From 18cb3409f7df12c9f0ddae1eabb860fb430cd8f1 Mon Sep 17 00:00:00 2001 From: PrathameshLadhe <121498849+PrathameshLadhe@users.noreply.github.com> Date: Thu, 25 May 2023 14:37:19 +0530 Subject: [PATCH 1/3] Add files via upload --- Assignment 1/220811_Prathamesh_01_Numpy.ipynb | 636 ++++++++++++++ .../220811_Prathamesh_02_salaries.ipynb | 795 ++++++++++++++++++ Assignment 1/220811_Prathamesh_03_Ecom.ipynb | 687 +++++++++++++++ 3 files changed, 2118 insertions(+) create mode 100644 Assignment 1/220811_Prathamesh_01_Numpy.ipynb create mode 100644 Assignment 1/220811_Prathamesh_02_salaries.ipynb create mode 100644 Assignment 1/220811_Prathamesh_03_Ecom.ipynb diff --git a/Assignment 1/220811_Prathamesh_01_Numpy.ipynb b/Assignment 1/220811_Prathamesh_01_Numpy.ipynb new file mode 100644 index 0000000..0dd1f2f --- /dev/null +++ b/Assignment 1/220811_Prathamesh_01_Numpy.ipynb @@ -0,0 +1,636 @@ +{ + "cells": [ + { + "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. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import NumPy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 zeros " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 ones" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.ones(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 10 fives" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.ones(10) * 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of the integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(10,51)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of all the even integers from 10 to 50" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(10,51,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a 3x3 matrix with values ranging from 0 to 8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5],\n", + " [6, 7, 8]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(9).reshape(3,3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a 3x3 identity matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.eye(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use NumPy to generate a random number between 0 and 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.22017974])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.rand(1)" + ] + }, + { + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[-0.36138746, 0.43800075, 0.3609818 , 0.53101901,\n", + " -0.20140857],\n", + " [-1.60433186, 0.46393485, -0.70699356, 0.33485919,\n", + " 1.47706039],\n", + " [-0.50865058, 0.33873972, 0.46700656, 0.9272898 ,\n", + " -1.45278073],\n", + " [ 0.0925855 , -1.4924024 , 2.09368936, -0.5896731 ,\n", + " -0.76158015],\n", + " [-0.20100313, 2.20272426, -0.41125413, 1.15451551,\n", + " 1.68728908]]])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.randn(1,5,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create the following matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n", + " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n", + " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n", + " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n", + " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n", + " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n", + " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n", + " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n", + " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n", + " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(1,101).reshape(10,10) / 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create an array of 20 linearly spaced points between 0 and 1:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n", + " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n", + " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n", + " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linspace(0,1,20)" + ] + }, + { + "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": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr= np.arange(1,26).reshape(5,5)\n", + "arr" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[12, 13, 14, 15],\n", + " [17, 18, 19, 20],\n", + " [22, 23, 24, 25]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([[12,13,14,15],[17,18,19,20],[22,23,24,25]])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr[3,4]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2],\n", + " [ 7],\n", + " [12]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr[0:3,1].reshape(3,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([21, 22, 23, 24, 25])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[16, 17, 18, 19, 20],\n", + " [21, 22, 23, 24, 25]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr[3:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now do the following" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "mat = 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]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the sum of all the values in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "325" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat = arr\n", + "np.sum(mat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the