131.12.166,9/24/2019,67,2584.15 +448,Opalina,McAndrew,omcandrewcf@imgur.com,Female,170.144.235.40,9/27/2019,32,1990.03 +449,Annabell,Bowne,abownecg@tamu.edu,Female,15.103.10.164,11/4/2019,82,4979.86 +450,Shepherd,Toye,stoyech@oakley.com,Male,28.94.51.150,5/4/2019,79,3581.94 +451,Dolorita,Rowatt,drowattci@yale.edu,Female,199.209.35.59,1/7/2019,46,2901.35 +452,Gale,Fenich,gfenichcj@seattletimes.com,Female,117.140.18.113,2/2/2019,95,1402.56 +453,Salvidor,Ruperti,srupertick@mashable.com,Male,253.18.192.61,6/10/2019,83,4161.11 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+511,Theo,Olivetta,tolivettae6@yellowpages.com,Male,135.95.4.136,8/1/2019,59,3670.72 +512,Gabie,Blasius,gblasiuse7@vistaprint.com,Male,176.138.63.24,3/12/2019,100,3276.6 +513,Tiebold,Wharmby,twharmbye8@chicagotribune.com,Male,204.219.78.122,6/13/2019,73,3542.76 +514,Tann,Duke,tdukee9@irs.gov,Male,153.153.136.24,3/14/2019,70,2338.35 +515,Westley,Ottery,wotteryea@soundcloud.com,Male,35.239.11.15,9/18/2019,61,2876.26 +516,Noll,Birkmyre,nbirkmyreeb@histats.com,Male,45.218.226.8,5/4/2019,3,3180.04 +517,Angelle,Canizares,acanizaresec@usgs.gov,Female,79.98.22.161,11/16/2019,57,1414.53 +518,Maddy,Rawlins,mrawlinsed@ocn.ne.jp,Female,194.212.205.87,8/24/2019,9,2037.3 +519,Gonzales,Digle,gdigleee@tinyurl.com,Male,188.81.253.229,2/11/2019,31,1096.61 +520,Matias,Arrighetti,marrighettief@jimdo.com,Male,119.210.95.197,10/13/2019,59,1525.12 +521,Ag,Almon,aalmoneg@discuz.net,Female,211.209.145.71,5/9/2019,5,1832.58 +522,Romona,Keppel,rkeppeleh@auda.org.au,Female,180.44.222.236,12/5/2018,69,1470.18 +523,Aeriel,Seear,aseearei@over-blog.com,Female,132.233.16.148,6/8/2019,20,4592.62 +524,Terri,Trulocke,ttrulockeej@clickbank.net,Female,138.176.80.221,10/1/2019,5,1787.96 +525,Linn,Stonestreet,lstonestreetek@un.org,Male,74.243.5.94,11/27/2019,31,1779.4 +526,Dmitri,Coode,dcoodeel@ameblo.jp,Male,201.81.71.70,1/25/2019,94,1400.26 +527,Alane,Sabattier,asabattierem@scribd.com,Female,32.109.38.132,10/10/2019,10,1416.04 +528,Sadye,Esplin,sesplinen@tiny.cc,Female,76.85.72.29,3/14/2019,45,3626.01 +529,Alexia,Iorizzo,aiorizzoeo@themeforest.net,Female,172.234.209.31,7/10/2019,46,4064.06 +530,Gayelord,Kalewe,gkaleweep@over-blog.com,Male,83.169.129.240,9/10/2019,53,1508.96 +531,Clive,Stuchbery,cstuchberyeq@dell.com,Male,9.182.133.41,12/29/2018,21,4980.91 +532,Derron,Lober,dloberer@epa.gov,Male,184.138.78.88,2/14/2019,24,2245.42 +533,Aleda,Addison,aaddisones@wsj.com,Female,181.181.179.67,6/17/2019,81,4493.79 +534,Marshall,Ugolotti,mugolottiet@aboutads.info,Male,159.50.251.20,7/3/2019,35,1104.16 +535,Garold,Moxon,gmoxoneu@slashdot.org,Male,117.142.41.149,10/7/2019,34,2523.56 +536,Fee,Colleer,fcolleerev@state.gov,Male,207.42.100.89,4/12/2019,87,4816.8 +537,Smitty,Smallridge,ssmallridgeew@spotify.com,Male,132.67.213.163,2/26/2019,84,3814.0 +538,Ber,Pettegre,bpettegreex@google.ru,Male,8.176.93.49,9/6/2019,67,3272.53 +539,Tybi,Brown,tbrowney@ibm.com,Female,100.73.19.52,3/7/2019,76,1088.01 +540,Roxanne,Rigler,rriglerez@weebly.com,Female,29.96.199.117,4/1/2019,65,2694.03 +541,Alphonse,Astlett,aastlettf0@hatena.ne.jp,Male,244.194.52.149,7/29/2019,47,1467.11 +542,Calvin,Finn,cfinnf1@ucoz.com,Male,199.41.9.21,12/30/2018,7,1828.33 +543,Broddie,Brandts,bbrandtsf2@answers.com,Male,100.8.254.225,10/29/2019,64,1792.68 +544,Herrick,Chadderton,hchaddertonf3@wired.com,Male,187.66.85.223,3/1/2019,38,3230.59 +545,Teodoor,Benny,tbennyf4@home.pl,Male,6.177.43.100,4/29/2019,13,2503.4 +546,Karl,Wrintmore,kwrintmoref5@apple.com,Male,73.104.69.176,8/7/2019,97,2688.4 +547,Alexandra,Testo,atestof6@loc.gov,Female,15.230.156.211,6/9/2019,84,4522.93 +548,Colin,Giorgietto,cgiorgiettof7@nymag.com,Male,13.75.177.91,2/21/2019,3,3730.12 +549,Talbot,McNickle,tmcnicklef8@wp.com,Male,44.250.27.152,3/18/2019,30,2209.95 +550,Cassandre,Hayle,chaylef9@newyorker.com,Female,116.81.18.95,11/21/2019,10,3204.22 +551,Tommi,Bilney,tbilneyfa@prweb.com,Female,61.67.100.120,10/16/2019,79,3613.66 +552,Larisa,Pollok,lpollokfb@loc.gov,Female,79.115.80.86,9/30/2019,29,1901.41 +553,Deb,Dorkins,ddorkinsfc@xrea.com,Female,26.52.68.180,10/15/2019,99,4906.29 +554,Johanna,Kainz,jkainzfd@chronoengine.com,Female,142.73.143.215,8/18/2019,4,1226.42 +555,Gasparo,Handrek,ghandrekfe@vinaora.com,Male,21.21.61.211,10/16/2019,91,1731.15 +556,Elwyn,Zahor,ezahorff@washingtonpost.com,Male,184.132.148.138,1/5/2019,55,1626.51 +557,Devina,De Vaar,ddevaarfg@miibeian.gov.cn,Female,195.180.225.61,7/12/2019,2,4095.24 +558,Ikey,Verner,ivernerfh@msu.edu,Male,175.129.187.1,8/8/2019,10,1643.3 +559,Shae,Potteril,spotterilfi@cyberchimps.com,Male,135.218.100.211,9/30/2019,43,2747.3 +560,Kim,Kittel,kkittelfj@buzzfeed.com,Male,224.125.75.142,12/12/2018,17,4109.17 +561,Alexei,Morecomb,amorecombfk@independent.co.uk,Male,172.224.245.105,12/9/2018,33,2899.08 +562,Cathryn,Finnan,cfinnanfl@ask.com,Female,27.110.235.165,5/28/2019,94,4841.66 +563,Maryjane,Duffus,mduffusfm@eventbrite.com,Female,3.255.15.114,10/22/2019,54,1514.19 +564,Violetta,Reisk,vreiskfn@typepad.com,Female,39.227.35.28,4/18/2019,92,4609.62 +565,Vicky,Lots,vlotsfo@cisco.com,Female,231.114.228.254,7/16/2019,5,2747.65 +566,Odilia,Shemmans,oshemmansfp@phpbb.com,Female,173.16.238.214,7/15/2019,64,2509.13 +567,Doralynn,Borne,dbornefq@vistaprint.com,Female,182.193.106.25,9/27/2019,58,2388.86 +568,Gallagher,Tinsey,gtinseyfr@wsj.com,Male,222.169.135.174,8/26/2019,9,2587.26 +569,Ulberto,Hellyar,uhellyarfs@yellowbook.com,Male,248.76.37.74,7/15/2019,30,1922.6 +570,Carilyn,Skones,cskonesft@liveinternet.ru,Female,156.207.111.147,5/19/2019,37,4692.05 +571,Sarine,Rubinovitch,srubinovitchfu@networksolutions.com,Female,80.120.49.55,8/19/2019,20,1594.95 +572,Jemmy,Marlowe,jmarlowefv@engadget.com,Female,75.14.237.58,4/14/2019,51,1904.61 +573,Gaby,Demelt,gdemeltfw@infoseek.co.jp,Male,229.88.216.80,2/3/2019,51,1648.97 +574,Jacob,Lamberton,jlambertonfx@hugedomains.com,Male,47.215.55.112,11/23/2019,98,2245.77 +575,Emile,Bullivent,ebulliventfy@is.gd,Male,222.149.227.61,1/14/2019,28,2610.31 +576,Reidar,Gierardi,rgierardifz@flickr.com,Male,137.20.90.27,7/14/2019,44,1963.44 +577,Dwain,De Mars,ddemarsg0@theglobeandmail.com,Male,29.126.92.147,5/8/2019,83,2977.28 +578,Seth,Hebron,shebrong1@github.io,Male,97.93.183.184,2/17/2019,17,2725.06 +579,Shep,Rabjohn,srabjohng2@sfgate.com,Male,118.230.96.220,4/5/2019,75,4150.25 +580,Georgine,Martill,gmartillg3@who.int,Female,89.160.34.190,1/8/2019,37,4712.71 +581,Theodora,Mitchenson,tmitchensong4@delicious.com,Female,140.245.239.155,4/11/2019,58,1505.59 +582,Geri,Reedman,greedmang5@mac.com,Male,149.218.213.221,6/22/2019,29,3919.37 +583,Hamel,Ruddick,hruddickg6@uiuc.edu,Male,74.103.5.12,3/19/2019,32,2242.44 +584,Marylou,Baggott,mbaggottg7@goo.gl,Female,67.211.254.135,11/7/2019,35,4631.99 +585,Ignacius,Croisdall,icroisdallg8@imdb.com,Male,194.136.32.103,4/9/2019,22,4505.82 +586,Agnes,Pentercost,apentercostg9@yolasite.com,Female,201.109.22.156,4/7/2019,6,2659.75 +587,Phillipe,O'Henery,poheneryga@baidu.com,Male,169.210.213.194,4/28/2019,100,3089.16 +588,Mersey,Bilston,mbilstongb@mozilla.org,Female,212.118.21.202,6/6/2019,47,1546.62 +589,Carie,Pawelski,cpawelskigc@examiner.com,Female,185.32.131.13,12/23/2018,13,1607.17 +590,Cassandra,Zywicki,czywickigd@ask.com,Female,58.164.239.126,10/6/2019,86,3316.82 +591,Myrtia,Avrasin,mavrasinge@tripod.com,Female,23.46.205.131,6/2/2019,28,1376.97 +592,Stanford,Greatrakes,sgreatrakesgf@europa.eu,Male,110.99.123.217,12/31/2018,89,4653.75 +593,Giulietta,Hinkens,ghinkensgg@list-manage.com,Female,146.42.27.152,3/17/2019,90,4663.89 +594,Dame,Eam,deamgh@jalbum.net,Male,93.210.79.222,12/3/2018,99,4955.03 +595,Claresta,Spradbery,cspradberygi@wunderground.com,Female,177.74.189.41,5/7/2019,69,2660.67 +596,Charisse,Janos,cjanosgj@yellowbook.com,Female,107.1.160.123,11/30/2019,27,3260.09 +597,Mufi,Letterick,mletterickgk@apache.org,Female,230.153.229.231,6/16/2019,59,2986.56 +598,Fabe,Sabberton,fsabbertongl@mapquest.com,Male,219.168.121.194,7/18/2019,70,3658.28 +599,Nikolas,Gildersleaves,ngildersleavesgm@ox.ac.uk,Male,162.123.231.93,7/20/2019,78,1233.59 +600,Justin,Vigours,jvigoursgn@sourceforge.net,Male,154.142.42.26,6/28/2019,31,4990.95 +601,Kile,Le Clercq,kleclercqgo@indiatimes.com,Male,239.28.13.21,12/13/2018,84,1001.19 +602,Isidoro,Tregonna,itregonnagp@webeden.co.uk,Male,171.67.239.44,1/16/2019,41,4417.17 +603,Mab,Ironside,mironsidegq@google.cn,Female,42.162.241.202,7/23/2019,91,2259.53 +604,Dimitry,Manass,dmanassgr@51.la,Male,45.173.17.98,1/25/2019,46,1586.27 +605,Rossy,Arden,rardengs@bigcartel.com,Male,214.115.174.58,4/28/2019,51,1823.4 +606,Daniele,Barnard,dbarnardgt@so-net.ne.jp,Female,135.128.233.112,2/1/2019,71,3995.81 +607,Helene,Westphalen,hwestphalengu@addthis.com,Female,106.111.55.197,11/15/2019,95,2675.0 +608,Zeke,Mixer,zmixergv@europa.eu,Male,70.187.67.163,2/1/2019,26,1019.63 +609,Modesta,Cock,mcockgw@berkeley.edu,Female,16.132.149.179,1/19/2019,79,2922.43 +610,Bard,Vieyra,bvieyragx@shinystat.com,Male,192.107.97.186,10/5/2019,17,4998.16 +611,Boote,Geall,bgeallgy@networksolutions.com,Male,54.164.158.9,1/1/2019,95,3385.89 +612,Ninnetta,Loveman,nlovemangz@histats.com,Female,25.150.239.54,9/19/2019,24,3487.97 +613,Sigismund,Fitzer,sfitzerh0@163.com,Male,93.203.46.222,9/7/2019,26,3405.11 +614,Perren,Haquin,phaquinh1@networkadvertising.org,Male,27.195.139.40,7/13/2019,95,4472.37 +615,Ramsay,Galland,rgallandh2@sohu.com,Male,64.157.166.62,2/15/2019,30,3256.71 +616,Silva,Strete,sstreteh3@yellowpages.com,Female,28.120.167.41,10/30/2019,67,4109.49 +617,Dee dee,Poate,dpoateh4@github.io,Female,169.234.44.240,1/26/2019,87,2487.52 +618,Bianca,Martell,bmartellh5@bloomberg.com,Female,17.155.34.89,12/5/2018,88,2523.49 +619,Alan,Wallwork,awallworkh6@bloglines.com,Male,136.41.225.203,11/17/2019,32,2593.63 +620,Alysa,Lembrick,alembrickh7@github.io,Female,4.186.245.140,3/12/2019,42,4087.94 +621,Yoshiko,Alans,yalansh8@list-manage.com,Female,113.124.38.19,1/8/2019,5,3091.72 +622,Chelsy,Watford,cwatfordh9@yellowpages.com,Female,10.18.35.232,5/4/2019,86,4661.23 +623,Bill,Gotthard,bgotthardha@moonfruit.com,Male,169.67.131.116,5/21/2019,11,1287.77 +624,Cad,Kirtlan,ckirtlanhb@xrea.com,Male,40.152.181.2,1/24/2019,24,3155.78 +625,Ahmed,McClenan,amcclenanhc@webmd.com,Male,20.99.191.248,8/13/2019,36,3301.08 +626,Tobit,Barthelemy,tbarthelemyhd@bizjournals.com,Male,35.143.211.133,9/10/2019,98,1382.31 +627,Kacie,Lurner,klurnerhe@illinois.edu,Female,4.70.92.52,3/14/2019,58,3754.13 +628,Keith,Skeech,kskeechhf@pbs.org,Male,14.243.98.120,8/22/2019,99,2840.36 +629,Loy,Brettoner,lbrettonerhg@marriott.com,Male,244.228.186.208,5/28/2019,83,1322.53 +630,Ring,Keppy,rkeppyhh@mediafire.com,Male,63.223.185.137,10/29/2019,49,3414.88 +631,Jennee,Saltsberg,jsaltsberghi@angelfire.com,Female,168.154.55.70,2/20/2019,89,1792.13 +632,Leslie,Sunley,lsunleyhj@furl.net,Female,142.154.216.195,1/14/2019,85,1049.36 +633,Serge,Brightman,sbrightmanhk@nsw.gov.au,Male,218.24.194.199,8/25/2019,17,3821.08 +634,Thadeus,Janosevic,tjanosevichl@auda.org.au,Male,117.42.23.253,6/4/2019,12,3771.8 +635,Spense,Ribchester,sribchesterhm@google.cn,Male,60.125.96.255,3/20/2019,10,4152.59 +636,Cordelia,Petrashov,cpetrashovhn@jalbum.net,Female,4.255.192.99,5/3/2019,42,4778.71 +637,Rhea,Jest,rjestho@hao123.com,Female,60.250.169.224,11/1/2019,88,3984.23 +638,Patricia,Feehery,pfeeheryhp@dot.gov,Female,238.226.108.19,1/3/2019,72,2436.71 +639,Griffie,Haswell,ghaswellhq@linkedin.com,Male,141.208.249.19,3/30/2019,54,4591.33 +640,Isahella,Brockett,ibrocketthr@webnode.com,Female,187.222.253.93,8/18/2019,40,2025.08 +641,Rhea,Castiglioni,rcastiglionihs@cpanel.net,Female,59.223.205.19,3/28/2019,8,4878.09 +642,Stu,Gladman,sgladmanht@yandex.ru,Male,148.174.89.129,2/7/2019,6,4658.63 +643,Meade,Penn,mpennhu@tinypic.com,Female,159.237.205.132,7/14/2019,67,1741.76 +644,Analise,Teesdale,ateesdalehv@istockphoto.com,Female,112.255.159.227,4/27/2019,43,4943.97 +645,Gabbie,Gawith,ggawithhw@bing.com,Male,156.88.177.49,9/1/2019,30,2127.26 +646,Fancie,Matura,fmaturahx@prweb.com,Female,245.199.208.176,10/14/2019,48,3611.12 +647,Oneida,Klee,okleehy@merriam-webster.com,Female,76.241.235.4,4/24/2019,75,3574.83 +648,Patty,Deadman,pdeadmanhz@deviantart.com,Female,119.238.140.122,12/3/2018,78,4990.77 +649,Rica,Habbershon,rhabbershoni0@imdb.com,Female,235.54.40.9,8/21/2019,72,4008.1 +650,Dominick,Morphey,dmorpheyi1@state.gov,Male,26.182.126.198,9/27/2019,32,2803.47 +651,Lin,Elvey,lelveyi2@qq.com,Female,217.142.5.50,5/2/2019,75,1382.58 +652,Candra,Ellershaw,cellershawi3@liveinternet.ru,Female,37.103.239.8,11/5/2019,64,1785.17 +653,Petr,Blamey,pblameyi4@cornell.edu,Male,102.165.209.59,11/16/2019,23,4146.8 +654,Starlene,Shoebrook,sshoebrooki5@un.org,Female,168.143.167.147,3/14/2019,4,4991.14 +655,Rochelle,Inggall,ringgalli6@biglobe.ne.jp,Female,102.127.212.247,9/12/2019,81,1583.69 +656,Ezmeralda,Jubert,ejuberti7@icio.us,Female,81.4.17.157,7/22/2019,52,4205.34 +657,Berny,Crufts,bcruftsi8@jigsy.com,Female,244.211.38.231,1/6/2019,46,4968.82 +658,Philis,Hitzke,phitzkei9@google.es,Female,206.218.221.24,10/5/2019,21,2082.32 +659,Dacey,Beck,dbeckia@netscape.com,Female,52.22.157.97,4/17/2019,22,3785.01 +660,Lammond,Esmead,lesmeadib@slate.com,Male,216.172.89.83,4/11/2019,87,4186.81 +661,Bevin,Pattullo,bpattulloic@yelp.com,Male,96.237.177.242,9/30/2019,9,3862.46 +662,Abbie,Melsom,amelsomid@businesswire.com,Female,226.28.218.124,8/28/2019,98,4520.86 +663,Hilarius,Weagener,hweagenerie@google.nl,Male,6.108.117.111,9/10/2019,80,4920.12 +664,Onida,Dewberry,odewberryif@chron.com,Female,95.189.17.251,7/12/2019,27,1206.75 +665,Sawyer,Behne,sbehneig@who.int,Male,232.40.190.187,6/26/2019,92,2730.93 +666,Ingaborg,Benoit,ibenoitih@diigo.com,Female,45.171.74.30,2/26/2019,79,3169.39 +667,Christyna,Beardow,cbeardowii@hibu.com,Female,212.112.66.197,8/15/2019,22,1664.25 +668,Chrysa,Bouda,cboudaij@posterous.com,Female,182.254.214.92,6/20/2019,58,4842.58 +669,Skell,Janny,sjannyik@statcounter.com,Male,117.115.79.57,8/25/2019,64,3147.82 +670,Willie,Gisbye,wgisbyeil@smh.com.au,Male,163.74.14.6,12/24/2018,60,2181.71 +671,Loydie,MacGiolla Pheadair,lmacgiollapheadairim@ftc.gov,Male,66.165.25.118,12/24/2018,17,4234.43 +672,Millisent,Warwick,mwarwickin@google.co.uk,Female,11.125.80.152,5/27/2019,57,2770.04 +673,Lutero,Campelli,lcampelliio@alibaba.com,Male,218.66.248.154,10/4/2019,29,4169.81 +674,Waverly,Kroch,wkrochip@google.co.uk,Male,117.138.8.158,10/6/2019,55,2248.55 +675,Jorge,Tipple,jtippleiq@google.cn,Male,75.32.226.30,1/5/2019,20,2316.81 +676,Rycca,Markham,rmarkhamir@posterous.com,Female,108.165.80.206,4/1/2019,37,3733.9 +677,Kendell,Tabourel,ktabourelis@ucsd.edu,Male,31.106.135.186,1/27/2019,41,2767.65 +678,Xymenes,Christofol,xchristofolit@slashdot.org,Male,205.98.142.54,4/17/2019,75,3761.36 +679,Willamina,Gluyas,wgluyasiu@patch.com,Female,208.141.12.123,9/10/2019,62,4055.56 +680,Missy,Chaudron,mchaudroniv@ox.ac.uk,Female,253.225.93.169,5/17/2019,54,3188.83 +681,Harrie,Levee,hleveeiw@dropbox.com,Female,104.60.96.6,9/4/2019,73,3779.89 +682,Nickie,Awdry,nawdryix@deviantart.com,Female,186.254.251.36,1/3/2019,76,2909.63 +683,Wash,Mapletoft,wmapletoftiy@baidu.com,Male,176.231.204.160,2/12/2019,3,1072.43 +684,Thea,Crankshaw,tcrankshawiz@bluehost.com,Female,240.218.215.50,12/5/2018,41,2503.95 +685,Bryna,Clemmen,bclemmenj0@slate.com,Female,117.149.65.242,6/24/2019,33,2635.47 +686,Fiann,Dipple,fdipplej1@usda.gov,Female,13.222.48.15,5/1/2019,97,4530.95 +687,Hilliary,Nowell,hnowellj2@jigsy.com,Female,237.64.236.63,9/8/2019,4,3931.64 +688,Francene,Perfitt,fperfittj3@upenn.edu,Female,35.17.66.62,3/5/2019,24,4353.24 +689,Adair,Foucher,afoucherj4@google.com,Male,184.228.99.56,5/7/2019,75,3261.23 +690,Evyn,Cristou,ecristouj5@xrea.com,Male,114.91.151.161,7/17/2019,95,3655.93 +691,Carleton,Mooreed,cmooreedj6@walmart.com,Male,17.30.55.141,8/31/2019,66,1486.95 +692,Ashley,Wallage,awallagej7@dyndns.org,Male,214.116.89.237,5/6/2019,17,3179.86 +693,Corney,Healeas,chealeasj8@phoca.cz,Male,36.170.221.28,12/24/2018,29,3068.73 +694,Marabel,Abbado,mabbadoj9@google.nl,Female,56.208.153.166,4/15/2019,4,4988.67 +695,Barr,Gregr,bgregrja@mozilla.com,Male,118.210.102.100,5/28/2019,31,2310.92 +696,Ulrika,Verni,uvernijb@hhs.gov,Female,252.208.223.205,3/13/2019,34,4254.15 +697,Carri,McWilliam,cmcwilliamjc@unblog.fr,Female,244.144.133.38,2/26/2019,57,4082.44 +698,Ciro,Burnet,cburnetjd@simplemachines.org,Male,56.117.3.236,11/7/2019,71,2943.82 +699,Eryn,Lindblom,elindblomje@paginegialle.it,Female,218.110.190.80,10/26/2019,98,3327.13 +700,Tandi,Jenney,tjenneyjf@sciencedirect.com,Female,61.111.94.217,8/9/2019,63,3161.01 +701,Karissa,McGeachy,kmcgeachyjg@themeforest.net,Female,97.151.85.120,3/15/2019,75,2793.19 +702,Daisie,Gosling,dgoslingjh@yale.edu,Female,80.132.185.177,12/24/2018,99,2564.95 +703,Web,Waters,wwatersji@icio.us,Male,138.42.236.158,11/26/2019,12,3133.25 +704,Beulah,Saltrese,bsaltresejj@rakuten.co.jp,Female,111.246.77.69,5/10/2019,85,1562.53 +705,Shea,Bansal,sbansaljk@timesonline.co.uk,Male,45.157.189.79,6/13/2019,64,4095.48 +706,Granger,Maddyson,gmaddysonjl@cdbaby.com,Male,4.174.208.225,10/22/2019,90,2312.31 +707,Frederic,Ferras,fferrasjm@europa.eu,Male,93.89.74.109,4/21/2019,1,2688.0 +708,Terri-jo,Blockwell,tblockwelljn@diigo.com,Female,139.206.39.244,10/28/2019,77,2032.65 +709,Portia,Messum,pmessumjo@cpanel.net,Female,205.8.231.154,7/5/2019,7,3703.36 +710,Fay,Bleakley,fbleakleyjp@fema.gov,Female,51.78.72.116,11/14/2019,97,4827.37 +711,Blayne,Marnane,bmarnanejq@nasa.gov,Male,254.10.160.105,11/17/2019,59,4393.22 +712,Brooke,Dreinan,bdreinanjr@yandex.ru,Male,101.190.49.170,7/13/2019,91,3896.42 +713,Dannie,Butten,dbuttenjs@w3.org,Male,254.183.249.58,7/3/2019,49,4087.49 +714,Niki,Gooch,ngoochjt@huffingtonpost.com,Male,14.126.59.16,2/13/2019,81,3541.85 +715,Penny,Bovis,pbovisju@macromedia.com,Female,194.73.1.154,9/15/2019,22,3343.77 +716,Agna,Wellings,awellingsjv@economist.com,Female,18.191.109.188,6/20/2019,59,3995.96 +717,Kelsi,Binestead,kbinesteadjw@weibo.com,Female,209.183.105.9,6/22/2019,10,2251.08 +718,Lucio,MacLennan,lmaclennanjx@oracle.com,Male,138.238.128.41,6/18/2019,76,4327.17 +719,Leonora,Shailer,lshailerjy@illinois.edu,Female,119.146.115.106,12/20/2018,29,4657.75 +720,Fonsie,O'Doogan,fodooganjz@cbsnews.com,Male,130.108.253.158,8/21/2019,94,1870.33 +721,Stearne,Claire,sclairek0@booking.com,Male,131.59.84.18,4/11/2019,98,3369.19 +722,Maye,Howis,mhowisk1@buzzfeed.com,Female,22.69.128.140,9/8/2019,85,1974.16 +723,Orton,Kiffe,okiffek2@github.com,Male,81.226.255.158,10/22/2019,52,3864.03 +724,Barty,Leadbeater,bleadbeaterk3@amazon.de,Male,208.66.100.248,4/24/2019,28,4214.56 +725,Sidnee,Coxhead,scoxheadk4@cam.ac.uk,Male,62.75.3.110,2/16/2019,8,4882.21 +726,Worden,Quan,wquank5@telegraph.co.uk,Male,86.238.192.197,3/13/2019,3,4136.64 +727,Brad,Marlor,bmarlork6@chron.com,Male,205.102.239.46,1/10/2019,27,3958.67 +728,Jefferson,Fanning,jfanningk7@google.de,Male,139.239.121.50,10/19/2019,26,3356.51 +729,North,Leidecker,nleideckerk8@nyu.edu,Male,240.19.0.156,4/1/2019,27,4866.45 +730,Chrissy,Rounsefull,crounsefullk9@topsy.com,Female,181.133.238.56,12/31/2018,19,1456.66 +731,Jennine,D'eathe,jdeatheka@ow.ly,Female,82.172.76.47,10/8/2019,87,1326.34 +732,Nedda,Hyland,nhylandkb@unesco.org,Female,85.174.186.120,10/9/2019,67,4967.34 +733,Ripley,Bavester,rbavesterkc@facebook.com,Male,200.70.120.11,7/30/2019,67,3364.31 +734,Amandi,Angrove,aangrovekd@hud.gov,Female,51.90.237.193,10/18/2019,98,3962.5 +735,Jandy,Fritchley,jfritchleyke@youtu.be,Female,236.202.35.177,11/17/2019,79,4641.47 +736,Kevin,Tozer,ktozerkf@godaddy.com,Male,92.185.81.191,9/20/2019,75,2652.18 +737,Erinna,Bucke,ebuckekg@hugedomains.com,Female,134.19.86.195,1/16/2019,19,4117.83 +738,Melania,Margach,mmargachkh@pagesperso-orange.fr,Female,57.86.172.205,1/22/2019,34,4369.1 +739,Darryl,Besset,dbessetki@canalblog.com,Female,150.168.219.97,10/15/2019,85,2449.44 +740,Urbano,Loy,uloykj@vistaprint.com,Male,192.95.90.230,1/29/2019,66,1697.74 +741,Shanan,Titcombe,stitcombekk@addtoany.com,Male,78.104.122.110,1/2/2019,31,2712.55 +742,North,Kropp,nkroppkl@huffingtonpost.com,Male,123.193.124.129,7/19/2019,32,2129.1 +743,Dario,Mennithorp,dmennithorpkm@nifty.com,Male,10.142.173.99,11/4/2019,64,2962.06 +744,Sally,Clerk,sclerkkn@youtu.be,Female,67.123.50.70,11/27/2019,32,4948.27 +745,Phillida,Grishkov,pgrishkovko@hatena.ne.jp,Female,105.59.57.150,5/19/2019,78,2513.36 +746,Warren,Gauthorpp,wgauthorppkp@dailymotion.com,Male,109.33.209.101,3/24/2019,74,3859.6 +747,Yurik,McArthur,ymcarthurkq@time.com,Male,177.53.197.246,6/4/2019,25,1927.47 +748,Lucine,Pritchett,lpritchettkr@adobe.com,Female,214.120.212.72,5/5/2019,44,3376.03 +749,Reeba,Facchini,rfacchiniks@fotki.com,Female,62.203.183.86,3/16/2019,29,2413.06 +750,Marys,MacGaughey,mmacgaugheykt@blogger.com,Female,241.223.207.143,7/28/2019,84,2694.2 +751,Abdul,Rollett,arollettku@intel.com,Male,224.183.67.157,5/16/2019,100,3327.64 +752,Lorry,Wolstenholme,lwolstenholmekv@facebook.com,Female,54.19.245.157,4/24/2019,43,4692.53 +753,Kore,Illingworth,killingworthkw@1und1.de,Female,4.3.169.154,11/2/2019,94,2378.36 +754,Mikel,Welbrock,mwelbrockkx@so-net.ne.jp,Male,94.86.96.2,5/16/2019,74,3938.38 +755,Debby,Largan,dlarganky@mozilla.org,Female,99.67.157.53,10/14/2019,62,2417.72 +756,Brana,Wabe,bwabekz@eepurl.com,Female,243.219.60.35,9/27/2019,36,3520.16 +757,Dyana,Dagworthy,ddagworthyl0@statcounter.com,Female,248.115.199.134,1/29/2019,10,1238.39 +758,Jobi,McAndie,jmcandiel1@alibaba.com,Female,76.175.25.123,9/8/2019,27,2305.18 +759,Karlene,Jery,kjeryl2@va.gov,Female,135.147.68.149,10/20/2019,58,1166.14 +760,Reinhold,Lepoidevin,rlepoidevinl3@baidu.com,Male,31.102.77.143,11/13/2019,22,2344.24 +761,Quent,Screen,qscreenl4@opera.com,Male,12.112.10.35,2/10/2019,53,4842.57 +762,Samuel,Lindberg,slindbergl5@github.io,Male,168.90.162.31,12/12/2018,89,1308.19 +763,Haleigh,Raubenheimer,hraubenheimerl6@quantcast.com,Female,105.212.55.253,1/14/2019,37,3535.98 +764,Shela,Croutear,scroutearl7@istockphoto.com,Female,91.117.195.180,2/8/2019,38,3265.27 +765,Kellyann,Capron,kcapronl8@example.com,Female,124.152.40.23,5/29/2019,50,4056.87 +766,Garey,enzley,genzleyl9@latimes.com,Male,90.152.83.190,9/28/2019,97,1906.66 +767,Mick,Schroter,mschroterla@google.fr,Male,7.60.95.15,5/31/2019,3,1395.4 +768,Alfy,Amdohr,aamdohrlb@google.com,Male,224.77.126.110,3/1/2019,50,4548.48 +769,Mirabelle,Staresmeare,mstaresmearelc@netvibes.com,Female,11.204.171.148,1/11/2019,55,4544.46 +770,Opal,Fincken,ofinckenld@paypal.com,Female,163.158.79.29,9/30/2019,16,4021.93 +771,Donielle,Philliphs,dphilliphsle@g.co,Female,141.55.105.178,7/20/2019,49,1131.35 +772,Stephie,Whitaker,swhitakerlf@skyrock.com,Female,225.117.77.187,3/27/2019,10,4871.35 +773,Elisa,Reddell,ereddelllg@cargocollective.com,Female,74.40.71.217,9/11/2019,9,4241.01 +774,Lars,Bunner,lbunnerlh@si.edu,Male,169.187.187.173,3/18/2019,30,4554.14 +775,Travers,Prinnett,tprinnettli@artisteer.com,Male,105.11.246.207,5/19/2019,60,1589.02 +776,Laurens,Held,lheldlj@delicious.com,Male,124.146.44.210,8/19/2019,96,2202.45 +777,Francesco,Florence,fflorencelk@army.mil,Male,220.136.205.22,7/26/2019,20,3108.6 +778,Lorenzo,Sandhill,lsandhillll@cbc.ca,Male,59.175.249.79,9/30/2019,75,1913.25 +779,Eddy,Duchart,educhartlm@blinklist.com,Male,45.9.255.250,9/3/2019,23,2272.21 +780,Melisse,Corpe,mcorpeln@biblegateway.com,Female,177.26.210.63,10/20/2019,52,4203.4 +781,Clementia,Raggett,craggettlo@wikispaces.com,Female,66.66.204.21,10/17/2019,35,3945.21 +782,Constantine,Spaducci,cspaduccilp@upenn.edu,Male,63.181.82.150,8/21/2019,46,2543.36 +783,Durant,Andreix,dandreixlq@desdev.cn,Male,196.76.252.7,9/20/2019,14,4613.73 +784,Dilan,Jesty,djestylr@nature.com,Male,121.131.244.90,3/19/2019,89,4665.71 +785,Geralda,Zotto,gzottols@samsung.com,Female,203.26.209.205,4/15/2019,76,2964.31 +786,Greg,Goodings,ggoodingslt@instagram.com,Male,64.38.244.127,9/12/2019,92,4711.1 +787,Bailey,Stuchberry,bstuchberrylu@indiatimes.com,Male,26.71.81.206,12/16/2018,30,1848.55 +788,Mata,Wilkowski,mwilkowskilv@google.co.uk,Male,155.242.58.56,11/26/2019,85,4335.11 +789,Hamnet,Phillpotts,hphillpottslw@paypal.com,Male,7.182.159.232,6/16/2019,71,1440.95 +790,Dimitry,Capeloff,dcapelofflx@pinterest.com,Male,109.227.224.121,7/10/2019,80,3282.33 +791,Rafaello,Coggles,rcogglesly@prweb.com,Male,111.224.235.85,12/10/2018,16,4340.53 +792,Judith,Witul,jwitullz@issuu.com,Female,175.239.78.176,3/2/2019,1,4375.79 +793,Addy,Symson,asymsonm0@xing.com,Female,21.208.246.221,2/11/2019,82,1009.31 +794,Joey,Troak,jtroakm1@hhs.gov,Male,134.87.130.194,1/28/2019,60,1454.05 +795,Goldi,Currell,gcurrellm2@xinhuanet.com,Female,93.106.48.19,10/16/2019,65,1228.04 +796,Joaquin,Teodori,jteodorim3@github.com,Male,240.116.96.102,7/31/2019,71,1091.62 +797,Rollo,Embling,remblingm4@un.org,Male,56.201.58.245,3/29/2019,20,2170.46 +798,Mina,Blondel,mblondelm5@weibo.com,Female,98.87.116.35,7/18/2019,58,1320.41 +799,Meridel,Aries,mariesm6@technorati.com,Female,142.95.106.58,11/15/2019,87,2705.6 +800,Bret,Perrelle,bperrellem7@360.cn,Male,118.67.219.42,6/16/2019,87,3500.22 +801,Augustine,Hamments,ahammentsm8@oracle.com,Male,55.61.38.103,3/26/2019,8,1526.07 +802,Billie,Roo,broom9@tuttocitta.it,Female,219.152.107.102,3/14/2019,97,3539.6 +803,Susann,Pargiter,spargiterma@umich.edu,Female,147.205.25.119,6/27/2019,22,1160.81 +804,Glennis,Penella,gpenellamb@posterous.com,Female,192.52.234.179,11/2/2019,36,2548.63 +805,Mozelle,Densumbe,mdensumbemc@phpbb.com,Female,72.69.188.204,7/10/2019,51,4208.3 +806,Bernarr,Congrave,bcongravemd@gizmodo.com,Male,54.147.238.56,6/24/2019,10,3573.77 +807,Humfrid,Glauber,hglauberme@adobe.com,Male,88.115.210.97,2/26/2019,18,1504.77 +808,Suzie,Awmack,sawmackmf@com.com,Female,25.246.10.228,9/22/2019,58,1734.63 +809,Tanitansy,McDill,tmcdillmg@apache.org,Female,176.212.121.232,9/10/2019,91,3442.09 +810,Astrid,O'Henery,aohenerymh@cnbc.com,Female,114.120.10.56,5/9/2019,8,3084.41 +811,Pepita,Estrella,pestrellami@1688.com,Female,32.233.56.52,11/12/2019,14,4239.8 +812,Cornie,Jennaway,cjennawaymj@amazon.de,Male,193.210.211.129,6/10/2019,19,3412.2 +813,Barbette,Rudgard,brudgardmk@myspace.com,Female,176.253.137.53,2/4/2019,1,1136.15 +814,Timofei,Donner,tdonnerml@ed.gov,Male,123.83.191.246,3/26/2019,1,1133.51 +815,Vasily,Jeary,vjearymm@nationalgeographic.com,Male,98.99.97.55,5/7/2019,92,1187.51 +816,Jamey,Windsor,jwindsormn@nih.gov,Male,196.125.95.56,6/29/2019,71,3115.9 +817,Evangelina,Gorrie,egorriemo@theglobeandmail.com,Female,35.130.99.82,1/14/2019,79,2252.03 +818,Odie,Titchmarsh,otitchmarshmp@sina.com.cn,Male,172.30.187.143,6/12/2019,63,3327.2 +819,Hestia,Fresson,hfressonmq@hp.com,Female,195.31.239.116,7/16/2019,39,3541.73 +820,Mortimer,Bestwerthick,mbestwerthickmr@microsoft.com,Male,217.124.4.132,10/24/2019,19,4855.38 +821,Chaunce,Hechlin,chechlinms@goo.ne.jp,Male,197.140.52.246,7/29/2019,1,2219.75 +822,Odella,Casson,ocassonmt@sciencedaily.com,Female,40.211.128.59,3/16/2019,93,3085.53 +823,Shellysheldon,Pallent,spallentmu@youtu.be,Male,153.196.220.186,2/19/2019,19,2695.0 +824,Emelyne,Childes,echildesmv@livejournal.com,Female,126.181.2.21,11/6/2019,13,1095.75 +825,Fred,Calf,fcalfmw@chicagotribune.com,Female,240.193.197.180,11/17/2019,79,3914.36 +826,Dag,Mulliss,dmullissmx@4shared.com,Male,238.137.20.149,8/6/2019,4,3539.27 +827,Georgeanna,Pitcock,gpitcockmy@comsenz.com,Female,102.167.151.225,10/12/2019,84,4585.02 +828,Sunny,Panther,spanthermz@unicef.org,Male,151.172.118.190,1/12/2019,91,4343.08 +829,Belita,Alderson,baldersonn0@blogtalkradio.com,Female,137.76.226.221,1/10/2019,42,3569.01 +830,Ame,Forte,aforten1@sourceforge.net,Female,147.157.177.133,1/8/2019,21,1365.56 +831,Timmie,Rodden,troddenn2@businessweek.com,Male,130.254.151.248,3/26/2019,96,1881.36 +832,Ole,Schafer,oschafern3@about.com,Male,127.222.178.16,3/17/2019,17,2401.6 +833,Patty,Cooling,pcoolingn4@seesaa.net,Female,61.215.62.193,2/11/2019,48,2474.66 +834,Collin,Stute,cstuten5@goo.ne.jp,Male,130.45.80.94,9/10/2019,4,3847.7 +835,Yorgo,Swancott,yswancottn6@intel.com,Male,219.62.66.145,2/25/2019,65,2423.66 +836,Ron,Routley,rroutleyn7@spotify.com,Male,56.197.234.81,8/23/2019,33,2148.92 +837,Stefania,McSkin,smcskinn8@dot.gov,Female,98.31.14.146,3/16/2019,62,1990.6 +838,Salem,Lergan,slergann9@usnews.com,Male,15.104.246.221,6/13/2019,38,2449.21 +839,Dunstan,Kenzie,dkenziena@networksolutions.com,Male,87.134.108.52,11/9/2019,44,4224.03 +840,Hunt,Ledamun,hledamunnb@yahoo.com,Male,176.159.246.215,3/10/2019,86,3822.13 +841,Farrand,Callam,fcallamnc@paypal.com,Female,226.107.7.0,8/27/2019,74,2378.51 +842,Nada,Catlin,ncatlinnd@furl.net,Female,47.100.217.78,9/6/2019,80,4344.19 +843,Carlita,Glavis,cglavisne@thetimes.co.uk,Female,51.186.12.78,12/12/2018,10,1226.08 +844,Lianna,McColm,lmccolmnf@smh.com.au,Female,11.59.43.151,12/24/2018,75,2920.03 +845,Darill,Klempke,dklempkeng@bloomberg.com,Male,130.161.53.81,10/30/2019,26,1470.72 +846,Laughton,Whithalgh,lwhithalghnh@booking.com,Male,245.52.162.163,5/13/2019,95,3958.01 +847,Cammie,Izhakov,cizhakovni@admin.ch,Female,124.164.33.27,4/7/2019,43,2040.2 +848,Marline,Duddell,mduddellnj@opensource.org,Female,170.97.255.200,6/14/2019,35,2449.64 +849,Hillier,Caulder,hcauldernk@npr.org,Male,83.220.85.148,8/30/2019,1,3069.76 +850,Coral,Raffles,crafflesnl@friendfeed.com,Female,149.92.2.58,1/20/2019,13,1301.57 +851,Barb,Dudny,bdudnynm@fc2.com,Female,243.249.246.96,2/19/2019,2,4053.62 +852,Arch,Bente,abentenn@mapy.cz,Male,229.210.219.135,9/9/2019,41,4556.35 +853,Alix,Sheriff,asheriffno@jigsy.com,Female,203.28.108.229,1/30/2019,70,2074.89 +854,Vaughn,Schwaiger,vschwaigernp@webmd.com,Male,112.58.20.107,5/25/2019,5,2348.46 +855,Stearn,Linggood,slinggoodnq@multiply.com,Male,247.71.219.146,11/23/2019,5,3348.25 +856,Doralin,Kleinschmidt,dkleinschmidtnr@ca.gov,Female,30.114.11.63,9/21/2019,29,3475.53 +857,Ali,Treweke,atrewekens@xrea.com,Female,181.217.116.253,1/19/2019,50,4184.66 +858,Brandon,Ogger,boggernt@example.com,Male,19.9.219.173,12/21/2018,53,3270.85 +859,Garth,Sterre,gsterrenu@sina.com.cn,Male,7.170.103.142,12/30/2018,60,4285.48 +860,Elberta,Stooke,estookenv@goo.gl,Female,104.12.213.185,4/7/2019,60,1570.75 +861,Winfield,Peacock,wpeacocknw@liveinternet.ru,Male,214.134.25.83,2/21/2019,76,4428.25 +862,Ernest,Allabarton,eallabartonnx@home.pl,Male,148.170.100.93,4/25/2019,54,2410.98 +863,Florette,Pendle,fpendleny@google.co.uk,Female,163.75.98.87,3/3/2019,25,1877.39 +864,Enrica,Smogur,esmogurnz@jiathis.com,Female,212.86.14.17,10/30/2019,90,1736.18 +865,Selle,Meece,smeeceo0@va.gov,Female,216.204.181.172,12/31/2018,9,4254.26 +866,Leighton,Behninck,lbehnincko1@ted.com,Male,128.137.145.34,7/28/2019,8,4817.49 +867,Dallis,Kildahl,dkildahlo2@ucsd.edu,Male,57.50.158.153,11/29/2019,80,3940.06 +868,Carr,McKeachie,cmckeachieo3@cbsnews.com,Male,155.169.255.5,10/25/2019,92,1248.52 +869,Gwendolin,Twinborough,gtwinborougho4@engadget.com,Female,126.69.215.135,10/31/2019,60,1790.54 +870,Katha,Mains,kmainso5@wired.com,Female,108.80.188.101,9/5/2019,90,1953.27 +871,Anabella,Hawkridge,ahawkridgeo6@elegantthemes.com,Female,194.91.70.106,11/7/2019,33,1067.92 +872,Carney,Moxon,cmoxono7@about.me,Male,243.35.248.83,5/25/2019,46,3541.93 +873,Cynthy,Minette,cminetteo8@unesco.org,Female,198.115.128.127,11/13/2019,17,1202.6 +874,Humfrey,Doogue,hdoogueo9@businesswire.com,Male,118.100.123.208,9/24/2019,21,1883.88 +875,Rosabella,Frowen,rfrowenoa@who.int,Female,168.21.241.150,3/26/2019,12,3747.05 +876,Inger,Benoiton,ibenoitonob@bing.com,Female,62.18.91.144,2/5/2019,55,3668.36 +877,Aldus,McHardy,amchardyoc@google.com,Male,217.43.238.237,2/7/2019,25,3733.76 +878,Thea,Klemke,tklemkeod@aboutads.info,Female,13.193.63.194,9/1/2019,15,2199.75 +879,Nathaniel,Alesio,nalesiooe@prweb.com,Male,45.77.38.94,8/21/2019,88,1398.05 +880,Jo-ann,Thring,jthringof@plala.or.jp,Female,67.45.206.82,3/28/2019,23,1119.69 +881,Betteann,Andreone,bandreoneog@google.it,Female,28.46.236.211,9/21/2019,68,2637.19 +882,Leland,Boulding,lbouldingoh@booking.com,Male,223.0.133.33,6/26/2019,95,1645.04 +883,Rosalia,Sieve,rsieveoi@opera.com,Female,146.244.116.76,11/14/2019,20,3684.84 +884,Pietra,Lillyman,plillymanoj@sphinn.com,Female,241.84.132.180,1/28/2019,100,4262.03 +885,Cosme,Guest,cguestok@chronoengine.com,Male,200.190.189.24,4/16/2019,70,2178.29 +886,Wallie,Telega,wtelegaol@ebay.com,Male,121.243.189.159,4/30/2019,52,1182.3 +887,Lishe,Skillett,lskillettom@umn.edu,Female,195.58.225.2,3/5/2019,71,2012.1 +888,Arleta,Manley,amanleyon@linkedin.com,Female,51.84.250.183,10/15/2019,15,4912.75 +889,Ravi,Willder,rwillderoo@clickbank.net,Male,172.59.196.252,11/3/2019,61,1209.86 +890,Averell,Towner,atownerop@twitpic.com,Male,39.252.161.23,5/12/2019,64,4531.62 +891,Patrice,Dedman,pdedmanoq@marriott.com,Male,210.154.94.25,6/13/2019,74,4735.98 +892,Shem,Girtin,sgirtinor@is.gd,Male,104.118.74.161,11/10/2019,100,4267.43 +893,Francyne,Bransby,fbransbyos@g.co,Female,44.30.38.123,2/4/2019,46,4181.62 +894,Christye,Farbrace,cfarbraceot@hibu.com,Female,112.150.190.191,2/16/2019,23,2095.36 +895,Nadean,Given,ngivenou@spiegel.de,Female,151.241.243.171,4/25/2019,81,2443.82 +896,Cesya,Clutheram,cclutheramov@smh.com.au,Female,165.200.216.167,7/4/2019,31,3937.84 +897,Bax,Goodwins,bgoodwinsow@nhs.uk,Male,11.52.15.206,4/9/2019,91,2252.61 +898,Lewiss,Lamblot,llamblotox@hubpages.com,Male,47.131.77.115,8/30/2019,24,2400.36 +899,Red,Wick,rwickoy@photobucket.com,Male,83.184.219.168,10/21/2019,15,4178.01 +900,Salvatore,Pimblett,spimblettoz@cmu.edu,Male,167.151.230.214,5/15/2019,42,1341.9 +901,Hughie,Bryceson,hbrycesonp0@princeton.edu,Male,196.234.12.37,6/21/2019,62,1336.58 +902,Clayson,Hankinson,chankinsonp1@over-blog.com,Male,23.197.178.40,8/12/2019,99,1615.05 +903,Lilas,Liccardo,lliccardop2@wisc.edu,Female,122.30.29.218,11/8/2019,18,2646.47 +904,Orville,Durrant,odurrantp3@clickbank.net,Male,72.40.168.148,8/12/2019,37,1473.73 +905,Orrin,Skelly,oskellyp4@a8.net,Male,105.220.135.4,5/6/2019,50,1525.97 +906,Tallou,Sandyford,tsandyfordp5@homestead.com,Female,227.225.220.181,2/6/2019,58,4786.43 +907,Quincy,Roseman,qrosemanp6@biglobe.ne.jp,Male,54.163.190.213,7/1/2019,24,4037.23 +908,Tabbie,Kiraly,tkiralyp7@newsvine.com,Male,224.190.131.109,3/13/2019,100,1323.77 +909,Sandy,Matskevich,smatskevichp8@simplemachines.org,Female,2.231.87.52,12/29/2018,82,3517.0 +910,Duane,Dye,ddyep9@nasa.gov,Male,104.68.110.228,10/11/2019,72,3764.03 +911,Vassily,McNeill,vmcneillpa@eepurl.com,Male,224.20.3.98,5/2/2019,93,2130.67 +912,Donalt,Astlet,dastletpb@chron.com,Male,114.210.201.171,5/7/2019,75,2025.04 +913,Nickey,Stobie,nstobiepc@icio.us,Male,185.166.110.244,4/16/2019,71,1045.07 +914,Alejandra,Evenett,aevenettpd@spotify.com,Female,53.71.229.172,3/31/2019,68,3272.6 +915,Malia,Turpey,mturpeype@slashdot.org,Female,100.204.158.195,7/24/2019,23,1887.0 +916,Sancho,Coy,scoypf@unicef.org,Male,172.2.26.180,7/3/2019,57,3139.02 +917,Gabey,Skoyles,gskoylespg@epa.gov,Female,194.148.9.101,3/13/2019,84,2292.4 +918,Bertrando,Ivankin,bivankinph@fda.gov,Male,118.157.82.229,9/22/2019,3,1287.51 +919,Tannie,MacCosty,tmaccostypi@digg.com,Male,72.202.202.95,12/24/2018,82,2588.07 +920,Ranee,Bessent,rbessentpj@imgur.com,Female,127.24.169.67,11/10/2019,54,3444.15 +921,Tiffanie,Trawin,ttrawinpk@marketwatch.com,Female,17.85.226.126,5/24/2019,65,2725.46 +922,Almeria,Gillan,agillanpl@geocities.com,Female,122.181.145.74,11/21/2019,9,4518.15 +923,Myron,Cornuau,mcornuaupm@fema.gov,Male,119.235.159.166,8/23/2019,71,2151.88 +924,Julie,Garmston,jgarmstonpn@istockphoto.com,Female,239.238.200.150,1/14/2019,64,2838.43 +925,Piggy,Gobel,pgobelpo@fastcompany.com,Male,118.158.61.149,2/6/2019,34,3108.47 +926,Gib,Featherstone,gfeatherstonepp@infoseek.co.jp,Male,90.82.112.24,7/12/2019,72,1446.28 +927,Ezri,Roome,eroomepq@bandcamp.com,Male,162.179.9.53,11/8/2019,89,4420.45 +928,Mattias,Bilbie,mbilbiepr@jugem.jp,Male,26.98.219.102,6/29/2019,53,3055.7 +929,Tremain,Puleston,tpulestonps@newsvine.com,Male,57.241.217.97,2/19/2019,74,4113.34 +930,Penrod,Human,phumanpt@amazon.co.jp,Male,191.23.222.227,8/12/2019,14,3914.95 +931,Helyn,Ladell,hladellpu@a8.net,Female,20.100.134.221,4/17/2019,94,2293.8 +932,Enid,Rough,eroughpv@imageshack.us,Female,107.222.42.7,6/17/2019,21,1914.06 +933,Eloise,Rumbold,erumboldpw@meetup.com,Female,43.79.13.253,12/16/2018,59,4639.52 +934,Sol,Merricks,smerrickspx@ow.ly,Male,123.39.195.179,11/1/2019,57,2899.49 +935,Mahmud,Keeney,mkeeneypy@wikispaces.com,Male,72.102.171.49,3/11/2019,6,4225.54 +936,Asia,Bartlosz,abartloszpz@mtv.com,Female,243.106.58.70,12/21/2018,69,1884.34 +937,Christian,Stirman,cstirmanq0@discuz.net,Female,107.80.178.46,8/29/2019,55,2956.9 +938,Alfredo,Adnams,aadnamsq1@washington.edu,Male,37.204.15.227,12/6/2018,15,2349.12 +939,Rudy,Diggins,rdigginsq2@nsw.gov.au,Male,136.105.142.55,10/22/2019,56,4720.66 +940,Valaria,Paulus,vpaulusq3@etsy.com,Female,109.100.3.110,1/2/2019,26,1610.79 +941,Conny,Roels,croelsq4@mozilla.com,Female,208.213.210.29,1/31/2019,54,4096.32 +942,Bucky,Kennan,bkennanq5@pinterest.com,Male,172.225.189.229,8/19/2019,87,4628.59 +943,Iago,Samwyse,isamwyseq6@github.io,Male,89.18.109.205,11/10/2019,60,4746.45 +944,Pietrek,Gori,pgoriq7@reverbnation.com,Male,92.121.178.154,11/5/2019,33,1692.9 +945,Tabina,Kears,tkearsq8@theglobeandmail.com,Female,144.45.170.150,10/3/2019,52,3611.17 +946,Wendye,Sollis,wsollisq9@walmart.com,Female,238.248.64.91,5/1/2019,5,3395.62 +947,Rafferty,Waycot,rwaycotqa@yellowbook.com,Male,56.12.230.243,10/22/2019,65,3819.8 +948,Bendix,Heamus,bheamusqb@adobe.com,Male,249.250.105.13,1/13/2019,1,3871.34 +949,Elfrieda,Gierardi,egierardiqc@uiuc.edu,Female,70.40.147.33,5/15/2019,33,3013.33 +950,Malachi,Sweet,msweetqd@soup.io,Male,45.89.78.169,12/12/2018,80,3668.82 +951,Lanae,Pollendine,lpollendineqe@nasa.gov,Female,95.61.177.183,2/13/2019,1,4889.44 +952,Ryley,Lawry,rlawryqf@scientificamerican.com,Male,125.229.131.211,1/14/2019,2,3286.34 +953,Terri-jo,Jamblin,tjamblinqg@epa.gov,Female,188.157.97.186,6/5/2019,47,2943.87 +954,Leland,Hildred,lhildredqh@scribd.com,Male,225.216.250.143,6/17/2019,56,4864.96 +955,Gabe,Olyff,golyffqi@nba.com,Male,132.213.9.245,4/23/2019,98,1702.76 +956,Madelena,Astling,mastlingqj@samsung.com,Female,30.58.29.118,12/18/2018,84,4673.11 +957,Reinaldo,Kitchingman,rkitchingmanqk@csmonitor.com,Male,188.111.118.234,5/23/2019,75,4450.61 +958,Padgett,Spacey,pspaceyql@google.de,Male,36.237.235.122,11/27/2019,56,1279.87 +959,Rachel,Chezelle,rchezelleqm@domainmarket.com,Female,96.79.189.100,11/24/2019,91,3136.13 +960,Pollyanna,Eggerton,peggertonqn@nsw.gov.au,Female,18.201.58.237,12/19/2018,20,3493.5 +961,Marris,Wincott,mwincottqo@bandcamp.com,Female,184.31.11.215,6/12/2019,8,1515.01 +962,Shae,Mair,smairqp@friendfeed.com,Male,62.3.75.126,5/26/2019,17,1886.62 +963,Rik,Citrine,rcitrineqq@ameblo.jp,Male,185.79.147.211,7/2/2019,34,4809.78 +964,Clare,Bohey,cboheyqr@wunderground.com,Female,107.176.103.42,5/11/2019,88,4309.81 +965,Hayes,Brushfield,hbrushfieldqs@i2i.jp,Male,2.5.51.42,11/26/2019,76,1551.99 +966,Misty,Domico,mdomicoqt@google.pl,Female,39.47.212.202,12/6/2018,70,4670.57 +967,Arthur,Elstub,aelstubqu@independent.co.uk,Male,176.248.255.191,12/30/2018,46,2705.52 +968,Berkeley,Hapgood,bhapgoodqv@technorati.com,Male,178.208.39.27,10/14/2019,22,3380.97 +969,Brose,Kynton,bkyntonqw@arstechnica.com,Male,46.20.210.218,3/5/2019,56,4943.64 +970,Tarah,Deguara,tdeguaraqx@netvibes.com,Female,218.157.37.87,10/15/2019,52,4735.44 +971,Lorie,Tooting,ltootingqy@e-recht24.de,Female,180.29.70.96,5/13/2019,2,3053.29 +972,Consuela,Lyndon,clyndonqz@nature.com,Female,33.99.22.111,10/26/2019,16,1782.96 +973,Heloise,Loges,hlogesr0@ebay.co.uk,Female,65.91.147.32,8/6/2019,91,3905.33 +974,Lamond,Hamnet,lhamnetr1@tripod.com,Male,59.5.218.194,3/21/2019,78,3783.64 +975,Matilda,Clemencet,mclemencetr2@livejournal.com,Female,50.223.87.130,10/4/2019,23,2543.68 +976,Bayard,Roskruge,broskruger3@sitemeter.com,Male,69.240.99.87,7/3/2019,80,1587.29 +977,Benn,Jirak,bjirakr4@delicious.com,Male,128.35.209.194,12/2/2018,44,3277.87 +978,Nahum,Attle,nattler5@tiny.cc,Male,112.43.114.140,6/8/2019,7,2273.81 +979,Evangeline,Ribey,eribeyr6@google.fr,Female,81.105.199.64,3/29/2019,67,4510.15 +980,Ryann,Ellul,rellulr7@ning.com,Female,240.217.190.216,12/13/2018,84,1668.81 +981,Ermina,Venn,evennr8@de.vu,Female,133.154.240.217,2/13/2019,97,1524.47 +982,Toby,Durward,tdurwardr9@state.tx.us,Male,121.122.162.100,6/18/2019,93,2462.12 +983,Frederic,Scraney,fscraneyra@gnu.org,Male,169.185.152.85,9/1/2019,5,4899.03 +984,Marty,Phebey,mphebeyrb@tripadvisor.com,Male,168.107.107.230,12/6/2018,84,4793.66 +985,Harris,Lockney,hlockneyrc@nifty.com,Male,93.129.49.110,8/4/2019,49,1798.63 +986,Eugen,Leverton,elevertonrd@instagram.com,Male,159.232.54.24,9/21/2019,60,3305.1 +987,Gabie,Beardwood,gbeardwoodre@theatlantic.com,Male,20.18.125.82,1/24/2019,5,1946.11 +988,Myrwyn,Pesterfield,mpesterfieldrf@scientificamerican.com,Male,145.133.177.91,8/13/2019,25,3808.27 +989,Mercy,Chadd,mchaddrg@51.la,Female,185.164.11.233,6/3/2019,40,4001.54 +990,Cornelle,Conford,cconfordrh@behance.net,Female,21.225.190.101,1/23/2019,37,3044.81 +991,Maynord,Till,mtillri@youtube.com,Male,152.132.239.240,7/13/2019,100,1128.62 +992,Evelin,Dmitrichenko,edmitrichenkorj@weibo.com,Male,209.57.214.249,7/17/2019,97,1964.24 +993,Quincy,Rupel,qrupelrk@github.io,Male,64.185.162.129,7/17/2019,98,3247.38 +994,Althea,Ternault,aternaultrl@hao123.com,Female,67.166.119.252,9/5/2019,71,1679.32 +995,Tracy,Usmar,tusmarrm@archive.org,Female,218.147.181.108,4/29/2019,13,4937.95 +996,Frank,Thebeaud,fthebeaudrn@phpbb.com,Male,80.246.35.156,3/21/2019,91,3229.53 +997,Kerk,Feathersby,kfeathersbyro@i2i.jp,Male,216.252.23.125,4/2/2019,38,2610.64 +998,Brigid,Worvell,bworvellrp@cnn.com,Female,48.50.178.158,5/19/2019,35,1974.9 +999,Ike,Van der Merwe,ivandermerwerq@craigslist.org,Male,103.61.116.181,8/14/2019,56,2115.14 +1000,Mark,Dallison,mdallisonrr@cdc.gov,Male,187.245.203.40,8/8/2019,73,1286.22 diff --git a/scripts/accum_pattern_multiple-scrabble.py b/scripts/accum_pattern_multiple-scrabble.py new file mode 100644 index 0000000..8fd9a4e --- /dev/null +++ b/scripts/accum_pattern_multiple-scrabble.py @@ -0,0 +1,30 @@ + # -*- coding: utf-8 -*- +""" +Created on Thu Jun 6 16:11:59 2019 + +@author: http://ice-web.cc.gatech.edu/ce21/1/static/audio/static/pip/Dictionaries/dictionary_accum.html#scarlet.txt +""" + +f = open('study_in_scarlet.txt', 'r', encoding='utf8') +txt = f.read() +f.close() +# now txt is one long string containing all the characters +x = {} # start with an empty dictionary +for c in txt: + if c not in x: + # we have not seen this character before, so initialize a counter for it + x[c] = 0 + + #whether we've seen it before or not, increment its counter + x[c] = x[c] + 1 + +# build a second dictionary, containing the scrabble scores for individual characters +# calculate the scrabble score of your text +letter_values = {'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f':4, 'g': 2, 'h':4, 'i':1, 'j':8, 'k':5, 'l':1, 'm':3, 'n':1, 'o':1, 'p':3, 'q':10, 'r':1, 's':1, 't':1, '':1, 'v':8, 'w':4, 'x':8, 'y':4, 'z':10} + +tot = 0 +for y in x: + if y in letter_values: + tot = tot + letter_values[y] * x[y] + +print('total scrabble score = ', tot) diff --git a/scripts/accum_pattern_multiple.py b/scripts/accum_pattern_multiple.py new file mode 100644 index 0000000..2cbaedd --- /dev/null +++ b/scripts/accum_pattern_multiple.py @@ -0,0 +1,22 @@ +h# -*- coding: utf-8 -*- +""" +Created on Thu Jun 6 15:31:20 2019 + +@author: http://ice-web.cc.gatech.edu/ce21/1/static/audio/static/pip/Dictionaries/dictionary_accum.html#scarlet.txt +""" + +f = open('study_in_scarlet.txt', 'r', encoding="utf8") +txt = f.read() +f.close() +# now txt is one long string containing all the characters +x = {} # start with an empty dictionary +for c in txt: + if c not in x: + # we have not seen this character before, so initialize a counter for it + x[c] = 0 + + #whether we've seen it before or not, increment its counter + x[c] = x[c] + 1 + +for c in x.keys(): + print(c + ": " + str(x[c]) + " occurrences") \ No newline at end of file diff --git a/scripts/accum_pattern_single.py b/scripts/accum_pattern_single.py new file mode 100644 index 0000000..ec0983f --- /dev/null +++ b/scripts/accum_pattern_single.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 6 14:47:11 2019 + +@author: http://ice-web.cc.gatech.edu/ce21/1/static/audio/static/pip/Iteration/iteration.html +""" + +nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] +accum = 0 +for w in nums: + accum = accum + w + print(accum) + diff --git a/scripts/accum_pattern_single_text.py b/scripts/accum_pattern_single_text.py new file mode 100644 index 0000000..7e7c546 --- /dev/null +++ b/scripts/accum_pattern_single_text.