└── DataAnalysis-using-Python-master ├── Assignments ├── Assignment_01.ipynb ├── Assignment_02.ipynb ├── Assignment_03.ipynb ├── csvFiles.zip └── kiwisaver_sample_wk9.csv ├── Course Content.docx ├── DA2_chapter1.ipynb ├── DA2_chapter2.ipynb ├── DA2_chapter3.ipynb ├── DA2_chapter4.ipynb ├── DA2_chapter5.ipynb ├── DA2_chapter6.ipynb └── data ├── AMZN.csv ├── Book1.csv ├── Book2.csv ├── Bronze.csv ├── FB.csv ├── GOOG.csv ├── Gold.csv ├── MELBOURNE_HOUSE_PRICES_LESS.csv ├── flights.csv ├── monthly_max_temp.csv ├── monthly_mean_temp.csv ├── population_00.csv ├── population_01.csv ├── population_02.csv ├── q1_rainfall_2013.csv └── q1_rainfall_2014.csv /DataAnalysis-using-Python-master/Assignments/Assignment_01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Assignment01.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [] 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "code", 21 | "execution_count": 11, 22 | "metadata": { 23 | "id": "mvl9kklGIdKC" 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "#import all the libraries here\n", 28 | "import numpy as np\n", 29 | "import pandas as pd" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "source": [ 35 | "from google.colab import drive\n", 36 | "drive.mount('/content/drive')" 37 | ], 38 | "metadata": { 39 | "colab": { 40 | "base_uri": "https://localhost:8080/" 41 | }, 42 | "id": "G_fV7jG0KO1e", 43 | "outputId": "24d4ad89-dcd1-4805-fa2d-f464c5214a77" 44 | }, 45 | "execution_count": 12, 46 | "outputs": [ 47 | { 48 | "output_type": "stream", 49 | "name": "stdout", 50 | "text": [ 51 | "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" 52 | ] 53 | } 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "source": [ 59 | "cd /content/drive/MyDrive/DataAnalysis-with-Python" 60 | ], 61 | "metadata": { 62 | "colab": { 63 | "base_uri": "https://localhost:8080/" 64 | }, 65 | "id": "BA6GLLNjKdaq", 66 | "outputId": "a5882b68-68e0-456c-e796-56c71f362e46" 67 | }, 68 | "execution_count": 4, 69 | "outputs": [ 70 | { 71 | "output_type": "stream", 72 | "name": "stdout", 73 | "text": [ 74 | "/content/drive/MyDrive/DataAnalysis-with-Python\n" 75 | ] 76 | } 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "source": [ 82 | "##Task 1:\t\n", 83 | "Inspecting your Data.\n", 84 | "\tread and disect your datafile \"kiwisaver_sample_wk9.csv\" to figure out list of all possible dirty data problems in your data." 85 | ], 86 | "metadata": { 87 | "id": "Z7XTp_iUgdZb" 88 | } 89 | }, 90 | { 91 | "cell_type": "code", 92 | "source": [ 93 | "" 94 | ], 95 | "metadata": { 96 | "id": "rsc6vGOlyg7e" 97 | }, 98 | "execution_count": null, 99 | "outputs": [] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "source": [ 104 | "##Task 2:\t\n", 105 | "You must have noticed we have some data with missing \"Morningstar Ratings\", createa method and figure out a way to imputate the missing data" 106 | ], 107 | "metadata": { 108 | "id": "iBDFQ3eyjSI-" 109 | } 110 | }, 111 | { 112 | "cell_type": "code", 113 | "source": [ 114 | "" 115 | ], 116 | "metadata": { 117 | "id": "ykxp6FIMjwRy" 118 | }, 119 | "execution_count": null, 120 | "outputs": [] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "source": [ 125 | "##Task 3:\t\n", 126 | "List all the unique \"Equity Style\"? \n", 127 | "What are the two most used and least used Equity Style?" 128 | ], 129 | "metadata": { 130 | "id": "wvntPD5OxWb_" 131 | } 132 | }, 133 | { 134 | "cell_type": "code", 135 | "source": [ 136 | "" 137 | ], 138 | "metadata": { 139 | "id": "-vS6KNxuyVH_" 140 | }, 141 | "execution_count": null, 142 | "outputs": [] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "source": [ 147 | "##Task 4:\t\n", 148 | "Mapping your data: Figure out a way to map the values of \"Morningstar Rating\" form [5,4,3,2,1] to ['Awsome','Good','Average','bad','Awful']" 149 | ], 150 | "metadata": { 151 | "id": "R7HKDkbJhJGn" 152 | } 153 | }, 154 | { 155 | "cell_type": "code", 156 | "source": [ 157 | "" 158 | ], 159 | "metadata": { 160 | "id": "qqTuTdAnjrQ0" 161 | }, 162 | "execution_count": null, 163 | "outputs": [] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "source": [ 168 | "##Task 5:\t\n", 169 | "Data Aggregation using GroupBy.\n", 170 | "\tUsing the datafile \"Kiwisaver_sample_wk9.csv\", group the Funds based on Branding Name and Equity Style.\n", 171 | "\tReport the average Holdings and Net Assets Size of the groups that you just created. " 172 | ], 173 | "metadata": { 174 | "id": "Y_b-s1ZiImia" 175 | } 176 | }, 177 | { 178 | "cell_type": "code", 179 | "source": [ 180 | "" 181 | ], 182 | "metadata": { 183 | "id": "HDFYVa92Il10" 184 | }, 185 | "execution_count": null, 186 | "outputs": [] 187 | }, 188 | { 189 | "cell_type": "markdown", 190 | "source": [ 191 | "##Task 6:\t\n", 192 | "Data Aggregation using pivot tables.\t\n", 193 | "\tSimilar to the previous task, you will be using the file \"Kiwisaver_sample_wk9.csv\". \n", 194 | "\tCreate a pivot table of average Holdings and Net Assets Size of Funds grouped by Branding Name and Equity Style (i.e. do the exact same thing as Task 1, but using a pivot table).