From 2f3bcb1d3b9e0a76f0b9c538f777ce6535bd78ef Mon Sep 17 00:00:00 2001 From: gautamkr62 <134583830+gautamkr62@users.noreply.github.com> Date: Thu, 25 May 2023 23:23:42 +0530 Subject: [PATCH 1/2] Add files via upload --- 01-Numpy Exercise solved.ipynb | 608 +++++++++++++ 03-Ecommerce Purchases Exercise solved.ipynb | 689 +++++++++++++++ SF Salaries Exercise solved.ipynb | 825 ++++++++++++++++++ 3 files changed, 2122 insertions(+) create mode 100644 01-Numpy Exercise solved.ipynb create mode 100644 03-Ecommerce Purchases Exercise solved.ipynb create mode 100644 SF Salaries Exercise solved.ipynb diff --git a/01-Numpy Exercise solved.ipynb b/01-Numpy Exercise solved.ipynb new file mode 100644 index 0000000..4375be4 --- /dev/null +++ b/01-Numpy Exercise solved.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NumPy Exercises \n", + "\n", + "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. \n", + "\n", + "You are all required to upload this file on github before 23:59 on 1/6/2022." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import NumPy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 zeros " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.zeros(10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 ones" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.ones(10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 fives" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "5*(np.ones(10))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of the integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n", + " 44, 45, 46, 47, 48, 49, 50])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.arange(10,51)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of all the even integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n", + " 44, 46, 48, 50])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.arange(10,51,2)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a 3x3 matrix with values ranging from 0 to 8" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5],\n", + " [6, 7, 8]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "arr = np.arange(9)\n", + "arr.reshape(3,3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a 3x3 identity matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.eye(3)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use NumPy to generate a random number between 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.42768001])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.03178048, -0.70978693, -0.12917581, -0.19761709, -0.26355688],\n", + " [-0.24011574, 1.32974798, 0.28486771, 0.97294082, -1.02839816],\n", + " [ 0.0058211 , -2.48669916, 1.44353482, 0.70485576, 0.33153463],\n", + " [ 0.1578372 , 0.41658345, 0.98736755, -1.57613094, 0.1436936 ],\n", + " [ 2.38204722, -0.55624847, -0.70216365, -0.16046999, -0.64091478]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.randn(5,5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create the following matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 20 linearly spaced points between 0 and 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n", + " 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1. ])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linspace(0,1,21)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numpy Indexing and Selection\n", + "\n", + "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 2., 3., 4., 5.],\n", + " [ 6., 7., 8., 9., 10.],\n", + " [11., 12., 13., 14., 15.],\n", + " [16., 17., 18., 19., 20.],\n", + " [21., 22., 23., 24., 25.]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr = np.arange(25)\n", + "arr.reshape(5,5) + np.ones((5,5))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12., 13., 14., 15.],\n", + " [16., 17., 18., 19.],\n", + " [20., 21., 22., 23.]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr = np.arange(12)\n", + "arr = arr.reshape(3,4) + 12*np.ones((3,4))\n", + "arr" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "20" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2],\n", + " [ 7],\n", + " [12]])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "arr = np.arange(2,13,5)\n", + "arr.reshape(3,1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.arange(5)\n", + "arr = arr + 21*np.ones(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[16 17 18 19 20]\n", + " [21 22 23 24 25]]\n" + ] + } + ], + "source": [ + "arr = np.arange(16,26,1)\n", + "print(arr.reshape(2,5))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now do the following" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the sum of all the values in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "205" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(arr)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the standard deviation of the values in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.8722813232690143" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.std(arr)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the sum of all the columns in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "def colsum(arr,n,m):\n", + " for i in range(n):\n", + " su = 0\n", + " for j in range (m):\n", + " su += arr[j][i]\n", + " print(su, end = \"\")\n", + " " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Great Job!" + ] + } + ], + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/03-Ecommerce Purchases Exercise solved.ipynb b/03-Ecommerce Purchases Exercise solved.ipynb new file mode 100644 index 0000000..0f7d775 --- /dev/null +++ b/03-Ecommerce Purchases Exercise solved.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "# Ecommerce Purchases Exercise\n", + "\n", + "In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder.\n", + "\n", + "Please excuse anything that doesn't make \"Real-World\" sense in the dataframe, all the data is fake and made-up.\n", + "\n", + "Also note that all of these questions can be answered with one line of code.\n", + "____\n", + "** Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom. **" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ecom = pd.read_csv('Ecommerc Purchases')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Check the head of the DataFrame.**" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AddressLotAM or PMBrowser InfoCompanyCredit CardCC Exp DateCC Security CodeCC ProviderEmailJobIP AddressLanguagePurchase Price
016629 Pace Camp Apt. 448\\nAlexisborough, NE 77...46 inPMOpera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...Martinez-Herman601192906112340602/20900JCB 16 digitpdunlap@yahoo.comScientist, product/process development149.146.147.205el98.14
19374 Jasmine Spurs Suite 508\\nSouth John, TN 8...28 rnPMOpera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...Fletcher, Richards and Whitaker333775816964535611/18561Mastercardanthony41@reed.comDrilling engineer15.160.41.51fr70.73
2Unit 0065 Box 5052\\nDPO AP 2745094 vEPMMozilla/5.0 (compatible; MSIE 9.0; Windows NT ...Simpson, Williams and Pham67595766612508/19699JCB 16 digitamymiller@morales-harrison.comCustomer service manager132.207.160.22de0.95
37780 Julia Fords\\nNew Stacy, WA 4579836 vmPMMozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...Williams, Marshall and Buchanan601157850443071002/24384Discoverbrent16@olson-robinson.infoDrilling engineer30.250.74.19es78.04
423012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...20 IEAMOpera/9.58.(X11; Linux x86_64; it-IT) Presto/2...Brown, Watson and Andrews601145662320799810/25678Diners Club / Carte Blanchechristopherwright@gmail.comFine artist24.140.33.94es77.82
\n", + "
" + ], + "text/plain": [ + " Address Lot AM or PM \\\n", + "0 16629 Pace Camp Apt. 448\\nAlexisborough, NE 77... 46 in PM \n", + "1 9374 Jasmine Spurs Suite 508\\nSouth John, TN 8... 28 rn PM \n", + "2 Unit 0065 Box 5052\\nDPO AP 27450 94 vE PM \n", + "3 7780 Julia Fords\\nNew Stacy, WA 45798 36 vm PM \n", + "4 23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5... 20 IE AM \n", + "\n", + " Browser Info \\\n", + "0 Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2... \n", + "1 Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr... \n", + "2 Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ... \n", + "3 Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ... \n", + "4 Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2... \n", + "\n", + " Company Credit Card CC Exp Date \\\n", + "0 Martinez-Herman 6011929061123406 02/20 \n", + "1 Fletcher, Richards and Whitaker 3337758169645356 11/18 \n", + "2 Simpson, Williams and Pham 675957666125 08/19 \n", + "3 Williams, Marshall and Buchanan 6011578504430710 02/24 \n", + "4 Brown, Watson and Andrews 6011456623207998 10/25 \n", + "\n", + " CC Security Code CC Provider \\\n", + "0 900 JCB 16 digit \n", + "1 561 Mastercard \n", + "2 699 JCB 16 digit \n", + "3 384 Discover \n", + "4 678 Diners Club / Carte Blanche \n", + "\n", + " Email Job \\\n", + "0 pdunlap@yahoo.com Scientist, product/process development \n", + "1 anthony41@reed.com Drilling engineer \n", + "2 amymiller@morales-harrison.com