standard deviation of the values in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.211102550927978" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.std(mat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get the sum of all the columns in mat" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([55, 60, 65, 70, 75])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat[0]+mat[1]+mat[2]+mat[3]+mat[4]\n", + "# Second Solution\n", + "##arr2 = np.zeros(5).astype(int)\n", + "##arrlen = arr2.size\n", + "##for i in range(arrlen):\n", + "## arr2 = arr2 + mat[i]\n", + "##arr2 " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Assignment 1/220811_Prathamesh_02_salaries.ipynb b/Assignment 1/220811_Prathamesh_02_salaries.ipynb new file mode 100644 index 0000000..a8db754 --- /dev/null +++ b/Assignment 1/220811_Prathamesh_02_salaries.ipynb @@ -0,0 +1,795 @@ +{ + "cells": [ + { + "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." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Import pandas as pd.**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Read Salaries.csv as a dataframe called sal.**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "sal = pd.read_csv('Salaries.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Check the head of the DataFrame. **" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Use the .info() method to find out how many entries there are.**" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "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()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What is the average BasePay ?**" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66325.4488404877" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['BasePay'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the highest amount of OvertimePay in the dataset ? **" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "245131.88" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['OvertimePay'].max()" + ] + }, + { + "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": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24 CAPTAIN, FIRE SUPPRESSION\n", + "Name: JobTitle, dtype: object" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal[(sal['EmployeeName'] == 'JOSEPH DRISCOLL')]['JobTitle']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How much does JOSEPH DRISCOLL make (including benefits)? **" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24 270324.91\n", + "Name: TotalPayBenefits, dtype: float64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal[(sal['EmployeeName'] == 'JOSEPH DRISCOLL')]['TotalPayBenefits']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the name of highest paid person (including benefits)?**" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "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": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minpay = sal['TotalPayBenefits'].min()\n", + "sal[(sal['TotalPayBenefits']== minpay)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What was the average (mean) BasePay of all employees per year? (2011-2014) ? **" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "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": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sal[(sal['Year'] >= 2011) & (sal['Year'] <= 2014)]\n", + "df.groupby('Year')['BasePay'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many unique job titles are there? **" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2159" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['JobTitle'].nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What are the top 5 most common jobs? **" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "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": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sal['JobTitle'].value_counts().head()" + ] + }, + { + "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": [] + }, + { + "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.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "627" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total1 = sal[sal['JobTitle'].str.contains('Chief') | sal['JobTitle'].str.contains('CHIEF')]\n", + "count1 = sal['JobTitle'].str.contains(r'\\bchief\\b', case=False).sum()\n", + "count1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Bonus: Is there a correlation between length of the Job Title string and Salary? **" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, + "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