\n" 195 | ], 196 | "metadata": { 197 | "id": "KNbcF7qQIqc2" 198 | } 199 | }, 200 | { 201 | "cell_type": "code", 202 | "source": [ 203 | "" 204 | ], 205 | "metadata": { 206 | "id": "k6rmNOrNIt07" 207 | }, 208 | "execution_count": null, 209 | "outputs": [] 210 | }, 211 | { 212 | "cell_type": "markdown", 213 | "source": [ 214 | "##Task 7: \n", 215 | "Still using the file \"Kiwisaver_sample_wk9.csv\", clean the dataframe if information related to Morningstar Rating and Manager Name is not available. \n", 216 | "\n", 217 | "Using either GroupBy or a pivot table, report the average Morningstar Rating for Funds managed by each of the managers in the file." 218 | ], 219 | "metadata": { 220 | "id": "OolBfm2pIuba" 221 | } 222 | }, 223 | { 224 | "cell_type": "code", 225 | "source": [ 226 | "" 227 | ], 228 | "metadata": { 229 | "id": "-EGJMPUCIxZe" 230 | }, 231 | "execution_count": null, 232 | "outputs": [] 233 | } 234 | ] 235 | } -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/Assignments/Assignment_02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Assignment_02.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "markdown", 20 | "source": [ 21 | "# Baby Births Data Analysis" 22 | ], 23 | "metadata": { 24 | "id": "JBiij_b1_BEL" 25 | } 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "source": [ 30 | "This assessment uses data from the United States Social Security Administration (SSA) which contains names of babies born between 1991 and 2010\n", 31 | "\n", 32 | "In particular, we are provided with yearly files, from “yob1991.csv’ all the way to “yob2010.csv”. Each of these files contains information such as name, sex, total number and the year of births.\n", 33 | "\n", 34 | "The objectives of this assessment are as follows:\n", 35 | "1. Data handling by combining multiple datasets\n", 36 | "2. Visualize the total male and female babies over time\n", 37 | "3. Tabulate the most and least popular baby names\n", 38 | "4. Analyze trends in names." 39 | ], 40 | "metadata": { 41 | "id": "F5OBeDD2-vN8" 42 | } 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "source": [ 47 | "## Task 1:\n", 48 | "Write a loop to concatenate (vertically) each of these files to form one big file. Call this big file as ‘full_data’. Using the data, compute the following statistics and report them as Table 1: (a) total number of observations, (b) total unique names, (c) total births, and (d) average births per unique name.\n", 49 | "\n" 50 | ], 51 | "metadata": { 52 | "id": "C-kZjGgC_PPr" 53 | } 54 | }, 55 | { 56 | "cell_type": "code", 57 | "source": [ 58 | "" 59 | ], 60 | "metadata": { 61 | "id": "PAz50i15_Rcl" 62 | }, 63 | "execution_count": null, 64 | "outputs": [] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "source": [ 69 | "## Task 2:\n", 70 | "Create a pivot table of total births by sex and year and then plot them as Figure 1." 71 | ], 72 | "metadata": { 73 | "id": "t6su9Gtg_STj" 74 | } 75 | }, 76 | { 77 | "cell_type": "code", 78 | "source": [ 79 | "" 80 | ], 81 | "metadata": { 82 | "id": "mAkAre5I_Rmw" 83 | }, 84 | "execution_count": null, 85 | "outputs": [] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "source": [ 90 | "## Task 3:\n", 91 | "Calculate the total births over the sample period by grouping the data by name and sex. Subset the group into male and female. Using these subsets, select the top and bottom 3 male and female names. In total, you should have 12 names in total. Report them as Table 2." 92 | ], 93 | "metadata": { 94 | "id": "IBEmjae0_Wp0" 95 | } 96 | }, 97 | { 98 | "cell_type": "code", 99 | "source": [ 100 | "" 101 | ], 102 | "metadata": { 103 | "id": "VnGOP60m_ZlD" 104 | }, 105 | "execution_count": null, 106 | "outputs": [] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "source": [ 111 | "## Task 4:\n", 112 | "Using the top male and female names (two names in total), check their trends over time, i.e. plot the total births with these names from 1991 to 2010 as Figure 2. In order to do this, you would first need to create a pivot table" 113 | ], 114 | "metadata": { 115 | "id": "Y3GSYKew_Z8H" 116 | } 117 | }, 118 | { 119 | "cell_type": "code", 120 | "source": [ 121 | "" 122 | ], 123 | "metadata": { 124 | "id": "2M0Bf0dz_jT-" 125 | }, 126 | "execution_count": null, 127 | "outputs": [] 128 | } 129 | ] 130 | } -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/Assignments/Assignment_03.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Assignment_03.