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 6 15:02:14 2019 + +@author: http://ice-web.cc.gatech.edu/ce21/1/static/audio/static/pip/Dictionaries/dictionary_accum.html#scarlet.txt +""" + +#f = open('study_in_scarlet.txt', 'r', encoding="utf8") +f = open('Data\patients.txt', 'r') +txt = f.read() +# now txt is one long string containing all the characters +t_count = 0 #initialize the accumulator variable + +# check the number of occurences of t +for ch in txt: + if ch == 't': + t_count = t_count + 1 #increment the counter +print("t: " + str(t_count) + " occurrences") +f.close() + + +# a more elaborted loop for 2 characters +# a lot of elif's required to test for all 26 characters +t_count = 0 #initialize the accumulator variable +s_count = 0 # initialize the s counter accumulator as well +for ch in txt: + if ch == 't': + t_count = t_count + 1 #increment the t counter + elif ch == 's': + s_count = s_count + 1 +print("t: " + str(t_count) + " occurrences") +print("s: " + str(s_count) + " occurrences") +f.close() diff --git a/scripts/actors.csv b/scripts/actors.csv new file mode 100644 index 0000000..b82ba4c --- /dev/null +++ b/scripts/actors.csv @@ -0,0 +1,5 @@ +First Name,Last Name,Date of Birth +Tom,Cruise,"July 3, 1962" +Bruce,Willis,"March 19, 1955" +Morgan,Freeman,"June 1, 1937" +John,Wayne,"May 26, 1907" \ No newline at end of file diff --git a/scripts/bitwise.py b/scripts/bitwise.py new file mode 100644 index 0000000..b8a165d --- /dev/null +++ b/scripts/bitwise.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 14 11:19:09 2018 +bitwise + +@author: u0015831 +""" +x = 10 +print(bin(x)) + +y = 13 +print(bin(y)) + +print('x&y',x&y) +print('x&y',bin(x&y)) + +print('x|y',x|y) +print('x|y',bin(x|y)) + +print('x', x) +print('x<<2',x<<2) +print('x<<2',bin(x<<2)) + +print('x', x) +print('x>>1',x>>1) +print('x>>1',bin(x>>1)) + +print('x', x) +print('~x',~x) +print('~x',bin(~x)) \ No newline at end of file diff --git a/scripts/block_code.py b/scripts/block_code.py new file mode 100644 index 0000000..0fae151 --- /dev/null +++ b/scripts/block_code.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Feb 21 10:06:18 2022 + +@author: u0015831 +""" + +from math import sqrt + +my_list = [1, 2, 3, 4] +result = 0 +for i in my_list: + if i%2 == 0: + result = result + sqrt(i) + print(result) \ No newline at end of file diff --git a/scripts/check_variable_object.py b/scripts/check_variable_object.py new file mode 100644 index 0000000..e4a0f4d --- /dev/null +++ b/scripts/check_variable_object.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +""" +check variable as an object + +@author: u0015831 +""" + +a = 4.5 +b = 56 +c = 'Hello' +d = a + b + +print(a, b, c, d) + +print (type(a), type(b), type(c), type(d)) + +print(dir(a)) +print('is a integer?', a.is_integer()) diff --git a/scripts/check_whitespace.py b/scripts/check_whitespace.py new file mode 100644 index 0000000..a5d1c2b --- /dev/null +++ b/scripts/check_whitespace.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 14 10:34:11 2018 +check the White Space! +@author: u0015831 +""" +#%% +for i in range(3): + print("Hello") + print("Bye!") + +#%% +# for i in range(10): +# if i%2 == 1: +# print (0.1*i) +# print ('bla') +# print('end block') + +#%% +# for i in range(10): +# if i%2 == 1: +# print (0.1*i) +# print ('bla') +# print('end block') + +#%% +# for i in range(10): +# if i%2 == 1: +# print (0.1*i) +# print('bla') +# print('end block') \ No newline at end of file diff --git a/scripts/command_line_io.py b/scripts/command_line_io.py new file mode 100644 index 0000000..b67ad56 --- /dev/null +++ b/scripts/command_line_io.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Nov 7 13:22:42 2019 + +command line IO + +example command line: python command_line_io.py o m g +example in Spyder: run command_line_io.py o m g + +@author: u0015831 +""" + +import sys +print('This is the name of the script: ', sys.argv[0]) +print('Number of arguments: ', len(sys.argv)) +print('The arguments are: ', sys.argv) + + +for arg in sys.argv: + print(type(arg), arg) \ No newline at end of file diff --git a/scripts/convert_from_decimal.py b/scripts/convert_from_decimal.py new file mode 100644 index 0000000..4b9dd2c --- /dev/null +++ b/scripts/convert_from_decimal.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Aug 29 09:51:15 2019 +taken from Marina von Steinkirch + +@author: u0015831 +""" + +def convert_to_decimal(number, base): + multiplier, result = 1, 0 + while number > 0: + result += number%10*multiplier + multiplier *= base + number = number//10 + return result + +def test_convert_to_decimal(): + number, base = 1001, 2 + assert(convert_to_decimal(number, base) == 9) + +def test_convert_from_decimal(): + number, base = 9, 2 + assert(convert_from_decimal(number, base) == 1001) \ No newline at end of file diff --git a/scripts/convert_to_decimal.py b/scripts/convert_to_decimal.py new file mode 100644 index 0000000..3dd17d2 --- /dev/null +++ b/scripts/convert_to_decimal.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Aug 29 09:51:15 2019 +taken from Marina von Steinkirch + +@author: u0015831 +""" + +def convert_to_decimal(number, base): + multiplier, result = 1, 0 + while number > 0: + result += number%10*multiplier + multiplier *= base + number = number//10 + return result + +def test_convert_to_decimal(): + number, base = 1001, 2 + print ('number = ', number) + print ('base = ', base) + assert(convert_to_decimal(number, base) == 9), 'result must equal 9' + diff --git a/scripts/create_lists.py b/scripts/create_lists.py new file mode 100644 index 0000000..7d3a950 --- /dev/null +++ b/scripts/create_lists.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 23 11:52:06 2019 + +create lists + +""" +# classic approach +squares = [] +for i in range(10): + squares.append(i * i) +squares + +# list comprehension +squares = [i * i for i in range(10)] +squares \ No newline at end of file diff --git a/scripts/deck.py b/scripts/deck.py new file mode 100644 index 0000000..0babfa5 --- /dev/null +++ b/scripts/deck.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- + +import random + + +def generate_deck(): + """Return a list containing the cards in a standard, 52-card deck.""" + suits = ["Spades", "Hearts", "Diamonds", "Clubs"] + ranks = ["2", "3", "4", "5", "6", "7", "8", "9", "10", + "Jack", "Queen", "King", "Ace"] + deck = [] + for s in suits: + for r in ranks: + name = "%s of %s" % (r, s) + deck.append(name) + return deck + + +def draw_random(deck): + """Draw a random card from the given deck.""" + n = len(deck) + i = random.randint(0, n) + k = draw_card(deck, i) + return k + + +def draw_card(deck, x): + """Return card no. x from the deck.""" + return deck[x] + + +def test_with_replacement(): + print("Generating deck") + deck = generate_deck() + + number = 10 + print("Drawing", number, "random cards with replacement:") + drawn = set() + for x in range(number): + card = draw_random(deck) + print(" You drew", card) + drawn.add(card) + unique = len(drawn) + print("Drew", unique, "different cards") + + +test_with_replacement() \ No newline at end of file diff --git a/scripts/demo_mix.xlsx b/scripts/demo_mix.xlsx new file mode 100644 index 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za8D&8cm4FOO2fv6M7yH=lR_}~+x7Fs;B5&#;#8HASORiwR7MGCoLPF*ZmHOo6Y3b~ zTlACe@8)KllZujZSM+&WAK_4vU$ya(^%uG8Hc)ztpw#gZo+Htsunptz#*(!5SexVe zNei(@H_|F~73MK6Jxl@~5lLhZBVSNsbzdBQHMh(?LcT3yCfB3TJ6pM77U@!~<3{Ld z9SOHKNJpSw9@`dIf_EFyDJBV_%<=yYVw>jwFB{fwoh|8nCAC;hTfgFlR{ ze^MFl0V^z5{C@MpUw_?S<8QV?sL21*z&{(be*u4vnXpU#tzCN`_|K-*-+}Eg$>RTS zVcoZLzlHJ3(hW@ayVuaT556yA{{r*E`Xw;%FG>48^uD_I3pxu68~?fPzqH2tR_;qQ zzpTt*|6%36U~}KV{rdQqfh@w`PUDYi`M#C=g~Tr_4KVHK=Q`XkEACtPXM+BP2LSS6 q;_ScD^?mq1gX-Vm>=b{4|0l$%$Roly@^eQ38lW3i5nNOKeD{AYFXBD` literal 0 HcmV?d00001 diff --git a/scripts/dictionaries_1.py b/scripts/dictionaries_1.py new file mode 100644 index 0000000..8739f6a --- /dev/null +++ b/scripts/dictionaries_1.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 21 15:58:30 2018 + +dictionaries + +@author: u0015831 +""" + +#A few examples of a dictionary + +#First way to setup a dictionary +zipdict = {} + +#Add a couple of cities to the dictionary +zipdict['Heverlee'] = 3001 +zipdict['Leuven'] = 3000 +zipdict['Brussel'] = 1000 +zipdict['Gent'] = 9000 + +# another way +zipdict.update({'Antwerpen': 2000, 'Brugge': 8000}) + +#Use the function key() - +if 'Gent' in zipdict: + print('Gent is in the dictionary. ZIP: ', zipdict['Gent']) +else: + print('Gent is not in the dictionary') + +#Use the function keys() - +keys = zipdict.keys() +print(keys) +#keys.sort() will this work? +#print(keys) + +# List of codes in a list: +values = zipdict.values() +print(values) +#values.sort() +#print(values) + diff --git a/scripts/fibonacci.py b/scripts/fibonacci.py new file mode 100644 index 0000000..ff5e55e --- /dev/null +++ b/scripts/fibonacci.py @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Aug 29 11:02:31 2019 +taken from Marina von Steinkirch + +@author: u0015831 +""" + +def fib_generator(): + a, b = 0, 1 + print(a, b) + while True: + print(b) + yield b + a, b = b, a+b + +def fib(n): + ''' + >>> fib(2) + 1 + >>> fib(5) + 5 + >>> fib(7) + 13 + ''' + if n < 3: + return 1 + a, b = 0, 1 + count = 1 + while count < n: + print(count) + count += 1 + a, b = b, a+b + return b + + +def fib_rec(n): + ''' + >>> fib_rec(2) + 1 + >>> fib_rec(5) + 5 + >>> fib_rec(7) + 13 + ''' + if n < 3: + return 1 + return fib_rec(n - 1) + fib_rec(n - 2) \ No newline at end of file diff --git a/scripts/file_basic_input.py b/scripts/file_basic_input.py new file mode 100644 index 0000000..1c2ab3c --- /dev/null +++ b/scripts/file_basic_input.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 5 15:12:20 2019 + +processing a file in a for-loop + +@author: u0015831 +""" + +txtfile = open("test_text.txt","r") + +for aline in txtfile: + values = aline.split() + if len(values) > 10: + print('some text', values[0], values[1], ' and also ', values[10] ) + +txtfile.close() diff --git a/scripts/file_io_2.py b/scripts/file_io_2.py new file mode 100644 index 0000000..275ec69 --- /dev/null +++ b/scripts/file_io_2.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 13:44:00 2018 + +@author: u0015831 +""" + +my_file=open('lines_10.txt','r') + +fa = my_file.read() +print(fa) + +my_file.seek(0) # return handle to first position +f3 = my_file.read(3) +print(f3) + +# use readline +frl = my_file.readline() +print(frl) diff --git a/scripts/file_io_3.py b/scripts/file_io_3.py new file mode 100644 index 0000000..cdcd266 --- /dev/null +++ b/scripts/file_io_3.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 14:16:29 2018 + +@author: u0015831 +""" + +fin=open('test_text.txt','r') +for line in fin: + line_in = line.rstrip('\r\n') #remove end of line + data = line_in.split() #split line of text + print(data) diff --git a/scripts/file_io_forLoop.py b/scripts/file_io_forLoop.py new file mode 100644 index 0000000..1c2ab3c --- /dev/null +++ b/scripts/file_io_forLoop.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 5 15:12:20 2019 + +processing a file in a for-loop + +@author: u0015831 +""" + +txtfile = open("test_text.txt","r") + +for aline in txtfile: + values = aline.split() + if len(values) > 10: + print('some text', values[0], values[1], ' and also ', values[10] ) + +txtfile.close() diff --git a/scripts/file_io_header.py b/scripts/file_io_header.py new file mode 100644 index 0000000..182a6eb --- /dev/null +++ b/scripts/file_io_header.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jun 3 10:09:14 2019 + +@author: taken from http://www2.hawaii.edu/~takebaya/cent110/file_in/file_in.html +""" + +infile = open("students_header.txt","r") +line1 = infile.readline() +lines = infile.readlines() +infile.close() +outfile = open("data.html","w") +outfile.write("\n \n") +outfile.write(' \n') +outfile.write(' \n') +headers = line1.strip().split(",") +outfile.write(' ') +for header in headers: + outfile.write(' ') +outfile.write('\n \n') +for line in lines: + tokens = line.strip().split(",") + outfile.write(' \n') + outfile.write(' ') + for token in tokens: + outfile.write(' ') + outfile.write('\n \n') +outfile.write('
' + header + '
' + token + '
\n') +outfile.write(' \n') +outfile.close() \ No newline at end of file diff --git a/scripts/file_io_readline.py b/scripts/file_io_readline.py new file mode 100644 index 0000000..009d590 --- /dev/null +++ b/scripts/file_io_readline.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 5 15:12:20 2019 + +processing a file with readline +the file is read into a list + +@author: u0015831 +""" + +txtfile = open("test_text.txt","r") + +aline = txtfile.readline() +values = aline.split() + +while aline: + if len(values) > 10: + print('some text', values[0], values[1], ' and also ', values[10] ) + aline = txtfile.readline() + values = aline.split() + +txtfile.close() diff --git a/scripts/file_io_readlines.py b/scripts/file_io_readlines.py new file mode 100644 index 0000000..ff6be8d --- /dev/null +++ b/scripts/file_io_readlines.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 5 15:12:20 2019 + +processing a file with readlines +the file is read into a list + +@author: u0015831 +""" + +txtfile = open("test_text.txt","r") +lines = txtfile.readlines() +txtfile.close() + +for aline in lines: + values = aline.split() + if len(values) > 10: + print('some text', values[0], values[1], ' and also ', values[10] ) + diff --git a/scripts/file_io_readlines_1.py b/scripts/file_io_readlines_1.py new file mode 100644 index 0000000..bc679f2 --- /dev/null +++ b/scripts/file_io_readlines_1.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jun 3 09:45:37 2019 + +@author: taken from http://www2.hawaii.edu/~takebaya/cent110/file_in/file_in.html +""" + +infile = open("students.txt","r") +lines = infile.readlines() +infile.close() +for line in lines: + tokens = line.split(",") + total = 0 + for i in range(1, len(tokens)): + total = total + float(tokens[i]) + average = total/(len(tokens) - 1) + print(tokens[0],"has an average of: ",average) \ No newline at end of file diff --git a/scripts/file_io_write_1.py b/scripts/file_io_write_1.py new file mode 100644 index 0000000..49883ca --- /dev/null +++ b/scripts/file_io_write_1.