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" + ], + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many rows and columns are there? **" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 10000 entries, 0 to 9999\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Address 10000 non-null object \n", + " 1 Lot 10000 non-null object \n", + " 2 AM or PM 10000 non-null object \n", + " 3 Browser Info 10000 non-null object \n", + " 4 Company 10000 non-null object \n", + " 5 Credit Card 10000 non-null int64 \n", + " 6 CC Exp Date 10000 non-null object \n", + " 7 CC Security Code 10000 non-null int64 \n", + " 8 CC Provider 10000 non-null object \n", + " 9 Email 10000 non-null object \n", + " 10 Job 10000 non-null object \n", + " 11 IP Address 10000 non-null object \n", + " 12 Language 10000 non-null object \n", + " 13 Purchase Price 10000 non-null float64\n", + "dtypes: float64(1), int64(2), object(11)\n", + "memory usage: 1.1+ MB\n" + ] + } + ], + "source": [ + "ecom.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the average Purchase Price? **" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50.347302" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].mean()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What were the highest and lowest purchase prices? **" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "99.99" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].max()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Purchase Price'].min()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have English 'en' as their Language of choice on the website? **" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[(ecom['Language'] == 'en')].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** How many people have the job title of \"Lawyer\" ? **\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 30 entries, 470 to 9979\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Address 30 non-null object \n", + " 1 Lot 30 non-null object \n", + " 2 AM or PM 30 non-null object \n", + " 3 Browser Info 30 non-null object \n", + " 4 Company 30 non-null object \n", + " 5 Credit Card 30 non-null int64 \n", + " 6 CC Exp Date 30 non-null object \n", + " 7 CC Security Code 30 non-null int64 \n", + " 8 CC Provider 30 non-null object \n", + " 9 Email 30 non-null object \n", + " 10 Job 30 non-null object \n", + " 11 IP Address 30 non-null object \n", + " 12 Language 30 non-null object \n", + " 13 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'] == 'Lawyer')].info()" + ] + }, + { + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PM 5068\n", + "AM 4932\n", + "Name: AM or PM, dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['AM or PM'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What are the 5 most common Job Titles? **" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Job'].value_counts().head()" + ] + }, + { + "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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "513 75.1\n", + "Name: Purchase Price, dtype: float64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[(ecom[\"Lot\"]==\"90 WT\")]['Purchase Price']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** What is the email of the person with the following Credit Card Number: 4926535242672853 **" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1234 bondellen@williams-garza.com\n", + "Name: Email, dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[(ecom['Credit Card']==4926535242672853)]['Email']" + ] + }, + { + "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": 14, + "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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom[(ecom['Purchase Price']>95) & (ecom['CC Provider']=='American Express')].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** Hard: How many people have a credit card that expires in 2025? **" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1033" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " ecom[(ecom['CC Exp Date']=='02/25') | (ecom['CC Exp Date']=='01/25') | (ecom['CC Exp Date']=='03/25') | (ecom['CC Exp Date']=='04/25') | (ecom['CC Exp Date']=='05/25') | (ecom['CC Exp Date']=='06/25') | (ecom['CC Exp Date']=='07/25') | (ecom['CC Exp Date']=='08/25') | (ecom['CC Exp Date']=='09/25') | (ecom['CC Exp Date']=='10/25') | (ecom['CC Exp Date']=='11/25') | (ecom['CC Exp Date']=='12/25')]['CC Exp Date'].count()" + ] + }, + { + "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": 16, + "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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ecom['Email'].str.split('@').str[-1].value_counts().head()\n" + ] + }, + { + "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": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 0048e156b20c10f64ea562cba458858db25b1650 Mon Sep 17 00:00:00 2001 From: PrathameshLadhe <121498849+PrathameshLadhe@users.noreply.github.com> Date: Fri, 2 Jun 2023 23:47:28 +0530 Subject: [PATCH 2/3] Create Assignment 2 --- Assignment 2 | 1 + 1 file changed, 1 insertion(+) create mode 100644 Assignment 2 diff --git a/Assignment 2 b/Assignment 2 new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Assignment 2 @@ -0,0 +1 @@ + From b6aa5de10cef98a63d7626aead6fa6c37526ec9f Mon Sep 17 00:00:00 2001 From: PrathameshLadhe <121498849+PrathameshLadhe@users.noreply.github.com> Date: Sat, 3 Jun 2023 00:00:39 +0530 Subject: [PATCH 3/3] Add files via upload --- 220811_Prathamesh_Matplotlb.ipynb.ipynb | 474 ++++++++++++++++++++++++ 1 file changed, 474 insertions(+) create mode 100644 220811_Prathamesh_Matplotlb.ipynb.ipynb diff --git a/220811_Prathamesh_Matplotlb.ipynb.ipynb b/220811_Prathamesh_Matplotlb.ipynb.ipynb new file mode 100644 index 0000000..c2caecd --- /dev/null +++ b/220811_Prathamesh_Matplotlb.ipynb.