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "markdown", 20 | "source": [ 21 | "# Visulizing SuperMarket Data" 22 | ], 23 | "metadata": { 24 | "id": "QRnmlGt5ugO-" 25 | } 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "source": [ 30 | "Instructions: \n", 31 | "* Use proper legends, labels, and title for every plot\n", 32 | "* Try to use different stylings available to make your plots look as esthetically pleasing as possible" 33 | ], 34 | "metadata": { 35 | "id": "V-wRPBA2swjS" 36 | } 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "source": [ 41 | "### Setup" 42 | ], 43 | "metadata": { 44 | "id": "dBrqO6yFwuWf" 45 | } 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 1, 50 | "metadata": { 51 | "colab": { 52 | "base_uri": "https://localhost:8080/" 53 | }, 54 | "id": "oLrxCM2hteBU", 55 | "outputId": "0ec96e38-80c7-4f08-ebf6-71f286c99092" 56 | }, 57 | "outputs": [ 58 | { 59 | "output_type": "stream", 60 | "name": "stdout", 61 | "text": [ 62 | "Mounted at /content/drive\n" 63 | ] 64 | } 65 | ], 66 | "source": [ 67 | "from google.colab import drive\n", 68 | "drive.mount('/content/drive')" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "source": [ 74 | "cd /content/drive/MyDrive/DataAnalysis-with-Python" 75 | ], 76 | "metadata": { 77 | "colab": { 78 | "base_uri": "https://localhost:8080/" 79 | }, 80 | "id": "MB2djoYKwzLd", 81 | "outputId": "d2f9444a-abd1-412c-eb6a-89ed15db2511" 82 | }, 83 | "execution_count": 2, 84 | "outputs": [ 85 | { 86 | "output_type": "stream", 87 | "name": "stdout", 88 | "text": [ 89 | "/content/drive/MyDrive/DataAnalysis-with-Python\n" 90 | ] 91 | } 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "source": [ 97 | "import pandas as pd\n", 98 | "import seaborn as sns\n", 99 | "import matplotlib.pyplot as plt\n", 100 | "# %maplotlib inline" 101 | ], 102 | "metadata": { 103 | "id": "MEgibQ_9w6LE" 104 | }, 105 | "execution_count": 3, 106 | "outputs": [] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "source": [ 111 | "data = pd.read_excel('SuperstoreData.xls')" 112 | ], 113 | "metadata": { 114 | "id": "QTrMia-QxAdE" 115 | }, 116 | "execution_count": 7, 117 | "outputs": [] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "source": [ 122 | "## Task 01\n", 123 | "Choose an appropriate graph to display the change in sales of each category throughout the year. Display each sale inside a separate graph;\n", 124 | "*Hint: Make use of subplots" 125 | ], 126 | "metadata": { 127 | "id": "vpYWLWQV4Lw5" 128 | } 129 | }, 130 | { 131 | "cell_type": "code", 132 | "source": [ 133 | "" 134 | ], 135 | "metadata": { 136 | "id": "t2szj0k04oXG" 137 | }, 138 | "execution_count": null, 139 | "outputs": [] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "source": [ 144 | "# Task 02\n", 145 | "What is the total number of sales of each sub-category inside each category\n", 146 | "*Hint: Use Subplots" 147 | ], 148 | "metadata": { 149 | "id": "iPvzX3TPsUBi" 150 | } 151 | }, 152 | { 153 | "cell_type": "code", 154 | "source": [ 155 | "" 156 | ], 157 | "metadata": { 158 | "id": "Eh3yD0pYstqA" 159 | }, 160 | "execution_count": null, 161 | "outputs": [] 162 | }, 163 | { 164 | "cell_type": "markdown", 165 | "source": [ 166 | "## Task 03\n", 167 | "Figure out a way to present your data to stakeholders in such a way they could;\n", 168 | "1. see the sales change in each country through out the year\n", 169 | "2. could differentiate between each category of the sales each country made" 170 | ], 171 | "metadata": { 172 | "id": "CK83eyrPtSrr" 173 | } 174 | }, 175 | { 176 | "cell_type": "code", 177 | "source": [ 178 | "" 179 | ], 180 | "metadata": { 181 | "id": "zZR6QA_Pt-Ck" 182 | }, 183 | "execution_count": null, 184 | "outputs": [] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "source": [ 189 | "## Task 04\n", 190 | "Play with the data, make some intresting plots and draw some conclusion" 191 | ], 192 | "metadata": { 193 | "id": "47R3l2wBt-SV" 194 | } 195 | }, 196 | { 197 | "cell_type": "code", 198 | "source": [ 199 | "" 200 | ], 201 | "metadata": { 202 | "id": "_O-5zmdbt-Hr" 203 | }, 204 | "execution_count": null, 205 | "outputs": [] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "source": [ 210 | "## Task 05\n", 211 | "If you used seaborn to visualize the contents of Task01, achieve the same result using matplotlib or vice versa" 212 | ], 213 | "metadata": { 214 | "id": "y-IogWksh7VS" 215 | } 216 | }, 217 | { 218 | "cell_type": "code", 219 | "source": [ 220 | "" 221 | ], 222 | "metadata": { 223 | "id": "FOKFsCxRiGC-" 224 | }, 225 | "execution_count": null, 226 | "outputs": [] 227 | } 228 | ] 229 | } -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/Assignments/csvFiles.