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 11:04:44 2018 + +@author: u0015831 +""" + +fp = open('testfile-write.txt','w') + +fp.write('Hello World') +fp.write('Hello World \n') + +# write the list +# note, here print is used +# It inserts spaces between arguments and appends the line terminator. +l = [0, 1, 2, 3] +print(l, file=fp) +for i in l: + print(i, file=fp) + +for i in l: + print(i, end=' ', file=fp) + + +# write the individual elements +for i in l: + fp.write(str(i)) + +# write the individual elements with a separator +for i in l: + fp.write(str(i) + '/') + +fp.write('\n') + +fp.close() diff --git a/scripts/file_io_writelines_1.py b/scripts/file_io_writelines_1.py new file mode 100644 index 0000000..3f1b432 --- /dev/null +++ b/scripts/file_io_writelines_1.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 11:04:44 2018 + +@author: u0015831 +""" + +fp = open('testfile-writelines.txt','w') + +fp.write('Hello World') +fp.write('Hello World \n') + +# write the list +l = [1, 2, 3] +l_str = map(str, l) +fp.writelines(l_str) + + +fp.close() + diff --git a/scripts/file_read_csv_dict.py b/scripts/file_read_csv_dict.py new file mode 100644 index 0000000..cdc16ad --- /dev/null +++ b/scripts/file_read_csv_dict.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 24 17:35:22 2019 + +source: https://www.alexkras.com/how-to-read-csv-file-in-python/ + +""" + +import csv +with open('MOCK_DATA.csv') as f: + reader = csv.DictReader(f) + data = [r for r in reader] + +print('type(data): ', type(data)) +print(data[0]['id']) \ No newline at end of file diff --git a/scripts/file_read_csv_list.py b/scripts/file_read_csv_list.py new file mode 100644 index 0000000..3f16f9c --- /dev/null +++ b/scripts/file_read_csv_list.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 24 17:32:57 2019 + +source: https://www.alexkras.com/how-to-read-csv-file-in-python/ +""" + +import csv +with open('MOCK_DATA.csv') as f: + reader = csv.reader(f) + next(reader) # skip header + data = [] + for row in reader: + data.append(row) \ No newline at end of file diff --git a/scripts/file_read_from_csv.py b/scripts/file_read_from_csv.py new file mode 100644 index 0000000..0b9720c --- /dev/null +++ b/scripts/file_read_from_csv.py @@ -0,0 +1,15 @@ +from csv import Sniffer, DictReader + +file_name = 'people_data.csv' +with open(file_name, 'rb') as csv_file: + dialect = Sniffer().sniff(csv_file.read(1024)) + csv_file.seek(0) + sum = 0.0 + csv_reader = DictReader(csv_file, fieldnames=None, + restkey='rest', restval=None, + dialect=dialect) + for row in csv_reader: + print('{name} --- {weight}'.format(name=row['name'], + weight=row['weight'])) + sum += float(row['weight']) + print('sum = {0}'.format(sum)) diff --git a/scripts/file_squares.txt b/scripts/file_squares.txt new file mode 100644 index 0000000..08b3b28 --- /dev/null +++ b/scripts/file_squares.txt @@ -0,0 +1,10 @@ +0: 0 +1: 1 +2: 4 +3: 9 +4: 16 +5: 25 +6: 36 +7: 49 +8: 64 +9: 81 diff --git a/scripts/file_write_csv.py b/scripts/file_write_csv.py new file mode 100644 index 0000000..a6638aa --- /dev/null +++ b/scripts/file_write_csv.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 24 17:43:58 2019 + +https://www.tutorialspoint.com/reading-and-writing-csv-file-using-python +""" + +# use writerow +import csv +csvData = [('Peter', '22'), ('Jasmine', '21'), ('Sam', '24')] +csvFile = open('person.csv', 'w', newline='') +obj=csv.writer(csvFile) + +for person in csvData: + obj.writerow(person) + +csvFile.close() + +#use writerows +csvfile = open('persons_bis.csv','w', newline='') +obj = csv.writer(csvfile) +obj.writerows(csvData) +csvFile.close() diff --git a/scripts/first_1.py b/scripts/first_1.py new file mode 100644 index 0000000..a3f3d3a --- /dev/null +++ b/scripts/first_1.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +""" +Spyder Editor + +This is a temporary script file. +""" + +a = 1 +b = 2.2 +c = a + b +print(c) diff --git a/scripts/first_15.py b/scripts/first_15.py new file mode 100644 index 0000000..2d75e78 --- /dev/null +++ b/scripts/first_15.py @@ -0,0 +1,4 @@ +a = 1 +b = 3.3 +c = a + b +print(c, a, b) \ No newline at end of file diff --git a/scripts/first_prog_1.py b/scripts/first_prog_1.py new file mode 100644 index 0000000..f2b8f83 --- /dev/null +++ b/scripts/first_prog_1.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +""" + +@author: u0015831 +""" + +a = 8 +b = 9.9 +c = a + b +print('c = ', c) + +#%% +a = 88 +d = 9.76 +r1 = a * d +print('r1 =', r1) + +# ============================================================================= +# +# ============================================================================= +if (a > 50): + print('a is larger than 50') +else: + print('a is smaller or equal to 50') + + \ No newline at end of file diff --git a/scripts/first_sum.py b/scripts/first_sum.py new file mode 100644 index 0000000..938fb48 --- /dev/null +++ b/scripts/first_sum.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 25 09:48:16 2019 + +@author: u0015831 +""" + +a = 1 +b = 7 +c = a + b +print(a+b) + +if (a > 6): + print('a larger than 6') + diff --git a/scripts/for_loop_1.py b/scripts/for_loop_1.py new file mode 100644 index 0000000..93e79e5 --- /dev/null +++ b/scripts/for_loop_1.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 17 16:51:05 2019 +calculate the average of a list of numbers +@author: u0015831 +""" + +#rli = range(1, 10, 2) +#for i in rli: +# print('i=', i) + + +sum=0 +numbers =[1,22,31,45] +for num in numbers: + print(num) + sum=sum+num +avg=sum/len(numbers) +print ('Average:', avg) + + \ No newline at end of file diff --git a/scripts/for_loop_2.py b/scripts/for_loop_2.py new file mode 100644 index 0000000..341d923 --- /dev/null +++ b/scripts/for_loop_2.py @@ -0,0 +1,2 @@ +for i in range(1, 101): + print(i) \ No newline at end of file diff --git a/scripts/for_loop_else.py b/scripts/for_loop_else.py new file mode 100644 index 0000000..ff680fe --- /dev/null +++ b/scripts/for_loop_else.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 12 13:57:24 2023 + +@author: u0015831 +""" + +numbers =[1,2,3,4] +for i in numbers: + print(i) + if i > 2: # try with 8 + break +else: # Not executed as there is a break + print("No Break") diff --git a/scripts/for_loop_index.py b/scripts/for_loop_index.py new file mode 100644 index 0000000..ca240f4 --- /dev/null +++ b/scripts/for_loop_index.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 20 15:57:29 2018 +for loop with index +@author: u0015831 +""" +fruitslist = ['banana', 'apple', 'mango'] +for i in range(len(fruitslist)): + print("Color {}: {}".format(i + 1, fruitslist[i])) +print +# use of enumerate +for line in enumerate(fruitslist, start=1): + print(line) +print# use of enumerate +for num, color in enumerate(fruitslist, start=1): + print("Color {}: {}".format(num, color)) +print +# is this working? +fruitset = {'banana', 'apple', 'mango'} +for num, color in enumerate(fruitset, start=1): + print("Color {}: {}".format(num, color)) \ No newline at end of file diff --git a/scripts/for_loop_list.py b/scripts/for_loop_list.py new file mode 100644 index 0000000..6870485 --- /dev/null +++ b/scripts/for_loop_list.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 20 14:20:38 2018 + +@author: u0015831 +""" + +#my_list = [1, 2, 32] +my_list = (1, 2, 3) + +for i in my_list: + print(my_list[i]) \ No newline at end of file diff --git a/scripts/for_loop_sequence.py b/scripts/for_loop_sequence.py new file mode 100644 index 0000000..fbfb90e --- /dev/null +++ b/scripts/for_loop_sequence.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 20 15:46:16 2018 +for loop on different sequences + +source: https://www.tutorialspoint.com/python/python_for_loop.htm +@author: u0015831 +""" + +for letter in 'Python': # First Example + print('Current Letter :', letter) + +fruitslist = ['banana', 'apple', 'mango'] +for fruit in fruitslist: # Second Example + print('Current fruit :', fruit) +print() +fruitstuple = ('banana', 'apple', 'mango') +for fruit in fruitstuple: # Third Example + print('Current fruit :', fruit) +print() +fruitset = {'banana', 'apple', 'mango'} +for fruit in fruitset: # Fourth Example + print('Current fruit :', fruit) diff --git a/scripts/for_loop_vanrossum_1.py b/scripts/for_loop_vanrossum_1.py new file mode 100644 index 0000000..d965c2e --- /dev/null +++ b/scripts/for_loop_vanrossum_1.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Spyder Editor + +example for loop from Python tutorial R 3.7.0 +""" +words = ['cat', 'window', 'defenestrate'] + + +for w in words[:]: # Loop over a slice copy of the entire list. +#for w in words: # Loop over the sequence, beware for an infinite list!. + if len(w) > 6: + words.insert(0, w) + +print(words) \ No newline at end of file diff --git a/scripts/for_loop_zip.py b/scripts/for_loop_zip.py new file mode 100644 index 0000000..96ca450 --- /dev/null +++ b/scripts/for_loop_zip.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 20 16:25:00 2018 + +source: http://treyhunner.com/2016/04/how-to-loop-with-indexes-in-python/ +@author: u0015831 +""" + +fruitslist = ['banana', 'apple', 'mango'] +ratios = [0.2, 0.3, 0.1, 0.4] +winelist = ['merlot', 'chenin', 'chardonnay', 'temperanillo', 'whatever'] +for fruit, ratio, wine in zip(fruitslist, ratios, winelist): + print('{}% {} more {}'.format(ratio * 100, fruit, wine)) diff --git a/scripts/format_io_1.py b/scripts/format_io_1.py new file mode 100644 index 0000000..75523c1 --- /dev/null +++ b/scripts/format_io_1.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 14:53:41 2018 + +source: https://www.digitalocean.com/community/tutorials/how-to-use-string-formatters-in-python-3 +@author: u0015831 +""" + +print("Sammy ate {0:f} percent of a {1}!".format(75, "pizza")) +print("Sammy ate {0:6.2f} percent of a {1}!".format(75, "pizza")) +print("Sammy ate {0:x} percent of a {1}!".format(75, "pizza")) +print("Sammy ate {0:3d} percent of a {1}!".format(75, "pizza")) + +for i in range(3,13): + print("{:3d} {:4d} {:5d}".format(i, i*i, i*i*i)) \ No newline at end of file diff --git a/scripts/format_string.py b/scripts/format_string.py new file mode 100644 index 0000000..58607d7 --- /dev/null +++ b/scripts/format_string.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 1 16:11:26 2023 + +Number formatting in f-strings + +""" + +name = 'Jane' +age = 53 +print('%s is %d years old' % (name, age)) # old school deprecated +print('{} is {} years old'.format(name, age)) +print(f'{name} is {age} years old') # preferred + + +# arithmetic expressions allowed +a = 5 +b = 10 +print(f'a plus b is {a + b} and 2 times a minus b is {2*a-b}') + +# The = specifier will print the expression and its value: +fnum = 2007.12345 +print(f"Simple = {fnum}") +print(f"Decimal Places specified = {fnum=:.2f}") +print(f"Significant Figures={fnum:.3g}") diff --git a/scripts/fstring_01.py b/scripts/fstring_01.py new file mode 100644 index 0000000..58607d7 --- /dev/null +++ b/scripts/fstring_01.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Sep 1 16:11:26 2023 + +Number formatting in f-strings + +""" + +name = 'Jane' +age = 53 +print('%s is %d years old' % (name, age)) # old school deprecated +print('{} is {} years old'.format(name, age)) +print(f'{name} is {age} years old') # preferred + + +# arithmetic expressions allowed +a = 5 +b = 10 +print(f'a plus b is {a + b} and 2 times a minus b is {2*a-b}') + +# The = specifier will print the expression and its value: +fnum = 2007.12345 +print(f"Simple = {fnum}") +print(f"Decimal Places specified = {fnum=:.2f}") +print(f"Significant Figures={fnum:.3g}") diff --git a/scripts/function_1.py b/scripts/function_1.py new file mode 100644 index 0000000..b7dbd7f --- /dev/null +++ b/scripts/function_1.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +""" + +basic example using function + +demo 1: copy main part in front of the function definitions + check the behavior of the IDE + +demo 2: run program in editor / debug mode + +@author: u0015831 +""" + + +# main part, calling the functions + + +def my_func_1 (): + """ + this function writes hello, no input, no output + """ + print('Hello') + +def my_func_2(a, b): + """ + parameters: + a: first part of mathematical manip + b: second part in mathematical manip + + """ + res = a + b + return (res) + +def my_func_3(a, b): + """ + parameters: + a: first part of mathematical manip + b: second part in mathematical manip + """ + res1 = a + b + res2 = a - b + return (res1, res2) + + + +my_func_1() + +my_func_2(5,6) # effect? +r1 = my_func_2(5,6) +print(type(r1)) +print(r1) + +r2 = my_func_3(5,6) +print(type(r2)) +print(r2) + +r3 = my_func_3(b=5,a=6) +print(type(r3)) +print(r3) \ No newline at end of file diff --git a/scripts/function_arg_kwarg.py b/scripts/function_arg_kwarg.py new file mode 100644 index 0000000..a9f7b9c --- /dev/null +++ b/scripts/function_arg_kwarg.py @@ -0,0 +1,38 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 4 10:14:49 2018 + +source: + https://www.saltycrane.com/blog/2008/01/how-to-use-args-and-kwargs-in-python/ + https://code.tutsplus.com/articles/understanding-args-and-kwargs-in-python--cms-29494 + https://www.digitalocean.com/community/tutorials/how-to-use-args-and-kwargs-in-python-3 +@author: u0015831 +""" + +def test_var_args(farg, *args): + print("formal arg:", farg) + for arg in args: + print("another arg:", arg, " - type", type(arg)) + +def test_var_kwargs(farg, **kwargs): + print("formal arg:", farg) + for key in kwargs: + print("another keyword arg: %s: %s" % (key, kwargs[key])) + +def multiply(*args): + z = 1 + for num in args: + z *= num + print(z) + print('final result: ',z) + + +#using the functions +test_var_args(1, "two", 3) + +test_var_kwargs(farg=1, myarg2="two", myarg3=3) +test_var_kwargs(100, myarg2="two", myarg3=3) +#test_var_kwargs(101, "two", myarg3=3) # only 1 argument + +multiply(2, 3) +multiply(2, 3, 99, 12.7) diff --git a/scripts/function_arg_kwarg_1.py b/scripts/function_arg_kwarg_1.py new file mode 100644 index 0000000..56e7f02 --- /dev/null +++ b/scripts/function_arg_kwarg_1.