Customer service manager \n", + "3 brent16@olson-robinson.info Drilling engineer \n", + "4 christopherwright@gmail.com Fine artist \n", + "\n", + " IP Address Language Purchase Price \n", + "0 149.146.147.205 el 98.14 \n", + "1 15.160.41.51 fr 70.73 \n", + "2 132.207.160.22 de 0.95 \n", + "3 30.250.74.19 es 78.04 \n", + "4 24.140.33.94 es 77.82 " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many rows and columns are there? **" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 10000 entries, 0 to 9999\n", + "Data columns (total 14 columns):\n", + "Address 10000 non-null object\n", + "Lot 10000 non-null object\n", + "AM or PM 10000 non-null object\n", + "Browser Info 10000 non-null object\n", + "Company 10000 non-null object\n", + "Credit Card 10000 non-null int64\n", + "CC Exp Date 10000 non-null object\n", + "CC Security Code 10000 non-null int64\n", + "CC Provider 10000 non-null object\n", + "Email 10000 non-null object\n", + "Job 10000 non-null object\n", + "IP Address 10000 non-null object\n", + "Language 10000 non-null object\n", + "Purchase Price 10000 non-null float64\n", + "dtypes: float64(1), int64(2), object(11)\n", + "memory usage: 1.1+ MB\n" + ] + } + ], + "source": [ + "ecom.info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the average Purchase Price? **" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50.34730200000025" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].mean()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What were the highest and lowest purchase prices? **" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "99.989999999999995" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].max() " + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].min()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have English 'en' as their Language of choice on the website? **" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Address 1098\n", + "Lot 1098\n", + "AM or PM 1098\n", + "Browser Info 1098\n", + "Company 1098\n", + "Credit Card 1098\n", + "CC Exp Date 1098\n", + "CC Security Code 1098\n", + "CC Provider 1098\n", + "Email 1098\n", + "Job 1098\n", + "IP Address 1098\n", + "Language 1098\n", + "Purchase Price 1098\n", + "dtype: int64" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[ecom['Language']==['en']].count()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have the job title of \"Lawyer\" ? **\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 30 entries, 470 to 9979\n", + "Data columns (total 14 columns):\n", + "Address 30 non-null object\n", + "Lot 30 non-null object\n", + "AM or PM 30 non-null object\n", + "Browser Info 30 non-null object\n", + "Company 30 non-null object\n", + "Credit Card 30 non-null int64\n", + "CC Exp Date 30 non-null object\n", + "CC Security Code 30 non-null int64\n", + "CC Provider 30 non-null object\n", + "Email 30 non-null object\n", + "Job 30 non-null object\n", + "IP Address 30 non-null object\n", + "Language 30 non-null object\n", + "Purchase Price 30 non-null float64\n", + "dtypes: float64(1), int64(2), object(11)\n", + "memory usage: 3.5+ KB\n" + ] + } + ], + "source": [ + "ecom[ecom['Job title']==['Lawyer']].count()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people made the purchase during the AM and how many people made the purchase during PM ? **\n", + "\n", + "**(Hint: Check out [value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) ) **" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PM 5068\n", + "AM 4932\n", + "Name: AM or PM, dtype: int64" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['AM pr PM'].valuecounts()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What are the 5 most common Job Titles? **" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Interior and spatial designer 31\n", + "Lawyer 30\n", + "Social researcher 28\n", + "Purchasing manager 27\n", + "Designer, jewellery 27\n", + "Name: Job, dtype: int64" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Job Titles'].value_counts().head(5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Someone made a purchase that came from Lot: \"90 WT\" , what was the Purchase Price for this transaction? **" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "513 75.1\n", + "Name: Purchase Price, dtype: float64" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[ecom['LOT']==['90 WT']]['Purchase Price']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the email of the person with the following Credit Card Number: 4926535242672853 **" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1234 bondellen@williams-garza.com\n", + "Name: Email, dtype: object" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[ecom['Credit Card Number']==4926535242672853]['Email']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have American Express as their Credit Card Provider *and* made a purchase above $95 ?**" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Address 39\n", + "Lot 39\n", + "AM or PM 39\n", + "Browser Info 39\n", + "Company 39\n", + "Credit Card 39\n", + "CC Exp Date 39\n", + "CC Security Code 39\n", + "CC Provider 39\n", + "Email 39\n", + "Job 39\n", + "IP Address 39\n", + "Language 39\n", + "Purchase Price 39\n", + "dtype: int64" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom(ecom[\"Credit Card Provider\"]==['American Express'] & ecom['purchase']> 95).count()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Hard: How many people have a credit card that expires in 2025? **" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1033" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(ecom['CC Exp Date'].apply(lambda x: x[3:]) == '25')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...) **" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "hotmail.com 1638\n", + "yahoo.com 1616\n", + "gmail.com 1605\n", + "smith.com 42\n", + "williams.com 37\n", + "Name: Email, dtype: int64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Email'].apply(lambda x: x.split('@')[1]).value_counts().head(5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Great Job!" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/SF Salaries Exercise solved.ipynb b/SF Salaries Exercise solved.ipynb new file mode 100644 index 0000000..e895bb6 --- /dev/null +++ b/SF Salaries Exercise solved.ipynb @@ -0,0 +1,825 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SF Salaries Exercise\n", + "\n", + "Welcome to a quick exercise for you to practice your pandas skills! We will be using the [SF Salaries Dataset](https://www.kaggle.com/kaggle/sf-salaries) from Kaggle! Just follow along and complete the tasks outlined in bold below. The tasks will get harder and harder as you go along." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Import pandas as pd.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Read Salaries.csv as a dataframe called sal.**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "sal = pd.read_csv('Salaries.csv')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Check the head of the DataFrame. **" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdEmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesAgencyStatus
01NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.00400184.25NaN567595.43567595.432011NaNSan FranciscoNaN
12GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011NaNSan FranciscoNaN
23ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.60NaN335279.91335279.912011NaNSan FranciscoNaN
34CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.0056120.71198306.90NaN332343.61332343.612011NaNSan FranciscoNaN
45PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.609737.00182234.59NaN326373.19326373.192011NaNSan FranciscoNaN
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" + ], + "text/plain": [ + " Id EmployeeName JobTitle \\\n", + "0 1 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 2 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 3 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 4 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 5 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.00 400184.25 NaN 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 NaN 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.60 NaN 335279.91 335279.91 \n", + "3 77916.00 56120.71 198306.90 NaN 332343.61 332343.61 \n", + "4 134401.60 9737.00 182234.59 NaN 326373.19 326373.19 \n", + "\n", + " Year Notes Agency Status \n", + "0 2011 NaN San Francisco NaN \n", + "1 2011 NaN San Francisco NaN \n", + "2 2011 NaN San Francisco NaN \n", + "3 2011 NaN San Francisco NaN \n", + "4 2011 NaN San Francisco NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Use the .info() method to find out how many entries there are.