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mustafa-Hassan2001/DataAnalysis-using-Python-master/193500bd81b03fbd14252fd213e881d4f1ebb5e7/DataAnalysis-using-Python-master/Assignments/csvFiles.zip -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/Assignments/kiwisaver_sample_wk9.csv: -------------------------------------------------------------------------------- 1 | Name,Firm Name,Branding Name,Holdings,Global Category,Morningstar Rating,Domicile,Fund Legal Structure,Equity Style,Inception Date,Manager Name,Manager Tenure,Net Assets Date,Net Assets Size,Net Assets Share Class 2 | AMP IL no E Fee NZ Share (RN),AMP Group,AMP,59,New Zealand Equity,3,New Zealand,Open Ended Investment Company,Mid Growth,3/02/1993,,,,135202.5,81121.5 3 | AMP KiwiSaver AMP Income Generator,AMP Group,AMP,689,Moderate Allocation,,New Zealand,Open Ended Investment Company,Mid Blend,28/07/2016,,,28/02/2017,861.923,517.154 4 | AMP KiwiSaver ANZ Balanced Plus,AMP Group,AMP,1544,Aggressive Allocation,3,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,28/02/2017,146665.955,87999.573 5 | AMP Prem PSS OnePath NZ Shares,AMP Group,AMP,125,Australia & New Zealand Equity,3,New Zealand,Open Ended Investment Company,Mid Growth,9/08/2004,,,28/02/2017,3176.42,1905.852 6 | ANZ Default KiwiSaver Scheme Cnsrv(Dflt),ANZ New Zealand Investments Limited,ANZ,1024,Cautious Allocation,4,New Zealand,Open Ended Investment Company,Mid Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,705818.111,423490.867 7 | ANZ Default KiwiSaver Scheme-Balanced,ANZ New Zealand Investments Limited,ANZ,1543,Allocation,3,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,60180.495,36108.297 8 | ANZ Default KiwiSaver Scheme-Balanced Gr,ANZ New Zealand Investments Limited,ANZ,1543,Aggressive Allocation,3,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,65818.65,39491.19 9 | ANZ Default KiwiSaver Scheme-Cnsrv Bal,ANZ New Zealand Investments Limited,ANZ,1543,Moderate Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,20822.57,12493.542 10 | ANZ Default KiwiSaver Scheme-Growth,ANZ New Zealand Investments Limited,ANZ,1543,Aggressive Allocation,5,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,54510.663,32706.398 11 | ANZ KiwiSaver-Balanced,ANZ New Zealand Investments Limited,ANZ,1543,Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,1115543.339,669326.003 12 | ANZ KiwiSaver-Balanced Growth,ANZ New Zealand Investments Limited,ANZ,1544,Aggressive Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,927431.575,556458.945 13 | ANZ KiwiSaver-Conservative,ANZ New Zealand Investments Limited,ANZ,1543,Cautious Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,433189.839,259913.903 14 | ANZ KiwiSaver-Conservative Balanced,ANZ New Zealand Investments Limited,ANZ,1543,Moderate Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,525664.141,315398.485 15 | ANZ KiwiSaver-Growth,ANZ New Zealand Investments Limited,ANZ,1543,Aggressive Allocation,5,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Stuart Millar,9.42,15/03/2017,1469339.561,881603.737 16 | Aon KiwiSaver ANZ Balanced,Aon New Zealand Ltd,Aon,265,Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,28/02/2017,19116.131,11469.679 17 | Aon KiwiSaver Milford,Aon New Zealand Ltd,Aon,384,Australia & New Zealand Equity,4,New Zealand,Open Ended Investment Company,Mid Growth,1/05/2009,,,28/02/2017,82200.346,49320.208 18 | ASB KiwiSaver Scheme's Balanced,ASB Group Investments Limited,ASB,1320,Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,John Smith,9.42,28/02/2017,683803.635,410282.181 19 | ASB KiwiSaver Scheme's Cnsrv (Default),ASB Group Investments Limited,ASB,1582,Cautious Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,John Smith,9.42,28/02/2017,2468879.526,1481327.716 20 | ASB KiwiSaver Scheme's Growth,ASB Group Investments Limited,ASB,841,Aggressive Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,John Smith,9.42,28/02/2017,743789.17,446273.502 21 | ASB KiwiSaver Scheme's Moderate,ASB Group Investments Limited,ASB,1535,Moderate Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,John Smith,9.42,28/02/2017,870727.423,522436.454 22 | BNZ KiwiSaver Balanced Fund,BNZ Investment Services Limited,Bank of New Zealand,2457,Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,11/02/2013,,,14/03/2017,135202.5,81121.5 23 | BNZ KiwiSaver Conservative (Default),BNZ Investment Services Limited,Bank of New Zealand,2458,Cautious Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,11/02/2013,,,14/03/2017,257482.696,154489.618 24 | BNZ KiwiSaver First Home Buyer Fund,BNZ Investment Services Limited,Bank of New Zealand,2456,Cautious Allocation,,New Zealand,Open Ended Investment Company,Large Blend,17/09/2015,,,14/03/2017,27564.969,16538.981 25 | BNZ KiwiSaver Growth Fund,BNZ Investment Services Limited,Bank of New Zealand,2456,Aggressive Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,11/02/2013,,,14/03/2017,113450.351,68070.211 26 | BNZ KiwiSaver Moderate Fund,BNZ Investment Services Limited,Bank of New Zealand,2457,Moderate Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,11/02/2013,,,14/03/2017,179096.412,107457.847 27 | Booster KiwiSaver Balanced,Booster Financial Services Limited,Grosvenor,174,Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,David Beattie;Nic Craven;,9.42,28/02/2017,255432.126,153259.276 28 | Booster KiwiSaver Balanced Growth,Booster Financial Services Limited,Grosvenor,174,Aggressive Allocation,2,New Zealand,Open Ended Investment Company,Large Blend,20/05/2010,David Beattie;Nic