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 13:14:08 2022 + +@author: u0015831 +""" + +def arg_printer(*args, **kwargs): + if args: + print('positional') + for arg in args: + print(arg) + if kwargs: + print('named') + for k, v in kwargs.items(): + print("{}={}".format(k, v)) + +print('first test') +arg_printer(1,2,3) + +print('test 2') +arg_printer(1,2,3,sweater=100,hat=5) + diff --git a/scripts/function_argument_pass_1.py b/scripts/function_argument_pass_1.py new file mode 100644 index 0000000..4f7ed55 --- /dev/null +++ b/scripts/function_argument_pass_1.py @@ -0,0 +1,62 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 27 11:42:31 2018 + +source: https://cs.nyu.edu/courses/summer16/CSCI-UA.0002-002/slides/Python_Functions.pdf +@author: u0015831 +""" + +def change_me(v): + print ("function got:", v, 'id(v) = ', id(v)) + v = 10 + print ("argument is now:", v, 'id(v) = ', id(v)) + +def change_me_bis(v): + print ("function got:", v, 'id(v) = ', id(v)) + v.append(555) + print ("argument is now:", v, 'id(v) = ', id(v)) + +def change_me_tris(v): + print ("function got:", v, 'id(v) = ', id(v)) + v = v * 3 + print ("argument is now:", v, 'id(v) = ', id(v)) + + +myvar = 5 +myvar2 = [1, 2, 3] + +# testing change_me +print('\n testing change_me') +print ("starting with:", myvar) +print ("starting with id:", id(myvar)) +change_me(myvar) +print ("ending with:", myvar) + +print ("starting with:", myvar2) +print ("starting with id:", id(myvar2)) +change_me(myvar2) +print ("ending with:", myvar2) + + +# testing change_me_bis +print('\n testing change_me_bis') + +print ("starting with:", myvar2) +print ("starting with id:", id(myvar2)) +change_me_bis(myvar2) +print ("ending with:", myvar2) + + + +# testing change_me_tris +print('\n testing change_me_tris') + +myvar = 5 +print ("starting with:", myvar) +change_me_tris(myvar) +print ("ending with:", myvar) + +myvar2 = [1, 2, 3] +print ("starting with:", myvar2) +change_me_tris(myvar2) +print ("ending with:", myvar2) diff --git a/scripts/function_on_list.py b/scripts/function_on_list.py new file mode 100644 index 0000000..5ea0995 --- /dev/null +++ b/scripts/function_on_list.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Jun 29 17:00:09 2018 + +function on list + +source: http://www.cs.cornell.edu/courses/cs1110/2018sp/ +""" + + +def add_one(the_list): + """Adds 1 to every elt + Pre: the_list is all numb.""" + for x in the_list: + x = x+1 + +def add_one_bis(the_list): + """Adds 1 to every elt + Pre: the_list is all numb.""" + lenl = len(the_list) + lll = list(range(lenl)) + for i in lll: + the_list[i] = the_list[i]+1 + +grades = [5,4,7] + +add_one(grades) +print(grades) + +add_one_bis(grades) +print(grades) diff --git a/scripts/function_optional_arguments.py b/scripts/function_optional_arguments.py new file mode 100644 index 0000000..7638314 --- /dev/null +++ b/scripts/function_optional_arguments.py @@ -0,0 +1,27 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 27 11:00:12 2018 + +source: https://www.pythoncentral.io/fun-with-python-function-parameters/ +@author: u0015831 +""" + +def foo(val1, val2, val3, calcSum=True): + # Calculate the sum + if calcSum: + return val1 + val2 - val3 + # Calculate the average instead + else: + return (val1 + val2 + val3) / 3 + +print(foo(10,20,30, True)) +print(foo(10,20,30, False)) +print(foo(10,20,30)) + +print(foo(val1=10,val3=20,val2=30,calcSum=True)) + + +# will this work? +#print(foo(10,20)) +#print(foo(10,20,False)) +#print(foo(val1=10,val3=20,val2=30,True)) diff --git a/scripts/function_pass_mutable.py b/scripts/function_pass_mutable.py new file mode 100644 index 0000000..e4f38f8 --- /dev/null +++ b/scripts/function_pass_mutable.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 12:11:11 2022 + +@author: u0015831 +""" + +def myfunction (seq): + print ("Entering sequence inside a function: ", seq, ' id', id(seq)) + seq.append(40) + print ("Modified sequence inside a function: ", seq, ' id', id(seq)) + seq = [1, 2, 3] + print ("New sequence inside a function: ", seq, ' id', id(seq)) + return + +mylist=[10,20,30] +print('mylist before call', mylist, ' id', id(mylist)) +myfunction(mylist) +print('mylist after call', mylist, ' id', id(mylist)) diff --git a/scripts/function_pass_nonmutable.py b/scripts/function_pass_nonmutable.py new file mode 100644 index 0000000..17be7fa --- /dev/null +++ b/scripts/function_pass_nonmutable.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 12:09:32 2022 + +@author: u0015831 +""" + +def myfunction(arg): + print ('value received:',arg,'id:',id(arg)) + arg = 8 + print ('value changed:',arg,'id:',id(arg)) + return + +#id() function returns a unique integer corresponding to the identity of an object. + +x=10 +print ('value passed:',x, 'id:',id(x)) + +myfunction(x) +print ('value after function call:',x, 'id:',id(x)) diff --git a/scripts/function_return.py b/scripts/function_return.py new file mode 100644 index 0000000..8cb5286 --- /dev/null +++ b/scripts/function_return.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 12 16:40:19 2023 + +@author: u0015831 +""" + +def test(p1=10): + + if (p1 < 10): + return ('abc', 100) + elif (p1 == 10): + return ('default value') + else: + return 0 + + +# +t1 = test(6) +print(type(t1), t1) + +t1 = test() +print(type(t1), t1) + +t1 = test(1000) +print(type(t1), t1) diff --git a/scripts/function_scope_1.py b/scripts/function_scope_1.py new file mode 100644 index 0000000..3de296f --- /dev/null +++ b/scripts/function_scope_1.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 20 10:21:52 2020 +taken from: https://www.python-course.eu/python3_global_vs_local_variables.php +@author: u0015831 +""" + +def f(): + print(a) + print(s) + +a = 1 +s = "I love Paris in the summer!" +f() \ No newline at end of file diff --git a/scripts/function_scope_2.py b/scripts/function_scope_2.py new file mode 100644 index 0000000..28dcb26 --- /dev/null +++ b/scripts/function_scope_2.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 20 10:28:19 2020 +taken from: https://www.python-course.eu/python3_global_vs_local_variables.php +@author: u0015831 +""" + +def f(): + s = "I love London!" + print(s) + +s = "I love Paris!" +f() +print(s) \ No newline at end of file diff --git a/scripts/function_scope_3.py b/scripts/function_scope_3.py new file mode 100644 index 0000000..1a1de03 --- /dev/null +++ b/scripts/function_scope_3.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 20 10:28:19 2020 +taken from: https://www.python-course.eu/python3_global_vs_local_variables.php +@author: u0015831 +""" + +def f(): + print(s) + s = "I love London!" # considered a local variable + print(s) + +s = "I love Paris!" +f() +print(s) \ No newline at end of file diff --git a/scripts/function_scope_4.py b/scripts/function_scope_4.py new file mode 100644 index 0000000..7194e00 --- /dev/null +++ b/scripts/function_scope_4.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 20 10:28:19 2020 +taken from: https://www.python-course.eu/python3_global_vs_local_variables.php +@author: u0015831 +""" + +def f(): + ''' + example working with global key word + ''' + global s + print(s) + s = "I love London!" # considered a local variable + print(s) + +s = "I love Paris!" +f() +print(s) \ No newline at end of file diff --git a/scripts/function_scope_5.py b/scripts/function_scope_5.py new file mode 100644 index 0000000..8f0d496 --- /dev/null +++ b/scripts/function_scope_5.py @@ -0,0 +1,38 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 15 09:25:39 2020 + +@author: https://www.tutorialsteacher.com/python/local-and-global-variables-in-python +""" + +def SayHello(): + user='John' + print ("user = ", user) + return + +def SayHello_implicit(): + # user must be known + print ("user = ", user) + return + +def SayHello_global(): + global user + print ("user = ", user) + user = 'Mary' + val_k = 0 + return + + +user = 'Jeff' +SayHello_implicit() +print('user', user) + +user = 'Jeff' +SayHello() +print('user', user) + +user = 'Jeff' +SayHello_global() +print('user', user) +#print(val_k) + diff --git a/scripts/functions_args_1.py b/scripts/functions_args_1.py new file mode 100644 index 0000000..b793453 --- /dev/null +++ b/scripts/functions_args_1.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 26 15:45:23 2018 + +@author: u0015831 +""" +# a regular function definition +def my_sub(a,b): + res = a - b + return (res, a, b) + +# defining a function with default values +def my_min(a=9, b = 2): + res = a - b + return (res, a, b) + +# calling the functions +res = my_sub(1, 2) +print(res) + +res = my_sub(b=10, a=5) +print(res) + +c = 1 +d = 2 +res = my_sub(c, d) +print(res) + +res = my_min() +print(res) + +res = my_min(7, 8) +print(res) diff --git a/scripts/generate_data.py b/scripts/generate_data.py new file mode 100644 index 0000000..a8fcbcd --- /dev/null +++ b/scripts/generate_data.py @@ -0,0 +1,8 @@ +jdef main(): + print('case', 'dim', 'temp') + case_nr = 0 + for dim_nr in [1, 2, 3]: + for temp in [-0.5, 0.0, 0.5]: + case_nr += 1 + print(case_nr, dim_nr, temp) + return 0 diff --git a/scripts/get_help.py b/scripts/get_help.py new file mode 100644 index 0000000..376d68b --- /dev/null +++ b/scripts/get_help.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 6 10:06:04 2019 + +getting help +- dir +- help + +@author: u0015831 +""" + +a = "string" +dir(a) + +help(a.split) diff --git a/scripts/greetings_bucket.py b/scripts/greetings_bucket.py new file mode 100644 index 0000000..215b734 --- /dev/null +++ b/scripts/greetings_bucket.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 13 16:17:00 2020 + +@author: u0015831 +""" + +def greetings(name): + """This function greets the name passed as an argument""" + print('Hello, ', name , ' Welcome!') + \ No newline at end of file diff --git a/scripts/greetings_calling.py b/scripts/greetings_calling.py new file mode 100644 index 0000000..8422b41 --- /dev/null +++ b/scripts/greetings_calling.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 29 10:12:42 2019 + +basic usage of a function + +@author: u0015831 +""" + +from greetings_bucket import greetings + +# use the function multiple times +greetings('Jan') +greetings('Lieve') +greetings('Jef') + diff --git a/scripts/greetings_func.py b/scripts/greetings_func.py new file mode 100644 index 0000000..b3090d2 --- /dev/null +++ b/scripts/greetings_func.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 29 10:12:42 2019 + +basic usage of a function + +@author: u0015831 +""" + +def greetings(name): + """This function greets the name passed as an argument""" + print('Hello, ', name , ' Welcome!') + + +# use the function multiple times +greetings('Jan') +greetings('Lieve') +greetings('Jef') + diff --git a/scripts/greetings_more_func.py b/scripts/greetings_more_func.py new file mode 100644 index 0000000..832f60a --- /dev/null +++ b/scripts/greetings_more_func.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 29 10:27:05 2019 + +@author: u0015831 +""" + +def greetings_more(name): + """This function greets the name passed as an argument""" + greetings('Dear') + greetings(name) + +def greetings(name): + """This function greets the name passed as an argument""" + print('Hello, ', name , ' Welcome!') + + +greetings_more('Marie') \ No newline at end of file diff --git a/scripts/handson_lists.py b/scripts/handson_lists.py new file mode 100644 index 0000000..7330710 --- /dev/null +++ b/scripts/handson_lists.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 19 15:36:15 2018 + +handson lists +@author: u0015831 +""" + +NumList=list(range(10)) + +# Print the length of the list +ll = len(NumList) +print("length list", ll) + +# Change the fourth element to 11 +NumList[3] = 11 + +# Extend the list with L=[20,30,40] +NumList.extend([20, 30, 40]) +print(NumList) + +# Print the index of the item 9 +i9 = NumList.index(9) +print("index of item 9", i9) + +# Remove that item from the list +NumList.remove(9) + +# Print the current length of the list +print("current length of list: ", len(NumList)) + +# Sort the list and then reverse the sorted version +NumList.sort +Lreversed=NumList[::-1] +print(Lreversed) \ No newline at end of file diff --git a/scripts/hello.py b/scripts/hello.py new file mode 100644 index 0000000..97d5019 --- /dev/null +++ b/scripts/hello.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 18 11:14:17 2019 + +@author: u0015831 +""" + +def hello(): + """ + Prints "Hello World". + Returns: + None + """ + + print('Hello World') + return + +def hello_again(): + """ + Prints "Hello World again". + Returns: + None + """ + + print('Hello World again') + return + +print('The docstring of the function hello: ' + hello.__doc__) +print('The docstring of the function hello_again: ' + hello_again.__doc__) diff --git a/scripts/hello_world.py b/scripts/hello_world.py new file mode 100644 index 0000000..456fdb0 --- /dev/null +++ b/scripts/hello_world.py @@ -0,0 +1,9 @@ +#!/usr/bin/env python + +# ============================================================================= +# print('hello world') +# print('hello world') +# print('hello world') +# ============================================================================= +print('hello world') # more +print('hello class') # text diff --git a/scripts/hello_world_function.py b/scripts/hello_world_function.py new file mode 100644 index 0000000..aaefb2a --- /dev/null +++ b/scripts/hello_world_function.py @@ -0,0 +1,16 @@ +m# -*- coding: utf-8 -*- +""" +Created on Mon Nov 25 17:02:29 2019 + +@author: u0015831 +""" + + +def hello(): + """Print "Hello World" """ + print("Later World") + +# Main program starts here +# once executed, the function hello is known +# the function can be called at the console +hello() diff --git a/scripts/hello_world_input.py b/scripts/hello_world_input.py new file mode 100644 index 0000000..8dd0958 --- /dev/null +++ b/scripts/hello_world_input.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python + +print('hello world') + +input('Press Enter to Continue...') \ No newline at end of file diff --git a/scripts/hi_python.py b/scripts/hi_python.py new file mode 100644 index 0000000..7e998a9 --- /dev/null +++ b/scripts/hi_python.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri May 31 14:00:55 2019 + +@author: u0015831 +""" + +print("Hi, this is a Python program") diff --git a/scripts/if_1.py b/scripts/if_1.py new file mode 100644 index 0000000..16c6f35 --- /dev/null +++ b/scripts/if_1.py @@ -0,0 +1,24 @@ +1# -*- coding: utf-8 -*- +""" +Created on Fri Jun 15 09:49:23 2018 + +Basic if statement + +source: http://anh.cs.luc.edu/handsonPythonTutorial/ifstatements.html +@author: u0015831 +""" +ipay = 0 +weight = float(input("How many pounds does your suitcase weigh? ")) +if weight > 50: + print("There is a $25 charge for luggage that heavy.") + ipay = 1 +print("Thank you for your business.") + +balance = float(input("Enter the balance on your account ")) +if ipay == 1: + balance = balance - 25 + if balance < 0: + print("negative balance!") + +print("Your balance is :", balance) + \ No newline at end of file diff --git a/scripts/if_else_1.py b/scripts/if_else_1.py new file mode 100644 index 0000000..df93b76 --- /dev/null +++ b/scripts/if_else_1.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Jun 15 09:43:06 2018 + +basic if else statement + +@author: u0015831 +""" + +from random import randint + +# use the input function, specify with int-casting that an integer is expected +x = int(input('Give me a number: ')) +if x < 0: + print(x, 'is negative') +else: + print(x, 'is positive') + + +xr = randint(1, 100) +print('xr = ', xr) +xn = x + xr + +if xn == 0: + print("The sum is equal to 0") +else: + if xn < 0: + print(xn, 'is negative') + else: + print(xn, 'is positive') diff --git a/scripts/import_1.py b/scripts/import_1.py new file mode 100644 index 0000000..b2b4fef --- /dev/null +++ b/scripts/import_1.py @@ -0,0 +1,9 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 18 20:48:23 2019 + +@author: u0015831 +""" + +import math +print(math.pi) \ No newline at end of file diff --git a/scripts/input_1.py b/scripts/input_1.py new file mode 100644 index 0000000..340f98b --- /dev/null +++ b/scripts/input_1.