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "029voA6ztlTM" + }, + "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": 35, + "metadata": { + "id": "w2zqnEGttlTP" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "x = np.arange(0,100)\n", + "y = x*2\n", + "z = x**2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ytcKbl24tlTR" + }, + "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": 36, + "metadata": { + "id": "zcBOAOkttlTS" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E2QLUkb8tlTT" + }, + "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": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 599 + }, + "id": "GIPOG882tlTT", + "outputId": "45b740e5-e852-45e1-dfc3-4ba3cfc5d3a5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(0.0, 200.0)" + ] + }, + "metadata": {}, + "execution_count": 37 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_axes([0,0,1,1])\n", + "ax.plot(x,y,'b')\n", + "ax.set_xlabel('x')\n", + "ax.set_ylabel('y')\n", + "ax.set_title('title')\n", + "ax.set_xlim([0,100])\n", + "ax.set_ylim([0,200])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KwmZeXh6tlTV" + }, + "source": [ + "## Exercise 2\n", + "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "9L0C4-51tlTW", + "outputId": "18882945-c291-4a33-e899-39f0726fe9e8" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "figu = plt.figure()\n", + "ax1 = figu.add_axes([0,0,1,1])\n", + "ax2 = figu.add_axes([0.2,0.5,0.2,0.2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpWm8HGYtlTW" + }, + "source": [ + "** Now plot (x,y) on both axes. And call your figure object to show it.**" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 565 + }, + "id": "lWh0l6nTtlTX", + "outputId": "d7056b88-1ac9-4137-9ab7-777cbbfd1949" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "ax1.plot(x,y,'r')\n", + "ax2.plot(x,y,'r')\n", + "ax1.set_xlim([0,100])\n", + "ax1.set_ylim([0,200])\n", + "ax2.set_xlim([0,100])\n", + "ax2.set_ylim([0,200])\n", + "ax1.set_xlabel('x')\n", + "ax1.set_ylabel('y')\n", + "ax2.set_xlabel('x')\n", + "ax2.set_ylabel('y')\n", + "figu" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NjVR0Ap7tlTX" + }, + "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": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 546 + }, + "id": "Wa7-dP-ztlTY", + "outputId": "dc9bd1b0-a675-4cd9-84b4-d452825a21d4" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig2 = plt.figure()\n", + "ax1 = fig2.add_axes([0,0,1,1])\n", + "ax2 = fig2.add_axes([0.2,0.5,0.4,0.4])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1jxU1-mNtlTY" + }, + "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": 41, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "bYDK2_OltlTY", + "outputId": "af41fd54-66e7-4d80-b5fc-b003ffc7f19a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig = plt.figure()\n", + "ax1=fig.add_axes([0,0,1,1])\n", + "ax2=fig.add_axes([.2,.5,.4,.4])\n", + "ax1.plot(x,z,'blue')\n", + "ax1.set_xlim([0,100])\n", + "ax1.set_ylim([0,10000])\n", + "ax2.plot(x,y,'blue')\n", + "ax2.set_xlim([20,22])\n", + "ax2.set_ylim([30,50])\n", + "ax1.set_xlabel('X')\n", + "ax1.set_ylabel('Z')\n", + "ax2.set_xlabel('X')\n", + "ax2.set_ylabel('Y')\n", + "ax2.set_title('zoom')\n", + "display(fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tKeSvyBmtlTY" + }, + "source": [ + "## Exercise 4\n", + "\n", + "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "dHcGUcRgtlTY", + "outputId": "0baa0ddc-2162-4ad9-8c64-1a5c5b2af3c7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig,axes = plt.subplots(nrows=1,ncols=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TLw1xkPDtlTY" + }, + "source": [ + "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "A2Jau-s1tlTZ", + "outputId": "6030dc14-e60a-42cb-c5de-32092e03141c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 43 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig,axes = plt.subplots(nrows=1,ncols=2)\n", + "axes[0].set_xlim([0,100])\n", + "axes[0].set_ylim([0,200])\n", + "axes[1].set_xlim([0,100])\n", + "axes[1].set_ylim([0,10000])\n", + "axes[0].plot(x,y,color='blue',lw=3,ls='--')\n", + "axes[1].plot(x,z,color='red',lw=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tFx3LjimtlTZ" + }, + "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": 44, + "metadata": { + "id": "QrQzYdkGtlTZ", + "outputId": "5ab204f8-a3aa-443d-b3f4-47071cbb959a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 44 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(10,2))\n", + "axes[0].set_xlim([0,100])\n", + "axes[0].set_ylim([0,200])\n", + "axes[1].set_xlim([0,100])\n", + "axes[1].set_ylim([0,10000])\n", + "axes[0].plot(x,y,color='blue',lw=4)\n", + "axes[1].plot(x,z,color='red',lw=2,ls='--')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o5jlhrE-tlTZ" + }, + "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": "3.10.9" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file