Craven;,6.83,28/02/2017,140261.169,84156.701 29 | Booster KiwiSaver Capital Guaranteed,Booster Financial Services Limited,Grosvenor,151,Miscellaneous,,New Zealand,Open Ended Investment Company,Large Blend,1/07/2014,David Beattie,2.67,28/02/2017,32777.503,19666.502 30 | Booster KiwiSaver Default Saver,Booster Financial Services Limited,Grosvenor,159,Cautious Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/07/2014,David Beattie;Nic Craven;,2.67,28/02/2017,23651.194,14190.716 31 | Booster KiwiSaver Geared Growth,Booster Financial Services Limited,Grosvenor,116,Aggressive Allocation,4,New Zealand,Open Ended Investment Company,Large Blend,1/06/2009,David Beattie,7.75,28/02/2017,5242.649,3145.589 32 | Booster KiwiSaver High Growth,Booster Financial Services Limited,Grosvenor,174,Aggressive Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,David Beattie;Nic Craven;,9.42,28/02/2017,118527.836,71116.702 33 | Booster KiwiSaver Moderate,Booster Financial Services Limited,Grosvenor,173,Moderate Allocation,3,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,David Beattie;Nic Craven;,9.42,28/02/2017,92511.222,55506.733 34 | Booster KiwiSaver Socially Rsp Inv Bal,Booster Financial Services Limited,Grosvenor,147,Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/07/2014,David Beattie,2.67,28/02/2017,18747.062,11248.237 35 | Booster KiwiSaver Socially Rsp Inv Gr,Booster Financial Services Limited,Grosvenor,149,Australia & New Zealand Equity,2,New Zealand,Open Ended Investment Company,Large Blend,20/05/2010,David Beattie,6.83,28/02/2017,18269.168,10961.501 36 | Booster KiwiSaver Trans-Tasman Share,Booster Financial Services Limited,Grosvenor,77,Australia & New Zealand Equity,1,New Zealand,Open Ended Investment Company,Large Blend,20/05/2010,David Beattie,6.83,28/02/2017,4180.297,2508.178 37 | FC KiwiSaver Scheme's Active Balanced,ASB Group Investments Limited,ASB,1237,Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,31/12/2014,7094.312,4256.587 38 | FC KiwiSaver Scheme's Active Cnsrv,ASB Group Investments Limited,ASB,1212,Moderate Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,31/12/2014,1794.037,1076.422 39 | FC KiwiSaver Scheme's Active Growth,ASB Group Investments Limited,ASB,1218,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,31/12/2014,8047.739,4828.643 40 | Fidelity KiwiSaver-Aggressive Kiwi Fund,Booster Financial Services Limited,Grosvenor,76,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/10/2014,15280.369,9168.221 41 | Fidelity KiwiSaver-Balanced Kiwi Fund,Booster Financial Services Limited,Grosvenor,75,Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/10/2014,94957.184,56974.31 42 | Fidelity KiwiSaver-Ethical Kiwi,Booster Financial Services Limited,Grosvenor,113,Allocation,,New Zealand,Open Ended Investment Company,Large Growth,14/05/2008,,,31/10/2014,10302.667,6181.6 43 | Fidelity KiwiSaver-Growth Kiwi Fund,Booster Financial Services Limited,Grosvenor,75,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/10/2014,42618.096,25570.858 44 | Fisher Funds Conservative KiwiSaver Fund,Fisher Funds Management Limited,Fisher Funds,222,Cautious Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,2/06/2009,Mark Brighouse,5.33,14/03/2017,350522.668,210313.601 45 | Fisher Funds Growth KiwiSaver Fund,Fisher Funds Management Limited,Fisher Funds,197,Aggressive Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Mark Brighouse,5.33,14/03/2017,864163.389,518498.033 46 | Forsyth Barr KiwiSaver Balanced Port,Forsyth Barr Limited,Forsyth Barr,153,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,26/06/2008,,,29/09/2016,12116.528,7269.917 47 | Forsyth Barr KiwiSaver Growth Portfolio,Forsyth Barr Limited,Forsyth Barr,153,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,26/06/2008,,,29/09/2016,10738.997,6443.398 48 | Generate KiwiSaver Conservative Fund,Generate Investment Management Limited,Generate,108,Moderate Allocation,5,New Zealand,Open Ended Investment Company,Mid Growth,16/04/2013,,,15/03/2017,55609.795,33365.877 49 | Generate KiwiSaver Focused Growth Fund,Generate Investment Management Limited,Generate,30,Aggressive Allocation,4,New Zealand,Open Ended Investment Company,Mid Blend,16/04/2013,,,15/03/2017,117232.058,70339.235 50 | Generate KiwiSaver Growth Fund,Generate Investment Management Limited,Generate,82,Aggressive Allocation,5,New Zealand,Open Ended Investment Company,Mid Blend,16/04/2013,,,15/03/2017,97760.366,58656.22 51 | Mercer KiwiSaver Balanced,Mercer (N.Z.) Limited,Mercer,1538,Allocation,4,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,Phillip Houghton-Brown,4.67,28/02/2017,204935.285,122961.171 52 | Mercer KiwiSaver Conservative (Default),Mercer (N.Z.) Limited,Mercer,1306,Cautious Allocation,5,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,28/02/2017,749774.754,449864.852 53 | Mercer KiwiSaver Growth,Mercer (N.Z.) Limited,Mercer,1602,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,18/02/2015,Phillip