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 27 14:22:32 2018 + +source: https://anh.cs.luc.edu/python/hands-on/3.1/handsonHtml/io.html +@author: u0015831 +""" + +name = input('Enter your name: ') +print('Hello ' + name + '!') +print(type(name)) +age = input('Enter your age: ') +print(age) +print(type(age)) + +#use typecasting +xString = input("Enter a number: ") +x = float(xString) +yString = input("Enter a second number: ") +y = float(yString) +print('The sum of ', x, ' and ', y, ' is ', x+y, '.') \ No newline at end of file diff --git a/scripts/isVow_module.py b/scripts/isVow_module.py new file mode 100644 index 0000000..f2f5375 --- /dev/null +++ b/scripts/isVow_module.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 14:42:24 2022 + +source: https://sites.pitt.edu/~naraehan/python3/mbb20.html +@author: u0015831 +""" + + +def isVow(char): + return char.lower() in 'aeiou' + +def main(): + print(isVow('i')) + print(isVow('I')) + print(isVow('K')) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/isVow_script.py b/scripts/isVow_script.py new file mode 100644 index 0000000..e2250f6 --- /dev/null +++ b/scripts/isVow_script.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 14:41:13 2022 + +source: https://sites.pitt.edu/~naraehan/python3/mbb20.html + +@author: u0015831 +""" + + +def isVow(char): + return char.lower() in 'aeiou' + +print(isVow('i')) +print(isVow('I')) +print(isVow('K')) diff --git a/scripts/isVow_using.py b/scripts/isVow_using.py new file mode 100644 index 0000000..c98fb24 --- /dev/null +++ b/scripts/isVow_using.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 14:43:11 2022 + +@author: u0015831 +""" + +import isVow_script +import isVow_module + + +# call isVow the bad way +# the code in the script file is fully executed, also the test part +print('regular file') +print(isVow_script.isVow('i')) +print(isVow_script.isVow('y')) + + +# call isVow the right way +# only the function itself will be executed +print('name == main') +print(isVow_module.isVow('i')) +print(isVow_module.isVow('y')) \ No newline at end of file diff --git a/scripts/iterator_1.py b/scripts/iterator_1.py new file mode 100644 index 0000000..98349ed --- /dev/null +++ b/scripts/iterator_1.py @@ -0,0 +1,54 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 5 10:32:20 2018 + +@author: u0015831 +""" +# what is range? +xr = range(10) +print(type(xr)) + +for i in range(10): + print(i, end=' ') + +print('\n') + +for value in [2, 4, 6, 8, 10]: +# do some operation + print(value + 1, end=' ') +print('\n') + +# enumerate +L1 = [2, 4, 6, 8, 10] +for i, val in enumerate(L1): + print(i, val) +print('\n') + +L2 = ['pizza', 'pasta', 'salad', 'nachos'] +print(list(enumerate(L2))) +for i, val in enumerate(L2): + print(i, val) + +# zip +L3 = [12.3, 44.56, 99.9, 768.02, 123, 123.56] +zipped = zip(L1, L2, L3) +print(list(zip(L1, L2, L3))) +for v1, v2, v3 in zipped: + print(v1, v2, v3) +print('\n') + +# unzip +e1, e2, e3 = zip(*zip(L1, L2, L3)) + +# map +square = lambda x: x ** 2 +for val in map(square, range(10)): + print(val, end=' / ') +print('\n') + +# find values up to 10 for which x % 2 is zero +is_even = lambda x: x % 2 == 0 +for val in filter(is_even, range(10)): + print(val, end=' ') +print('\n') + diff --git a/scripts/lambda_func_1.py b/scripts/lambda_func_1.py new file mode 100644 index 0000000..b73a7cc --- /dev/null +++ b/scripts/lambda_func_1.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 12 14:25:31 2022 + +@author: u0015831 +""" + +x = lambda a : a + 10 +f = lambda a, b: a + b +v1 = 8.8 + +print('type x:', type(x)) +print(x(6)) +print(x(v1)) + +print('type f:', type(f)) +print(f(5.6,6)) \ No newline at end of file diff --git a/scripts/lines_10.txt b/scripts/lines_10.txt new file mode 100644 index 0000000..7d3e69b --- /dev/null +++ b/scripts/lines_10.txt @@ -0,0 +1,19 @@ +Inhabit hearing perhaps on ye do no. It maids decay as there he. Smallest on suitable disposed do although blessing he juvenile in. Society or if excited forbade. Here name off yet she long sold easy whom. Differed oh cheerful procured pleasure securing suitable in. Hold rich on an he oh fine. Chapter ability shyness article welcome be do on service. + +Mind what no by kept. Celebrated no he decisively thoroughly. Our asked sex point her she seems. New plenty she horses parish design you. Stuff sight equal of my woody. Him children bringing goodness suitable she entirely put far daughter. + +Parish so enable innate in formed missed. Hand two was eat busy fail. Stand smart grave would in so. Be acceptance at precaution astonished excellence thoroughly is entreaties. Who decisively attachment has dispatched. Fruit defer in party me built under first. Forbade him but savings sending ham general. So play do in near park that pain. + +Attachment apartments in delightful by motionless it no. And now she burst sir learn total. Hearing hearted shewing own ask. Solicitude uncommonly use her motionless not collecting age. The properly servants required mistaken outlived bed and. Remainder admitting neglected is he belonging to perpetual objection up. Has widen too you decay begin which asked equal any. + +It prepare is ye nothing blushes up brought. Or as gravity pasture limited evening on. Wicket around beauty say she. Frankness resembled say not new smallness you discovery. Noisier ferrars yet shyness weather ten colonel. Too him himself engaged husband pursuit musical. Man age but him determine consisted therefore. Dinner to beyond regret wished an branch he. Remain bed but expect suffer little repair. + +Had repulsive dashwoods suspicion sincerity but advantage now him. Remark easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest jointure saw horrible. He private he on be imagine suppose. Fertile beloved evident through no service elderly is. Blind there if every no so at. Own neglected you preferred way sincerity delivered his attempted. To of message cottage windows do besides against uncivil. + +Led ask possible mistress relation elegance eat likewise debating. By message or am nothing amongst chiefly address. The its enable direct men depend highly. Ham windows sixteen who inquiry fortune demands. Is be upon sang fond must shew. Really boy law county she unable her sister. Feet you off its like like six. Among sex are leave law built now. In built table in an rapid blush. Merits behind on afraid or warmly. + +Ecstatic advanced and procured civility not absolute put continue. Overcame breeding or my concerns removing desirous so absolute. My melancholy unpleasing imprudence considered in advantages so impression. Almost unable put piqued talked likely houses her met. Met any nor may through resolve entered. An mr cause tried oh do shade happy. + +Raising say express had chiefly detract demands she. Quiet led own cause three him. Front no party young abode state up. Saved he do fruit woody of to. Met defective are allowance two perceived listening consulted contained. It chicken oh colonel pressed excited suppose to shortly. He improve started no we manners however effects. Prospect humoured mistress to by proposal marianne attended. Simplicity the far admiration preference everything. Up help home head spot an he room in. + +Extended kindness trifling remember he confined outlived if. Assistance sentiments yet unpleasing say. Open they an busy they my such high. An active dinner wishes at unable hardly no talked on. Immediate him her resolving his favourite. Wished denote abroad at branch at. diff --git a/scripts/list_cheat.py b/scripts/list_cheat.py new file mode 100644 index 0000000..82b425f --- /dev/null +++ b/scripts/list_cheat.py @@ -0,0 +1,70 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Jun 22 11:38:15 2018 + +list_cheat + +beased on http://ehmatthes.github.io/pcc/cheatsheets/README.html +@author: u0015831 +""" + +# Use square brackets to define a list, and use commas to separate individual items in the list. Use plural names for lists, to make your code easier to read. +# [] +zips = [3001, 3000, 1000, 9000, 8000] + +# Individual elements in a list are accessed according to their index. The index is 0 based. +# Use square brackets []" +first = zips[0] +last = zips[-1] + +#Refer to the index of the item you want to modify. +zips[1] = 8500 +zips[-2] = 2000 + +# Add elements to the end of a list, +# or insert at wherever position in the list." +zips.append(3500) +zips.insert(0, 3050) +zips.insert(3,4000) + +# remove elements by their position in a list, +# or by the value of the item. Python removes only the first occurrence of that value" +del zips[1] +zips.remove(4000) + +# pop() returns the last element in the list, but any index can be specified. +recent_zip = zips.pop() +first_zip = zips.pop(0) + +# The len() function returns the number of items +num_zip = len(zips) + +# The sort() method changes the order of a list permanently. +# The sorted() function returns a copy of the list, leaving the original list unchanged. " +zips.sort() +zips.sort(reverse=True) +zs = sorted(zips) + +#Python loop pulls each item from the list one at a time and stores it in a temporary variable, which you provide a name for. +for zip in zips: + print(zip) + +# The range() function starts at 0 by default, and stops below the upper boundary. +# range(start, stop[, step]) +for number in range(1001): + print(number) +# create a list of numbers, interlaced by 100 +numbers = list(range(1, 1000001, 100)) + +# Simple statistics are possible on a list containing numerical data. +smallest = min(zips) +largest = max(zips) +sumzips = sum(zips) + +# A portion of a list is called a slice. To slice a list start with the index of the first item , colon, index after the last item. +first3 = zips[:3] +last3 = zips[-3:] +middle3 = zips[2:5] + +#Copy a list: make a slice from the first to the last element. Remember that a variable name is a label, pointer to some spot in memory +copy_of_zips = zips[:] diff --git a/scripts/list_copy.py b/scripts/list_copy.py new file mode 100644 index 0000000..4dc18a9 --- /dev/null +++ b/scripts/list_copy.py @@ -0,0 +1,50 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 19 16:12:40 2018 + +lists: copying + +@author: u0015831 +""" +# this behavior is specific for any mutable data structure +v1=[1,2,3,4] +v2=v1 +v2[2]=9 +print("v1: ", v1) + +# slicing notation +v1=[1,2,3,4] +C=v1[:] +C[1]=11 +print("v1: ", v1) + +# use list function +v1=[1,2,3,4] +v2=list(v1) +v2[2]=9 +print("v1: ", v1) + +# use copy +v1=[1,2,3,4] +v2=v1.copy() +v2[2]=9 +print("v1: ", v1) + +# more layers +L1 = [1,2,3] +L2 = [4,5,6] +L3 = [7,8,9] + +Lbig = [L1,L2,L3] +LbigAlias = Lbig +LbigAlias2 = Lbig.copy() +LbigShallow = Lbig[:] + +from copy import deepcopy +LbigDeep = deepcopy(Lbig) + +L1[0] = 99 #changes first element of L1 +print(LbigAlias) +print(LbigAlias2) +print(LbigShallow) +print(LbigDeep) diff --git a/scripts/list_nested.py b/scripts/list_nested.py new file mode 100644 index 0000000..51678aa --- /dev/null +++ b/scripts/list_nested.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 2 11:46:02 2018 + +nested lists +@author: u0015831 +""" + +a = [1, 2] +b = [3, 4, 5] + +ln1 = [10, a] + +ln2 = [1, 2, a, b, ln1] + +print(ln2) +print(ln2[-1]) +print(ln2[-1][1]) +print(ln2[-1][1][1]) \ No newline at end of file diff --git a/scripts/make_it_upper.py b/scripts/make_it_upper.py new file mode 100644 index 0000000..98e7409 --- /dev/null +++ b/scripts/make_it_upper.py @@ -0,0 +1,18 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 18 20:12:22 2019 +source: http://rwet.decontextualize.com/book/functions/ +example: python make_it_upper.py 0: + print(ucfirst(line)) + else: + print(line) \ No newline at end of file diff --git a/scripts/mall_customers.csv b/scripts/mall_customers.csv new file mode 100644 index 0000000..93d04e8 --- /dev/null +++ b/scripts/mall_customers.csv @@ -0,0 +1,201 @@ +CustomerID,Gender,Age,Annual Income (k$),Spending Score +1,Male,19,15,39 +2,Male,21,15,81 +3,Female,20,16,6 +4,Female,23,16,77 +5,Female,31,17,40 +6,Female,22,17,76 +7,Female,35,18,6 +8,Female,23,18,94 +9,Male,64,19,3 +10,Female,30,19,72 +11,Male,67,19,14 +12,Female,35,19,99 +13,Female,58,20,15 +14,Female,24,20,77 +15,Male,37,20,13 +16,Male,22,20,79 +17,Female,35,21,35 +18,Male,20,21,66 +19,Male,52,23,29 +20,Female,35,23,98 +21,Male,35,24,35 +22,Male,25,24,73 +23,Female,46,25,5 +24,Male,31,25,73 +25,Female,54,28,14 +26,Male,29,28,82 +27,Female,45,28,32 +28,Male,35,28,61 +29,Female,40,29,31 +30,Female,23,29,87 +31,Male,60,30,4 +32,Female,21,30,73 +33,Male,53,33,4 +34,Male,18,33,92 +35,Female,49,33,14 +36,Female,21,33,81 +37,Female,42,34,17 +38,Female,30,34,73 +39,Female,36,37,26 +40,Female,20,37,75 +41,Female,65,38,35 +42,Male,24,38,92 +43,Male,48,39,36 +44,Female,31,39,61 +45,Female,49,39,28 +46,Female,24,39,65 +47,Female,50,40,55 +48,Female,27,40,47 +49,Female,29,40,42 +50,Female,31,40,42 +51,Female,49,42,52 +52,Male,33,42,60 +53,Female,31,43,54 +54,Male,59,43,60 +55,Female,50,43,45 +56,Male,47,43,41 +57,Female,51,44,50 +58,Male,69,44,46 +59,Female,27,46,51 +60,Male,53,46,46 +61,Male,70,46,56 +62,Male,19,46,55 +63,Female,67,47,52 +64,Female,54,47,59 +65,Male,63,48,51 +66,Male,18,48,59 +67,Female,43,48,50 +68,Female,68,48,48 +69,Male,19,48,59 +70,Female,32,48,47 +71,Male,70,49,55 +72,Female,47,49,42 +73,Female,60,50,49 +74,Female,60,50,56 +75,Male,59,54,47 +76,Male,26,54,54 +77,Female,45,54,53 +78,Male,40,54,48 +79,Female,23,54,52 +80,Female,49,54,42 +81,Male,57,54,51 +82,Male,38,54,55 +83,Male,67,54,41 +84,Female,46,54,44 +85,Female,21,54,57 +86,Male,48,54,46 +87,Female,55,57,58 +88,Female,22,57,55 +89,Female,34,58,60 +90,Female,50,58,46 +91,Female,68,59,55 +92,Male,18,59,41 +93,Male,48,60,49 +94,Female,40,60,40 +95,Female,32,60,42 +96,Male,24,60,52 +97,Female,47,60,47 +98,Female,27,60,50 +99,Male,48,61,42 +100,Male,20,61,49 +101,Female,23,62,41 +102,Female,49,62,48 +103,Male,67,62,59 +104,Male,26,62,55 +105,Male,49,62,56 +106,Female,21,62,42 +107,Female,66,63,50 +108,Male,54,63,46 +109,Male,68,63,43 +110,Male,66,63,48 +111,Male,65,63,52 +112,Female,19,63,54 +113,Female,38,64,42 +114,Male,19,64,46 +115,Female,18,65,48 +116,Female,19,65,50 +117,Female,63,65,43 +118,Female,49,65,59 +119,Female,51,67,43 +120,Female,50,67,57 +121,Male,27,67,56 +122,Female,38,67,40 +123,Female,40,69,58 +124,Male,39,69,91 +125,Female,23,70,29 +126,Female,31,70,77 +127,Male,43,71,35 +128,Male,40,71,95 +129,Male,59,71,11 +130,Male,38,71,75 +131,Male,47,71,9 +132,Male,39,71,75 +133,Female,25,72,34 +134,Female,31,72,71 +135,Male,20,73,5 +136,Female,29,73,88 +137,Female,44,73,7 +138,Male,32,73,73 +139,Male,19,74,10 +140,Female,35,74,72 +141,Female,57,75,5 +142,Male,32,75,93 +143,Female,28,76,40 +144,Female,32,76,87 +145,Male,25,77,12 +146,Male,28,77,97 +147,Male,48,77,36 +148,Female,32,77,74 +149,Female,34,78,22 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len(text) + # Start of middle third + start2 = size//3 + # End of middle third + start3 = (2*size)//3 + # Get the substring + middle_third = text[start2:start3] + return middle_third + +def firstparens(text): + """Returns: substring in () + Uses the first set of parens + Param text: a string with ()""" + # Find the open parenthesis + start = text.index('(') + # Find the close parenthesis + end = text.index(‘)’) + return text[start+1:end] \ No newline at end of file diff --git a/scripts/min_max_f.py b/scripts/min_max_f.py new file mode 100644 index 0000000..a50fe52 --- /dev/null +++ b/scripts/min_max_f.