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 148654 entries, 0 to 148653\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 148654 non-null int64 \n", + " 1 EmployeeName 148654 non-null object \n", + " 2 JobTitle 148654 non-null object \n", + " 3 BasePay 148045 non-null float64\n", + " 4 OvertimePay 148650 non-null float64\n", + " 5 OtherPay 148650 non-null float64\n", + " 6 Benefits 112491 non-null float64\n", + " 7 TotalPay 148654 non-null float64\n", + " 8 TotalPayBenefits 148654 non-null float64\n", + " 9 Year 148654 non-null int64 \n", + " 10 Notes 0 non-null float64\n", + " 11 Agency 148654 non-null object \n", + " 12 Status 0 non-null float64\n", + "dtypes: float64(8), int64(2), object(3)\n", + "memory usage: 14.7+ MB\n" + ] + } + ], + "source": [ + "sal.info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What is the average BasePay ?**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66325.44884050643" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['BasePay'].mean()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the highest amount of OvertimePay in the dataset ? **" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "245131.88" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['Overtime'].max()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the job title of JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll). **" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['JobTitle']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How much does JOSEPH DRISCOLL make (including benefits)? **" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24 270324.91\n", + "Name: TotalPayBenefits, dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal[sal['EmployeeName']=='JOSEPH DRISCOLL']['TotalPayBenefits']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the name of highest paid person (including benefits)?**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Id EmployeeName JobTitle \\\n", + "0 1 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.0 400184.25 NaN 567595.43 567595.43 \n", + "\n", + " Year Notes Agency Status \n", + "0 2011 NaN San Francisco NaN " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal[sal[ ' TotalPayBenefits ' ] = sal['TotalPayBenefits'].max()]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdEmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesAgencyStatus
148653148654Joe LopezCounselor, Log Cabin Ranch0.00.0-618.130.0-618.13-618.132014NaNSan FranciscoNaN
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" + ], + "text/plain": [ + " Id EmployeeName JobTitle BasePay OvertimePay \\\n", + "148653 148654 Joe Lopez Counselor, Log Cabin Ranch 0.0 0.0 \n", + "\n", + " OtherPay Benefits TotalPay TotalPayBenefits Year Notes \\\n", + "148653 -618.13 0.0 -618.13 -618.13 2014 NaN \n", + "\n", + " Agency Status \n", + "148653 San Francisco NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal[sal[''TotalPayBenefits]==sal[TotalPayBenefits].min()]\n", + "it's negative --> very strange" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What was the average (mean) BasePay of all employees per year? (2011-2014) ? **" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Year\n", + "2011 63595.956517\n", + "2012 65436.406857\n", + "2013 69630.030216\n", + "2014 66564.421924\n", + "Name: BasePay, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal.groupby('Year').mean()['BasePay']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many unique job titles are there? **" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2159" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['JobTitle'].unique()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What are the top 5 most common jobs? **" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Transit Operator 7036\n", + "Special Nurse 4389\n", + "Registered Nurse 3736\n", + "Public Svc Aide-Public Works 2518\n", + "Police Officer 3 2421\n", + "Name: JobTitle, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['JobTitle'].value_counts().head(5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?) **" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "202" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum(sal[sal['Year']==2013][JobTitle].value_counts()==1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have the word Chief in their job title? (This is pretty tricky) **\n", + "**include all the cases as chief,Chief,CHIEF and more.**" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def chief_string(title):\n", + " if 'chief' in title.lower():\n", + " return True\n", + " else:\n", + " return False\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum(sal['JobTitle'].apply(lambda x: chief_string(x)))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Bonus: Is there a correlation between length of the Job Title string and Salary? **" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "627" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['title_len'] = sal['JobTitle'].apply(len)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "sal[['title_len','TotalPayBenefits']].corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " title_len TotalPayBenefits\n", + "title_len 1.000000 -0.036878\n", + "TotalPayBenefits -0.036878 1.000000" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Great Job!" + ] + } + ], + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 8eed0e2740d8c6e7b32f49ce84a5ae7e32d65c80 Mon Sep 17 00:00:00 2001 From: gautamkr62 <134583830+gautamkr62@users.noreply.github.com> Date: Fri, 2 Jun 2023 22:00:23 +0530 Subject: [PATCH 2/2] Add files via upload --- Assignment 2/02-Matplotlib Exercises.ipynb | 359 ++++++++++++++ Assignment 2/07-Seaborn Exercises.ipynb | 552 +++++++++++++++++++++ 2 files changed, 911 insertions(+) create mode 100644 Assignment 2/02-Matplotlib Exercises.ipynb create mode 100644 Assignment 2/07-Seaborn Exercises.ipynb diff --git a/Assignment 2/02-Matplotlib Exercises.ipynb b/Assignment 2/02-Matplotlib Exercises.ipynb new file mode 100644 index 0000000..731397c --- /dev/null +++ b/Assignment 2/02-Matplotlib Exercises.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n", + "\n", + "# Exercises\n", + "\n", + "Follow the instructions to recreate the plots using this data:\n", + "\n", + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "x = np.arange(0,100)\n", + "y = x*2\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 1\n", + "\n", + "** Follow along with these steps: **\n", + "* ** Create a figure object called fig using plt.figure() **\n", + "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n", + "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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IXpctqassbjWO12VL6jKLW43hinFJsrjVEK4Yl6Qei1u1ZmVL0lYWt2prs7I3NqxsSdpkcat2+it7cRHm56sekSTVh8WtWhk8l+2kLUlbWdyqBStbknbH4lblrGxJ2j2LW5Vxxbgkja6y4o6IByLiryLiixHx+WLb3og4FhH3R8SnI+KcqsancnldtiSNJzKzml8c8VXghzPzsb5tNwHfyszfjogFYG9m3jDktVnVuDUZK1tSl0UEmRmTvEeV57hjyO+/GriteHwb8KaZjkilsrIlaXJVF/fjwHeAD2TmhyLisczc2/ecRzPzeUNea3E3iCvGJalnGsVd5eK012fmwxHx/cCxiLgfGJyNnZ0bbnkZDh6Eyy7rVfaePVWPSJKarbKJOzMfLv75zxHxSeBSYDUizsvM1Yg4H/jGdq8/fPjw6cdzc3PMzc2VO2CNxMqWJFhZWWFlZWWq71nJofKIeBZwVmauR8SzgWPArwOXA49m5k0uTmuuzco+cACOHLGyJWnTNA6VVzVxXwT8Ib1D4WcDt2fmeyLiecCdwIXAg8A1mfn4kNc7cdfQ2hosLMDSkivGJWmYxk7ck3Lirh8rW5J21vTFaWoBr8uWpNnyXuUam9dlS9LsWdwamSvGJak6FrdGslnZGxt+kpckVcHi1q5Y2ZJUDxa3dmRlS1J9WNzalpUtSfVjcWuowRXjTtqSVA8Wt7bwumxJqjeLW6d5XbYk1Z/FLdbXe5XtPcYlqf4s7o5bXob9++HUKStbkprA4u4oK1uSmsni7iArW5Kay+LuECtbkprP4u4IK1uS2sHibjkrW5LaxeJuMStbktrH4m4h734mSe1lcbeMdz+TpHazuFvCypakbrC4W8DKlqTusLgbzMqWpO6xuBvKypakbrK4G8bKlqRus7gbZLOyNzasbEnqKou7Afore3ER5uerHpEkqSoWd80Nnst20pakbrO4a8rKliQNY3HXkJUtSdqOxV0jrhiXJO3E4q4Jr8uWJO2GxV0xK1uSNAqLu0JWtiRpVBZ3BVwxLkkal8U9Y8ePb737mZO2JGkUFveMWNmSpGmwuGfg+HHYv9/rsiVJk7O4S+SKcUnStFncJRmsbCdtSdI0WNxTZmVLkspkcU+R12VLkspmcU+BlS1JmhWLe0JWtiRplizuMa2twcICLC1Z2ZKk2bG4x7BZ2adOWdmSpNmyuEfguWxJUtUs7l3yXLYkqQ4s7h1Y2ZKkOrG4z8DKliTVjcU9xPp6r7JdMS5JqhuLe8Dycu8e464YlyTVkcVd8LpsSVITWNx4XbYkqTk6XdyuGJckNU1ni9sV45KkJupccVvZkqQm61Rxb1b2xoaVLUlqpk4Ud39lLy7C/HzVI5IkaTytL+7Bc9lO2pKkJmttcVvZkqQ2amVxW9mSpLZqVXG7YlyS1Ha1LO6IuDIi/jYi/i4iFnbzGq/LliR1Qe0m7og4C3g/8OPAK4C3RMR/3O75a2tw3XVw7bVw881w9Cjs2TOr0bbPyspK1UNoJfdredy35XC/1lftJm7gUuBEZj6YmU8AdwBXD3uilT19/o+1HO7X8rhvy+F+ra86nuO+AHio7/uv05vMt7juOs9lS5K6p44T965sVraHxSVJXRKZWfUYtoiI1wGHM/PK4vsbgMzMm/qeU69BS5K0S5kZk7y+jhP3M4D7gcuBh4HPA2/JzPsqHZgkSTVQu0PlmfmdiHgHcIze4rlbnbQlSeqpXXFLkqTt1fFysDMa5+YserqI2BcRyxHxNxHxlYj4xWL73og4FhH3R8SnI+KcqsfaRBFxVkR8ISLuLr53v05BRJwTER+PiPuKv93Xum8nFxHvioi/jogvR8TtEfFM9+t4IuLWiFiNiC/3bdt2X0bEjRFxovibvmI3v6NRE/eoN2fRGT0J/FJmvgL4T8AvFPvyBuCezPwhYBm4scIxNtn1wL1937tfp+N9wB9l5sXAK4G/xX07kYh4AfBO4NWZeQm9U6hvwf06rg/Tm6P6Dd2XEfFy4BrgYmAeuCUidly41qiJmxFuzqIzy8xHMvNLxeN14D5gH739eVvxtNuAN1UzwuaKiH3AG4EP9W12v04oIp4L/FhmfhggM5/MzG/jvp2GZwDPjoizge8FTuJ+HUtmfhZ4bGDzdvvyKuCO4m/5AeAEQ+5bMqhpE/ewm7NcUNFYWiMiXgy8CvgccF5mrkJvcgfOrW5kjfVe4JeB/gUk7tfJXQR8MyI+XJyGWIyIZ+G+nUhm/hPwO8DX6E3Y387Me3C/TtO52+zLwTntJLuY05o2cWvKIuI5wCeA64vyHlyt6OrFEUTETwCrxdGMMx3ycr+O7mzg1cDNmflq4F/oHYL0b3YCEbGHXhG+CHgBvfL+SdyvZZpoXzZt4j4JvLDv+33FNo2hOCz2CeCjmXlXsXk1Is4rfn4+8I2qxtdQrweuioivAv8HuCwiPgo84n6d2NeBhzLzL4rv/4DeRO7f7GTeAHw1Mx/NzO8Afwj8KO7XadpuX54ELux73q7mtKZN3H8OvDQiXhQRzwTeDNxd8Zia7Chwb2a+r2/b3cDPFI+vBe4afJG2l5m/kpkvzMyX0Pv7XM7MnwKWcL9OpDjU+FBE/GCx6XLgb/BvdlJfA14XEd9TLIy6nN7CSvfr+IKtR9y225d3A28uVvFfBLyU3k3HzvzmTbuOOyKupLeydPPmLO+peEiNFBGvB/4E+Aq9wzYJ/Aq9P5o76f1X4IPANZn5eFXjbLKI+C/A/8zMqyLiebhfJxYRr6S36O+7gK8Cb6W3sMp9O4GIOETvPzSfAL4I/CzwH3C/jiwiPgbMAc8HVoFDwCeBjzNkX0bEjcBBevv++sw8tuPvaNrELUlSlzXtULkkSZ3mxC1JUoM4cUuS1CBO3JIkNYgTtyRJDeLELUlSgzhxS5LUIE7ckiQ1iBO3JAAi4kci4q+K2y8+OyL+uvi8YEk14p3TJJ0WEb9B7/OYv5feB3rcVPGQJA1w4pZ0WkR8F70P8/n/wI+m/wch1Y6HyiX1+z7gOfQ+YOJ7Kh6LpCEsbkmnRcRd9D5H/CLgBZn5zoqHJGnA2VUPQFI9RMRPARuZeUdEnAX8WUTMZeZKxUOT1MfiliSpQTzHLUlSgzhxS5LUIE7ckiQ1iBO3JEkN4sQtSVKDOHFLktQgTtySJDWIE7ckSQ3yb9M5KmTbHOCcAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.plot(x,y)\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1 = fig.add_axes([0,0,1,1])\n", + "ax2 = fig.add_axes([0.2,0.5,.2,.2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Now plot (x,y) on both axes. And call your figure object to show it.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax1.plot(x,y)\n", + "ax2.plot(x,y)\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 3\n", + "\n", + "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure\n", + "ax1 = fig.add_axes(0,0,1,1)\n", + "ax2 = flg.add_axes(0.2,0.5,0.4,0.4)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax1.plot(x,z)\n", + "ax1.set_xlabel('x')\n", + "ax1.set_ylabel('z')\n", + "ax2.plot(x,y)\n", + "ax2.set_xlabel('x')\n", + "ax2.set_ylabel('y')\n", + "ax2.set_xlim([20,22])\n", + "ax2.set_ylim([30,50])\n", + "fig1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise 4\n", + "\n", + "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "axes[0].plot(x,y,linestyle='--',color='blue')\n", + "axes[1].plot(x,z,linestyle='-',color='red')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))\n", + "axes[0].plot(x,y,linestyle='--',color='blue')\n", + "axes[1].plot(x,z,linestyle='-',color='red')\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Great Job!" + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Assignment 2/07-Seaborn Exercises.ipynb b/Assignment 2/07-Seaborn Exercises.ipynb new file mode 100644 index 0000000..e273fac --- /dev/null +++ b/Assignment 2/07-Seaborn Exercises.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seaborn Exercises\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic = sns.load_dataset('titanic')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", + "
" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + "\n", + " who adult_male deck embark_town alive alone \n", + "0 man True NaN Southampton no False \n", + "1 woman False C Cherbourg yes False \n", + "2 woman False NaN Southampton yes True \n", + "3 woman False C Southampton yes False \n", + "4 man True NaN Southampton no True " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercises\n", + "\n", + "** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.**\n", + "\n", + "** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* **" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CODE HERE\n", + "# REPLICATE EXERCISE PLOT IMAGE BELOW\n", + "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n", + "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(x='fare',y='age',data=titanic)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CODE HERE\n", + "# REPLICATE EXERCISE PLOT IMAGE BELOW\n", + "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n", + "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WAADOnz+PXr16obGx0b22f1JSEmpra0WWQERE9xB+Vo9SqURBQQFWrlyJSZMmweVyuZ9Tq9Uwm82iSyAiojv45MvdNWvW4MqVK5g6dSpu3brlftxqtUKj0Tzy9Uaj0aP98AbwvmGz2Tzuk8dpk6caiCei7zrbZfeJ1139J7Svdu/ejYsXL+LNN9/EM888A6VSiZEjR6Kurg6jR49GTU0NEhISHtlOXFycR/tTqVSAneEvmkql8rhPHqfNWwFyJ6tAJqLvOtt1tlm7vV262+P038PeIIQG/6uvvopFixYhMzMTDocDhYWFGDx4MAoLC2G326HVapGamiqyBAoQFosF7e1tvEuUQO1XLVCEd/i7DHoKCA3+iIgIbNy48b7HDQaDyN0SEdFDcFqOngqRkZFw9QjhPXcF+r+5nyBSGeHvMugpwLV6iIgkw+AnIpJM0E31hN1shXbnWn+X4ZEQ2+2zWDpUgfPxO+xmK6CO8ncZRPQEgir4o6ICK5BMba0AgO+re/m5ksegjgq4vzMR3S2ogr87liv1pc4lYcvKyvxcCRHJhHP8RESSYfATEUmGwU9EJBkGPxGRZBj8RESSYfATEUkmqE7nJCL/sFgsaG+zd+t9YeluVy12hHdYuqUtjviJiCTDET8RPbHIyEj0CLFjY/Ywf5cStN4uOwFlRGS3tMXgp6dG+1VLQN2IxW5tBwCEqcP9XIln2q9a0DMqcNaFInEY/PRUCMT1f0y3bt9qsGfPwAjTnlERAfl3pu7H4KenQqCtswRwrSUKXPxyl4hIMgx+IiLJCJvqcTgcWLx4Mc6dOwe73Y7Zs2djyJAhKCgogFKpRExMDIqKikTtnoiIHkBY8P/1r3/F9773Paxbtw6tra14/fXXMWzYMOj1euh0OhQVFaG6uhopKSmiSiAioi4Im+qZMGEC5s2bBwDo6OhASEgIGhsbodPpAABJSUmora0VtXsiInoAYcEfERGBHj16wGKxYN68eZg/fz5cLpf7ebVaDbPZLGr3RET0AEJP5/z2228xd+5cZGZm4rXXXsP777/vfs5qtUKj0XjUjtFoFFWiX9lsNgDBe3zBjv33HZvNxnPDfcBms3XLvzdhfWUymZCTk4P33nsPCQkJAIDhw4ejvr4e8fHxqKmpcT/+KHFxcaLK9CuVSgUgeI8v2LH/vqNSqeBss/q7jKCnUqk8/vf2sDcIYcG/ZcsWtLa2YvPmzdi0aRMUCgWWLFmClStXwm63Q6vVIjU1VdTuiYjoAYQF/5IlS7BkyZL7HjcYDKJ2SUREHuAFXEREkuH3MUTULa5aAudGLNb2DgCAOjzEz5V47qrFju5aXJXBT0RPLNBW/bxlNQEAekZ8z8+VeC4qovv+zgx+Inpigba6quwrq3KOn4hIMgx+IiLJMPiJiCTD4CcikgyDn4hIMgx+IiLJMPiJiCTD4CcikgyDn4hIMgx+IiLJMPiJiCTD4CcikgyDn4hIMgx+IiLJMPiJiCQjPPgbGhqQlZUFAGhpaUF6ejoyMzNRXFwsetdERNQFocH/xz/+EYWFhbDb7QCAkpIS6PV6lJeXw+l0orq6WuTuiYioC0KDPzo6Gps2bXL/fPz4ceh0OgBAUlISamtrRe6eiIi6IDT4x48fj5CQ725m7HK53NtqtRpms1nk7omIqAs+/XJXqfxud1arFRqNxpe7JyIi+Phm6y+99BLq6+sRHx+PmpoaJCQkePQ6o9EouDL/sNlsAIL3+IId+y9wyd53Pg3+/Px8LF26FHa7HVqtFqmpqR69Li4uTnBl/qFSqQAE7/EFO/Zf4JKh7x72piY8+F944QVUVlYCAAYOHAiDwSB6l0RE9BC8gIuISDIMfiIiyTD4iYgkw+AnIpIMg5+ISDIMfiIiyTD4iYgkw+AnIpIMg5+ISDIMfiIiyTD4iYgkw+AnIpKMT1fnJAomra2t/i6ByCsMfgp6W7duxf79+7u93fb2dgBAdnZ2t7edmJiI3Nzcbm+XCOBUD5FX7hztc+RPgYYjfgp6ubm53T56njhxonvbZrOhrKysW9snEokjfiIiyTD4ibzwgx/8oMttokDA4CfyQktLS5fbRIGAwU9EJBmff7nrcrmwbNkyNDU1QaVSYdWqVXjxxRd9XQbRE+nbty+sVqt7myiQ+Dz4q6urYbPZUFlZiYaGBpSUlGDz5s2+LuOxiDoP3GQyARBzHjjAc8FF4lQPBTKfB7/RaERiYiIAIDY2FseOHfN1CU+N8PBwf5dARBLyefBbLBb07NnzuwJCQ+F0OqFUPr1fN4g4D5wCW8+ePWE2m93bJAY/bYvh87SNjIx0z40CeOpDn6grhYWFXW5TYAgPD5f6E7fC5XK5fLnDvXv34t///jdKSkrw5ZdfYvPmzSgtLX3g7xuNRh9WR0QUPOLi4rp83OfBf+dZPQBQUlKCQYMG+bIEIiKp+Tz4iYjIvzi5TkQkGQY/EZFkGPxERJJh8BMRSYY3YhHo3LlzmDx5MkaMGAGXywWFQoGEhAQAwJw5cx75+hs3bmD//v2YNGmS6FKlVVpaitraWjgcDiiVSixcuBAjRowQvl+9Xo+ZM2ciPj5e+L5ksnbtWhw7dgwmkwnt7e0YMGAATp06hZ/85Cf44IMP7vrdkpIS/OY3v0H//v0f2N706dOxYcMGPP/886JL9ykGv2AxMTFe353pxIkT2LdvH4NfkK+//hr79u1DZWUlgNt/74KCAuzatcvPlZG38vPzAQCff/45mpubodfrUVdXh23btt33u4sWLfJ1eU8NBr9g954tW1dXh8rKSqxfvx5jx46FVqvFkCFDEBcXh61btyIsLAz9+vXD+vXrsWXLFjQ1NWH79u2YNm2an44geEVGRuLChQvYsWMHEhMTMWzYMGzfvh1fffUVVq5cCQDo3bs3Vq9ejcjISKxYsQJHjhyBw+FAXl4efvazn2Ht2rUwGo1QKBSYNGkSsrKysGjRIoSFheHcuXMwmUxYs2YNhg8fjoqKCuzYsQPPPvssrl696uejl0tzczPefPNNXLlyBWPHjsXcuXORlZWF5cuXY8+ePTh8+DBu3ryJVatWYffu3Thw4AD69++P69ev+7t0IRj8gp06dQrZ2dnuqZ5p06ZBoVAAAC5cuIDdu3dDo9Fg3rx5mDVrFl599VXs3r0bVqsVs2fPxrZt2xj6gjz33HP4wx/+AIPBgE2bNiEiIgJvv/02PvnkE6xevRparRY7duzA1q1bMWrUKFy/fh3bt2+H2WzGp59+CqVSiXPnzuEvf/kLHA4HMjIy8OMf/xgAMGDAACxfvhzbt2/Htm3bkJeXh7KyMuzZswcAkJaW5s9Dl47dbsfmzZvhcDjcwX8nrVaLxYsX49ixYzAajdi5cycsFgtSU1P9VLFYDH7B7p3qqaurc2/36dMHGo0GwO2PnVu2bIHBYIBWq0VKSorPa5VNS0sL1Go1Vq9eDQA4fvw4Zs2aBZvNhuLiYgCAw+FAdHQ0mpub8corrwC4vSjb7373O3zyySfuS+JDQ0Px8ssv49SpUwCA4cOHAwD69++P//3vf2hpacHQoUMRGnr7v9yoUaN8eqyyi4mJQWhoKEJDQxESEnLf852rB5w5cwYjR44EcPsTYUxMjE/r9BWe1SPYwy6M7hz5A3CPCg0GA5xOJ/75z39CqVSio6PDF2VKqampCcuXL4fdbgcAREdHQ6PRIDo6GuvWrUNZWRkWLFjgnpI7cuQIAMBsNiMnJwdDhgxxryVlt9tx+PBhd4Dc2bedbZ88eRI2mw0dHR1obGz04ZHSvf1xr86FIocMGeLu55s3b7rfyIMNR/yCPeofXKeXX34Zv/3tb6FWq6FWqzF27Fi0t7fj5MmTKCsrE7Z8rMzGjx+P06dPY+rUqVCr1XA6nVi4cCG+//3v491330VHRweUSiVWrVqF6OhoHDx4EOnp6XA6nZg7dy5++tOf4r///S9mzJgBu92OiRMnukf69+rTpw9yc3Mxffp09OnTB2q12sdHS/fq6v/msGHDkJiYiLS0NDz77LOIioryQ2Xica0eIiLJcKqHiEgyDH4iIskw+ImIJMPgJyKSDIOfiEgyDH4iIskw+IkeISsrC/X19f4ug6jbMPiJiCTDK3eJ7vH++++juroaYWFh+NWvfuV+vKOjA8uWLcPJkydx5coVDBo0CB999BFsNhveeecdmEwmAMDcuXMxduxYfPrpp9i1axdCQkIwatQo9/o/RP7G4Ce6wz/+8Q98+eWX2LNnD+x2O2bOnAmbzQYAOHz4MFQqFSorK+FyuZCdnY0vvvgCVqsVAwYMwJYtW/D111+jqqoKSUlJKC0txYEDB6BUKrF8+XJcunQJ/fr18/MREjH4ie5SX1+PCRMmuFdy3LVrF7KysgAAOp0OvXv3RkVFBZqbm9HS0gKr1Yof/vCH2LBhAy5cuIDk5GTMmTMHISEh+NGPfoS0tDSMGzcOGRkZDH16anCOn+gOncsmdzp79iza2toAAPv27cOCBQugVquRlpYGnU4H4PbKm3//+98xefJkHDp0CFOnTgUAbNq0yT29k5OTg0OHDvnwSIgejMFPdIf4+Hjs3bsXDocDbW1tyM3NxaVLlwAABw8exMSJE/GLX/wCffr0QX19PTo6OlBRUYEPP/wQP//5z/Hee+/h6tWruHbtGiZMmIChQ4ciLy8PY8aMQVNTk5+Pjug2rs5JdI+NGzfiX//6FwAgIyMDf/vb35CXl4devXrhnXfeQVhYGFQqFfr16wetVotZs2ZBr9fj/PnzCAsLQ1paGjIyMvCnP/0J27ZtQ0REBJ5//nmsXbsWPXr08PPRETH4iYikw6keIiLJMPiJiCTD4CcikgyDn4hIMgx+IiLJMPiJiCTD4CcikgyDn4hIMv8P9qlbLeIn4+kAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CODE HERE\n", + "# REPLICATE EXERCISE PLOT IMAGE BELOW\n", + "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n", + "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.swarmplot(x='class',y='age',data=titanic,palette='rainbow')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CODE HERE\n", + "# REPLICATE EXERCISE PLOT IMAGE BELOW\n", + "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n", + "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CODE HERE\n", + "# REPLICATE EXERCISE PLOT IMAGE BELOW\n", + "# BE CAREFUL NOT TO OVERWRITE CELL BELOW\n", + "# THAT WOULD REMOVE THE EXERCISE PLOT IMAGE!" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Great Job!\n", + "\n", + "### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section!" + ] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}