Houghton-Brown,2.08,28/02/2017,29364.475,17618.685 54 | Mercer KiwiSaver High Growth,Mercer (N.Z.) Limited,Mercer,1328,Aggressive Allocation,5,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,28/02/2017,82304.615,49382.769 55 | Mercer KiwiSaver Moderate,Mercer (N.Z.) Limited,Mercer,1364,Moderate Allocation,,New Zealand,Open Ended Investment Company,Large Growth,18/02/2015,Phillip Houghton-Brown,2.08,28/02/2017,38324.385,22994.631 56 | Mercer Super Trust KiwiSaver Active Bal,Mercer (N.Z.) Limited,Mercer,1228,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/05/2015,87240.978,52344.587 57 | Mercer Super Trust KiwiSaver Conserv,Mercer (N.Z.) Limited,Mercer,1039,Moderate Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/05/2015,633.303,379.982 58 | Mercer Super Trust KiwiSaver Growth,Mercer (N.Z.) Limited,Mercer,1239,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/05/2015,6859.595,4115.757 59 | Mercer Super Trust KiwiSaver High Growth,Mercer (N.Z.) Limited,Mercer,1131,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,31/05/2015,9139.787,5483.872 60 | Mercer Super Trust KiwiSaver Moderate,Mercer (N.Z.) Limited,Mercer,1213,Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,31/05/2015,2240.726,1344.436 61 | Mercer Super Trust KiwiSaver Shares,Mercer (N.Z.) Limited,Mercer,862,Australia & New Zealand Equity,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,31/05/2015,5335.246,3201.148 62 | Mercer Super Trust KiwiSaver TransTasman,Mercer (N.Z.) Limited,Mercer,74,Australia & New Zealand Equity,,New Zealand,Open Ended Investment Company,Mid Growth,1/10/2007,,,31/01/2015,1881.585,1128.951 63 | Milford KiwiSaver Balanced,Milford Funds Limited,Milford Funds,497,Allocation,5,New Zealand,Open Ended Investment Company,Mid Growth,1/04/2010,Jonathan Windust,6.92,14/03/2017,93936.811,56362.087 64 | Milford KiwiSaver Conservative Fund,Milford Funds Limited,Milford Funds,361,Cautious Allocation,,New Zealand,Open Ended Investment Company,Mid Growth,1/10/2012,David Lewis,1.5,15/03/2017,23663.555,14198.133 65 | NZ Defence Force KiwiSaver Balanced,Mercer (N.Z.) Limited,Mercer,1538,Allocation,,New Zealand,Open Ended Investment Company,Large Growth,9/10/2015,,,28/02/2017,17475.715,10485.429 66 | NZ Defence Force KiwiSaver Conservative,Mercer (N.Z.) Limited,Mercer,1306,Cautious Allocation,,New Zealand,Open Ended Investment Company,Large Growth,9/10/2015,,,28/02/2017,916.695,550.017 67 | NZ Defence Force KiwiSaver Growth,Mercer (N.Z.) Limited,Mercer,1602,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,9/10/2015,,,28/02/2017,4589.997,2753.998 68 | NZ Defence Force KiwiSaver Moderate,Mercer (N.Z.) Limited,Mercer,1364,Moderate Allocation,,New Zealand,Open Ended Investment Company,Large Growth,9/10/2015,,,28/02/2017,1084.035,650.421 69 | NZ Funds KiwiSaver Growth Strategy,New Zealand Funds Management Limited,New Zealand Funds,185,Miscellaneous,,New Zealand,Open Ended Investment Company,Mid Value,21/09/2010,,,28/02/2017,60645.442,36387.265 70 | NZ Funds KiwiSaver Inflation Strategy,New Zealand Funds Management Limited,New Zealand Funds,182,Miscellaneous,,New Zealand,Open Ended Investment Company,Mid Value,21/09/2010,,,28/02/2017,43965.166,26379.1 71 | OneAnswer KiwiSaver-Australasian Share,ANZ New Zealand Investments Limited,ANZ,147,Australia & New Zealand Equity,4,New Zealand,Open Ended Investment Company,Mid Growth,1/10/2007,Mark Brown,9.42,15/03/2017,17059.192,10235.515 72 | SIL NZ Share,ANZ New Zealand Investments Limited,ANZ,145,New Zealand Equity,,New Zealand,Open Ended Investment Company,Mid Growth,1/03/1992,Mark Brown;Craig Brown;,11.25,31/10/2016,15396.723,9238.034 73 | Simplicity KiwiSaver Balanced Fund,Simplicity (NZ) Limited,Simplicity NZ,66,Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/09/2016,,,14/03/2017,15381.98,9229.188 74 | Simplicity KiwiSaver Conservative Fund,Simplicity (NZ) Limited,Simplicity NZ,31,Cautious Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/09/2016,,,14/03/2017,3951.668,2371.001 75 | Simplicity KiwiSaver Growth Fund,Simplicity (NZ) Limited,Simplicity NZ,66,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/09/2016,,,14/03/2017,47602.397,28561.438 76 | Staples Rodway KiwiSaver Balanced,Staples Rodway Superannuation Limited,Staples Rodway,1361,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Growth,1/10/2007,,,29/02/2016,15219.139,9131.483 77 | Staples Rodway KiwiSaver Growth,Staples Rodway Superannuation Limited,Staples Rodway,873,Aggressive Allocation,,New Zealand,Open Ended Investment Company,Large Blend,1/10/2007,,,29/02/2016,7621.845,4573.107 78 | -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/Course Content.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mustafa-Hassan2001/DataAnalysis-using-Python-master/193500bd81b03fbd14252fd213e881d4f1ebb5e7/DataAnalysis-using-Python-master/Course Content.docx -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/AMZN.