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 21 15:19:07 2018 + +usage: +seq = [1, 9, 2, 4, 3, 540] +print min_max(seq) + +string = 'Hello World' +print min_max(string) + +@author: u0015831 +""" + +def min_max(t): + """Returns the smallest and largest + elements of a sequence as a tuple""" + return (min(t), max(t)) + diff --git a/scripts/mod_random.py b/scripts/mod_random.py new file mode 100644 index 0000000..17bd464 --- /dev/null +++ b/scripts/mod_random.py @@ -0,0 +1,35 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jul 2 15:11:32 2018 + +some examples on random module +@author: u0015831 +""" +# import the module +import random + +# good practice to use the seed function, and timing data as an argument for it. +from datetime import datetime +random.seed(datetime.now()) + +print(random.random()) +print(random.random()) + +print(random.uniform(10, 14)) + +random.randint(1, 100) +random.randint(1, 100) +random.randint(1, 100) +random.randint(1, 100) + +ls = [1, 2, 88, 92, 10, 3, 127] +print(random.choice(ls)) +print(random.choice(ls)) + +print(random.randrange(1,33)) +print(random.randrange(1,33)) +print(random.randrange(1,33)) + +print(ls) +print(random.shuffle(ls)) +print(ls) \ No newline at end of file diff --git a/scripts/module_1.py b/scripts/module_1.py new file mode 100644 index 0000000..45e5b16 --- /dev/null +++ b/scripts/module_1.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 5 14:00:38 2018 + +@author: u0015831 +""" + +import math +print(math.cos(math.pi)) + +import numpy as np +print(np.cos(np.pi)) \ No newline at end of file diff --git a/scripts/module_math_1.py b/scripts/module_math_1.py new file mode 100644 index 0000000..d066d84 --- /dev/null +++ b/scripts/module_math_1.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 16:20:24 2018 + +@author: u0015831 +""" + +import math +print(math.cos(90.0)) +print(math.pi) +print(math.cos(math.pi)) \ No newline at end of file diff --git a/scripts/multi_return_func.py b/scripts/multi_return_func.py new file mode 100644 index 0000000..21b92d4 --- /dev/null +++ b/scripts/multi_return_func.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jan 7 16:56:59 2019 +source: https://pythonbasics.org/multiple-return/ +@author: u0015831 +""" + +def getPerson(): + name = "Leona" + age = 35 + country = "UK" + return name,age,country + +name,age,country = getPerson() +print(name) +print(age) +print(country) \ No newline at end of file diff --git a/scripts/my_accum.py b/scripts/my_accum.py new file mode 100644 index 0000000..c9c0c79 --- /dev/null +++ b/scripts/my_accum.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 20 09:43:40 2020 + +@author: u0015831 +""" + +big_sum = 0 # accumumator as global variable + +def my_accum(val): + big_sum = big_sum + val + return +# big_sum = 0 +my_accum(5.7) +my_accum(6) +my_accum(99) +print('big_sum', big_sum) \ No newline at end of file diff --git a/scripts/my_first_script.py b/scripts/my_first_script.py new file mode 100644 index 0000000..0b04221 --- /dev/null +++ b/scripts/my_first_script.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Nov 6 09:26:53 2019 + +My first script +- create some variables +- make some calculations +@author: u0015831 +""" +a = 1 +b = 8.7 + +c = a + b +print('c = ', c) + +d = a / b +print('d = ', d) diff --git a/scripts/my_function_1.py b/scripts/my_function_1.py new file mode 100644 index 0000000..0ce34ee --- /dev/null +++ b/scripts/my_function_1.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 27 10:17:12 2018 + +can I use this file? a la Matlab function? + +@author: u0015831 +""" + +def my_function_1(): + """ this function is printing a message + this function has no passing parameters + is not returning anything + """ + print('I am my_function_1') + \ No newline at end of file diff --git a/scripts/my_module.py b/scripts/my_module.py new file mode 100644 index 0000000..e202920 --- /dev/null +++ b/scripts/my_module.py @@ -0,0 +1,13 @@ +# my_module.py +""" +Created on Thu Jun 28 16:33:19 2018 + +@author: u0015831 +""" + +"""This is a simple module. +It shows how modules work""" +x = 1+2 +x = 3*x + +print(x) \ No newline at end of file diff --git a/scripts/my_script.py b/scripts/my_script.py new file mode 100644 index 0000000..e2ad6cd --- /dev/null +++ b/scripts/my_script.py @@ -0,0 +1,4 @@ +a = 1.1 +b = 2.33 +c = a + b +print(c) \ No newline at end of file diff --git a/scripts/name.txt b/scripts/name.txt new file mode 100644 index 0000000..7fb1d87 --- /dev/null +++ b/scripts/name.txt @@ -0,0 +1 @@ +frank \ No newline at end of file diff --git a/scripts/name_01.txt b/scripts/name_01.txt new file mode 100644 index 0000000..2d95e23 --- /dev/null +++ b/scripts/name_01.txt @@ -0,0 +1 @@ +jef \ No newline at end of file diff --git a/scripts/name_02.txt b/scripts/name_02.txt new file mode 100644 index 0000000..1cd7815 --- /dev/null +++ b/scripts/name_02.txt @@ -0,0 +1,4 @@ +KU Leuvencampus Arenberg +KU Leuvencampus Arenberg +KU Leuven +campus Arenberg diff --git a/scripts/named_tuple.py b/scripts/named_tuple.py new file mode 100644 index 0000000..e29379b --- /dev/null +++ b/scripts/named_tuple.py @@ -0,0 +1,25 @@ + +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 22 17:29:17 2019 + +source: https://www.journaldev.com/22439/python-namedtuple +""" + +from collections import namedtuple + +Employee = namedtuple('Employee', ['name', 'age', 'role']) + +# below are also valid ways to namedtuple +# Employee = namedtuple('Employee', 'name age role') + +emp1 = Employee('Pankaj', 35, 'Editor') +emp2 = Employee(name='David', age=40, role='Author') + +for p in [emp1, emp2]: + print(p) + + +# accessing via name of the field +for p in [emp1, emp2]: + print(p.name, 'is a', p.age, 'years old working as', p.role) \ No newline at end of file diff --git a/scripts/nested_while_error.py b/scripts/nested_while_error.py new file mode 100644 index 0000000..abb8c45 --- /dev/null +++ b/scripts/nested_while_error.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 6 17:12:27 2023 + +@author: u0015831 +""" + +count1 = 0 +count2 = 0 +a = 100 +b = 6 + +while count1 < 10: + a = a + 1 + while count2 < 10: + b = b + 1 + count2 = count2 + 1 + print(b) + print(a) + count1 = count1 + 1 +print('the end') \ No newline at end of file diff --git a/scripts/nextSquare.py b/scripts/nextSquare.py new file mode 100644 index 0000000..426f7e8 --- /dev/null +++ b/scripts/nextSquare.py @@ -0,0 +1,27 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Nov 22 16:30:26 2019 + +source: https://www.geeksforgeeks.org/use-yield-keyword-instead-return-keyword-python/ +""" + +# A Python program to generate squares from 1 +# to 100 using yield and therefore generator + +# An infinite generator function that prints +# next square number. It starts with 1 +def nextSquare(): + i = 1; + + # An Infinite loop to generate squares + while True: + yield i*i + i += 1 # Next execution resumes + # from this point + +# Driver code to test above generator +# function +for num in nextSquare(): + if num > 1000: + break + print(num) \ No newline at end of file diff --git a/scripts/pass_arguments_1.py b/scripts/pass_arguments_1.py new file mode 100644 index 0000000..2f7ad09 --- /dev/null +++ b/scripts/pass_arguments_1.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jul 5 15:08:08 2018 + +@author: u0015831 +""" + +def double_the_values ( l ): + print(" in double_the_values : l = ", l) + for i in range (len( l )): + l [ i ] = l [ i ] * 2 + print(" in double_the_values : changed l to l = ", l) + +l_global = [0 , 1 , 2 , 3 , 10] +print(" In main : s = ", l_global ) +double_the_values ( l_global ) +print(" In main : s = ", l_global ) \ No newline at end of file diff --git a/scripts/pass_by_reference_list.py b/scripts/pass_by_reference_list.py new file mode 100644 index 0000000..dd8dc80 --- /dev/null +++ b/scripts/pass_by_reference_list.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 15 09:10:52 2020 + +@author: https://www.tutorialsteacher.com/python/python-user-defined-function +""" + +def myfunction (seq): + print ("Entering sequence inside a function: ", seq, ' id', id(seq)) + seq.append(40) + print ("Modified sequence inside a function: ", seq, ' id', id(seq)) + seq = [1, 2, 3] + print ("New sequence inside a function: ", seq, ' id', id(seq)) + return + +mylist=[10,20,30] +print('mylist before call', mylist, ' id', id(mylist)) +myfunction(mylist) +print('mylist after call', mylist, ' id', id(mylist)) + +# this will not work +# mytuple=(10,20,30) +# print('mytuple before call', mytuple, ' id', id(mytuple)) +# myfunction(mytuple) +# print('mytuple after call', mytuple, ' id', id(mytuple)) diff --git a/scripts/pass_by_reference_number.py b/scripts/pass_by_reference_number.py new file mode 100644 index 0000000..c1f49af --- /dev/null +++ b/scripts/pass_by_reference_number.py @@ -0,0 +1,20 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 15 09:04:02 2020 + +@author: https://www.tutorialsteacher.com/python/python-user-defined-function +""" + +def myfunction(arg): + print ('value received:',arg,'id:',id(arg)) + arg = 8 + print ('value changed:',arg,'id:',id(arg)) + return + +#id() function returns a unique integer corresponding to the identity of an object. + +x=10 +print ('value passed:',x, 'id:',id(x)) + +myfunction(x) +print ('value after function call:',x, 'id:',id(x)) diff --git a/scripts/pass_example.py b/scripts/pass_example.py new file mode 100644 index 0000000..401f530 --- /dev/null +++ b/scripts/pass_example.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Nov 11 19:20:37 2023 + +@author: u0015831 +""" + +n = 11 + +# use pass inside if statement +if n > 10: + # TODO complete this code! + # test to comment pass + pass +print('Hello') \ No newline at end of file diff --git a/scripts/people_data.csv b/scripts/people_data.csv new file mode 100644 index 0000000..48f3b73 --- /dev/null +++ b/scripts/people_data.csv @@ -0,0 +1,4 @@ +id,name,age,weight +1,Alice,20,120.6 +2,Freddie,21,190.6 +3,Bob,17,120.0 \ No newline at end of file diff --git a/scripts/person.csv b/scripts/person.csv new file mode 100644 index 0000000..71cf8ed --- /dev/null +++ b/scripts/person.csv @@ -0,0 +1,3 @@ +Peter,22 +Jasmine,21 +Sam,24 diff --git a/scripts/persons_bis.csv b/scripts/persons_bis.csv new file mode 100644 index 0000000..71cf8ed --- /dev/null +++ b/scripts/persons_bis.csv @@ -0,0 +1,3 @@ +Peter,22 +Jasmine,21 +Sam,24 diff --git a/scripts/print_1.py b/scripts/print_1.py new file mode 100644 index 0000000..e441600 --- /dev/null +++ b/scripts/print_1.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 28 10:29:50 2018 + +@author: u0015831 +""" + +print('Hello') + +a = 5 +b = 7 +# multiple objects +print('a =', a, ' b', b) +# use separator +l = [10, 1, 33, 34] +print(*l, sep='.') +print(*l, sep='\n') diff --git a/scripts/print_greet.py b/scripts/print_greet.py new file mode 100644 index 0000000..17700e5 --- /dev/null +++ b/scripts/print_greet.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 18 20:54:12 2019 + +@author: u0015831 +""" + +def print_greet( par ): + print('Greetings My Dear ', par) + return + +# test the function +print_greet('all_of_you') \ No newline at end of file diff --git a/scripts/print_greet_call1.py b/scripts/print_greet_call1.py new file mode 100644 index 0000000..8c29eb2 --- /dev/null +++ b/scripts/print_greet_call1.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Feb 19 08:07:33 2021 + +@author: u0015831 +""" + +from print_greet import print_greet + +print_greet('jan') +print_greet('jules') diff --git a/scripts/print_greet_call2.py b/scripts/print_greet_call2.py new file mode 100644 index 0000000..b1e285f --- /dev/null +++ b/scripts/print_greet_call2.py @@ -0,0 +1,11 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Feb 19 08:07:33 2021 + +@author: u0015831 +""" + +from print_greet_main import print_greet + +print_greet('jan') +print_greet('jules') diff --git a/scripts/print_greet_main.py b/scripts/print_greet_main.py new file mode 100644 index 0000000..a6ce7fe --- /dev/null +++ b/scripts/print_greet_main.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Nov 18 20:54:12 2019 + +@author: u0015831 +""" + +def print_greet( par ): + print('Greetings My Dear ', par) + return + +if __name__ == '__main__' : # test the code without if + print_greet('all_of_you') \ No newline at end of file diff --git a/scripts/py_call_matlab.py b/scripts/py_call_matlab.py new file mode 100644 index 0000000..ba3574b --- /dev/null +++ b/scripts/py_call_matlab.py @@ -0,0 +1,10 @@ +p# -*- coding: utf-8 -*- +""" +Created on Mon May 28 11:07:12 2018 + +@author: u0015831 +""" + +import matlab.engine +eng = matlab.engine.start_matlab() +eng.triarea(nargout=0) \ No newline at end of file diff --git a/scripts/python_test.py b/scripts/python_test.py new file mode 100644 index 0000000..e802ea7 --- /dev/null +++ b/scripts/python_test.py @@ -0,0 +1,5 @@ +print("Running test.py") +a = 1 +b = 2.3 +c = a / b +print(c) \ No newline at end of file diff --git a/scripts/python_test_1.py b/scripts/python_test_1.py new file mode 100644 index 0000000..534228f --- /dev/null +++ b/scripts/python_test_1.py @@ -0,0 +1,10 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jun 13 10:43:56 2018 + +@author: u0015831 +""" + +x = 5 +y = 7 +z = x + y \ No newline at end of file diff --git a/scripts/scope_01.py b/scripts/scope_01.py new file mode 100644 index 0000000..51dc5d2 --- /dev/null +++ b/scripts/scope_01.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun Nov 12 17:10:52 2023 + +@author: u0015831 +""" + +j = 1 +while(j <= 5): + print(j) + j = j + 1 + k = j + +print('j =', j) 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