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2021-01-05,3166.010010,3223.379883,3165.060059,3218.510010,3218.510010,2655500 3 | 2021-01-06,3146.479980,3197.510010,3131.159912,3138.379883,3138.379883,4394800 4 | 2021-01-07,3157.000000,3208.540039,3155.000000,3162.159912,3162.159912,3514500 5 | 2021-01-08,3180.000000,3190.639893,3142.199951,3182.699951,3182.699951,3537700 6 | 2021-01-11,3148.010010,3156.379883,3110.000000,3114.209961,3114.209961,3683400 7 | 2021-01-12,3120.000000,3142.139893,3086.000000,3120.830078,3120.830078,3514600 8 | 2021-01-13,3128.439941,3189.949951,3122.080078,3165.889893,3165.889893,3321200 9 | 2021-01-14,3167.520020,3178.000000,3120.590088,3127.469971,3127.469971,3070900 10 | 2021-01-15,3123.020020,3142.550049,3095.169922,3104.250000,3104.250000,4244000 11 | 2021-01-19,3107.000000,3145.000000,3096.000000,3120.760010,3120.760010,3305100 12 | 2021-01-20,3181.989990,3279.800049,3175.000000,3263.379883,3263.379883,5309800 13 | 2021-01-21,3293.000000,3348.550049,3289.570068,3306.989990,3306.989990,4945100 14 | 2021-01-22,3304.310059,3321.909912,3283.159912,3292.229980,3292.229980,2821900 15 | 2021-01-25,3328.500000,3363.889893,3243.149902,3294.000000,3294.000000,3749800 16 | 2021-01-26,3296.360107,3338.000000,3282.870117,3326.129883,3326.129883,2955200 17 | 2021-01-27,3341.489990,3346.520020,3207.080078,3232.580078,3232.580078,4660200 18 | 2021-01-28,3235.040039,3301.679932,3228.689941,3237.620117,3237.620117,3149200 19 | 2021-01-29,3230.000000,3236.989990,3184.550049,3206.199951,3206.199951,4285600 20 | 2021-02-01,3242.360107,3350.260010,3235.030029,3342.879883,3342.879883,4160200 21 | 2021-02-02,3380.000000,3427.739990,3361.129883,3380.000000,3380.000000,7098600 22 | 2021-02-03,3425.010010,3434.000000,3308.620117,3312.530029,3312.530029,7088800 23 | 2021-02-04,3330.000000,3347.000000,3277.750000,3331.000000,3331.000000,3670700 24 | 2021-02-05,3319.000000,3377.000000,3302.709961,3352.149902,3352.149902,3613600 25 | 2021-02-08,3358.500000,3365.000000,3304.000000,3322.939941,3322.939941,3257400 26 | 2021-02-09,3312.489990,3338.000000,3297.840088,3305.000000,3305.000000,2203500 27 | 2021-02-10,3314.000000,3317.949951,3254.000000,3286.580078,3286.580078,3151600 28 | 2021-02-11,3292.000000,3292.000000,3248.060059,3262.129883,3262.129883,2295800 29 | 2021-02-12,3250.000000,3280.250000,3233.310059,3277.709961,3277.709961,2329300 30 | 2021-02-16,3254.050049,3308.300049,3253.590088,3268.949951,3268.949951,2574700 31 | 2021-02-17,3263.600098,3320.909912,3259.500000,3308.639893,3308.639893,3297500 32 | 2021-02-18,3282.419922,3338.000000,3273.939941,3328.229980,3328.229980,3027400 33 | 2021-02-19,3328.229980,3333.500000,3245.750000,3249.899902,3249.899902,4305200 34 | 2021-02-22,3208.129883,3232.320068,3172.260010,3180.739990,3180.739990,3515700 35 | 2021-02-23,3127.030029,3204.729980,3093.600098,3194.500000,3194.500000,4677200 36 | 2021-02-24,3166.750000,3171.229980,3125.379883,3159.530029,3159.530029,3011300 37 | 2021-02-25,3136.739990,3178.260010,3047.760010,3057.159912,3057.159912,4533800 38 | 2021-02-26,3095.199951,3122.439941,3036.699951,3092.929932,3092.929932,4273500 39 | 2021-03-01,3127.889893,3149.560059,3097.989990,3146.139893,3146.139893,2729100 40 | 2021-03-02,3143.469971,3163.520020,3087.120117,3094.530029,3094.530029,2590000 41 | 2021-03-03,3081.179932,3107.780029,2995.000000,3005.000000,3005.000000,3967200 42 | 2021-03-04,3012.000000,3058.129883,2945.429932,2977.570068,2977.570068,5458700 43 | 2021-03-05,3005.000000,3009.000000,2881.000000,3000.459961,3000.459961,5383400 44 | 2021-03-08,3015.000000,3064.590088,2951.310059,2951.949951,2951.949951,4178500 45 | 2021-03-09,3017.989990,3090.959961,3005.149902,3062.850098,3062.850098,4023500 46 | 2021-03-10,3098.449951,3116.459961,3030.050049,3057.639893,3057.639893,3008700 47 | 2021-03-11,3104.010010,3131.780029,3082.929932,3113.590088,3113.590088,2770800 48 | 2021-03-12,3075.000000,3098.979980,3045.500000,3089.489990,3089.489990,2418500 49 | 2021-03-15,3074.570068,3082.239990,3032.090088,3081.679932,3081.679932,2913600 50 | 2021-03-16,3104.969971,3128.909912,3075.860107,3091.860107,3091.860107,2510100 51 | 2021-03-17,3073.219971,3173.050049,3070.219971,3135.729980,3135.729980,3100900 52 | 2021-03-18,3101.000000,3116.629883,3025.000000,3027.989990,3027.989990,3649600 53 | 2021-03-19,3029.229980,3077.290039,3016.629883,3074.959961,3074.959961,4616400 54 | 2021-03-22,3067.850098,3126.580078,3060.050049,3110.870117,3110.870117,2902200 55 | 2021-03-23,3127.000000,3182.000000,3120.850098,3137.500000,3137.500000,3817300 56 | 2021-03-24,3151.040039,3160.310059,3085.149902,3087.070068,3087.070068,2959000 57 | 2021-03-25,3072.989990,3109.780029,3037.139893,3046.260010,3046.260010,3563500 58 | 2021-03-26,3044.060059,3056.659912,2996.000000,3052.030029,3052.030029,3306700 59 | 2021-03-29,3055.439941,3091.250000,3028.449951,3075.729980,3075.729980,2746000 60 | 2021-03-30,3070.010010,3073.000000,3034.000000,3055.290039,3055.290039,2337600 61 | 2021-03-31,3064.060059,3119.330078,3062.500000,3094.080078,3094.080078,3093900 62 | 2021-04-01,3117.939941,3162.439941,3115.550049,3161.000000,3161.000000,2940300 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/Book1.csv: -------------------------------------------------------------------------------- 1 | city,revenue,branch_id 2 | austin,100,10 3 | denver,83,20 4 | spring,4,30 5 | mexico,200,47 6 | -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/Book2.csv: -------------------------------------------------------------------------------- 1 | city,managers,branch_id 2 | austin,tom,10 3 | denver,jon,20 4 | mexico,charles,47 5 | spring,jassy,31 6 | -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/Bronze.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,1052.0 3 | URS,Soviet Union,584.0 4 | GBR,United Kingdom,505.0 5 | FRA,France,475.0 6 | GER,Germany,454.0 7 | AUS,Australia,413.0 8 | ITA,Italy,374.0 9 | HUN,Hungary,345.0 10 | SWE,Sweden,325.0 11 | NED,Netherlands,320.0 12 | ROU,Romania,282.0 13 | JPN,Japan,270.0 14 | RUS,Russia,240.0 15 | CAN,Canada,227.0 16 | GDR,East Germany,225.0 17 | POL,Poland,223.0 18 | FIN,Finland,209.0 19 | CHN,China,193.0 20 | FRG,West Germany,180.0 21 | BRA,Brazil,174.0 22 | DEN,Denmark,152.0 23 | BEL,Belgium,150.0 24 | NOR,Norway,144.0 25 | SUI,Switzerland,138.0 26 | BUL,Bulgaria,136.0 27 | KOR,"Korea, South",135.0 28 | YUG,Yugoslavia,118.0 29 | CUB,Cuba,109.0 30 | TCH,Czechoslovakia,105.0 31 | ESP,Spain,92.0 32 | EUA,Unified team of Germany,92.0 33 | ARG,Argentina,88.0 34 | UKR,Ukraine,78.0 35 | EUN,Unified team,66.0 36 | NZL,New Zealand,61.0 37 | BLR,Belarus,53.0 38 | GRE,Greece,49.0 39 | MEX,Mexico,48.0 40 | AUT,Austria,44.0 41 | LTU,Lithuania,42.0 42 | JAM,Jamaica,39.0 43 | RSA,South Africa,36.0 44 | PAK,Pakistan,34.0 45 | IND,India,34.0 46 | PRK,"Korea, North",30.0 47 | URU,Uruguay,30.0 48 | NGR,Nigeria,27.0 49 | KEN,Kenya,24.0 50 | TUR,Turkey,22.0 51 | IRI,Iran,22.0 52 | CHI,Chile,21.0 53 | EST,Estonia,19.0 54 | CRO,Croatia,18.0 55 | POR,Portugal,18.0 56 | GHA,Ghana,15.0 57 | KAZ,Kazakhstan,14.0 58 | ETH,Ethiopia,14.0 59 | SRB,Serbia,14.0 60 | TPE,Taiwan,13.0 61 | CZE,Czech Republic,13.0 62 | INA,Indonesia,12.0 63 | TRI,Trinidad and Tobago,11.0 64 | SLO,Slovenia,11.0 65 | GEO,Georgia,11.0 66 | ZZX,Mixed teams,10.0 67 | EGY,Egypt,10.0 68 | MGL,Mongolia,10.0 69 | MAR,Morocco,10.0 70 | THA,Thailand,10.0 71 | RU1,0,9.0 72 | AZE,Azerbaijan,9.0 73 | SVK,Slovakia,8.0 74 | IRL,Ireland,8.0 75 | UZB,Uzbekistan,8.0 76 | ALG,Algeria,8.0 77 | VEN,Venezuela,8.0 78 | COL,Colombia,7.0 79 | PHI,Philippines,7.0 80 | ARM,Armenia,7.0 81 | HAI,Haiti,7.0 82 | BOH,Bohemia,6.0 83 | BAH,Bahamas,5.0 84 | ANZ,Australasia,5.0 85 | ISR,Israel,5.0 86 | PUR,Puerto Rico*,5.0 87 | BWI,British West Indies,5.0 88 | LAT,Latvia,4.0 89 | MAS,Malaysia,3.0 90 | MDA,Moldova,3.0 91 | TUN,Tunisia,3.0 92 | ISL,Iceland,2.0 93 | UGA,Uganda,2.0 94 | LIB,Lebanon,2.0 95 | CRC,Costa Rica,2.0 96 | IOP,Independent Olympic Participants (1992),2.0 97 | KGZ,Kyrgyzstan,2.0 98 | PAN,Panama,2.0 99 | QAT,Qatar,2.0 100 | ZIM,Zimbabwe,1.0 101 | CMR,Cameroon,1.0 102 | DOM,Dominican Republic,1.0 103 | SYR,Syria,1.0 104 | KSA,Saudi Arabia,1.0 105 | TJK,Tajikistan,1.0 106 | ZAM,Zambia,1.0 107 | MOZ,Mozambique,1.0 108 | SUR,Suriname,1.0 109 | AFG,Afghanistan,1.0 110 | BAR,Barbados,1.0 111 | BER,Bermuda*,1.0 112 | DJI,Djibouti,1.0 113 | ERI,Eritrea,1.0 114 | GUY,Guyana,1.0 115 | IRQ,Iraq,1.0 116 | KUW,Kuwait,1.0 117 | MKD,Macedonia,1.0 118 | MRI,Mauritius,1.0 119 | NIG,Niger,1.0 120 | TOG,Togo,1.0 121 | PAR,Paraguay, 122 | PER,Peru, 123 | SCG,Serbia, 124 | NAM,Namibia, 125 | SIN,Singapore, 126 | HKG,Hong Kong*, 127 | SRI,Sri Lanka, 128 | TAN,Tanzania, 129 | VIE,Vietnam, 130 | ECU,Ecuador, 131 | LUX,Luxembourg, 132 | AHO,Netherlands Antilles*, 133 | CIV,Cote d'Ivoire, 134 | ISV,Virgin Islands*, 135 | SEN,Senegal, 136 | SUD,Sudan, 137 | TGA,Tonga, 138 | BDI,Burundi, 139 | UAE,United Arab Emirates, 140 | -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/FB.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2021-01-05,268.290009,272.399994,268.209991,270.970001,270.970001,9871600 3 | 2021-01-06,262.000000,267.750000,260.010010,263.309998,263.309998,24354100 4 | 2021-01-07,265.899994,271.609985,264.779999,268.739990,268.739990,15789800 5 | 2021-01-08,268.309998,268.950012,263.179993,267.570007,267.570007,18528300 6 | 2021-01-11,260.480011,263.470001,255.899994,256.839996,256.839996,30412300 7 | 2021-01-12,256.630005,259.720001,250.300003,251.089996,251.089996,26449900 8 | 2021-01-13,251.550003,253.949997,249.199997,251.639999,251.639999,19528900 9 | 2021-01-14,253.399994,255.029999,244.610001,245.639999,245.639999,29739400 10 | 2021-01-15,247.899994,253.860001,247.160004,251.360001,251.360001,24942900 11 | 2021-01-19,256.899994,262.200012,252.720001,261.100006,261.100006,28028500 12 | 2021-01-20,268.929993,270.320007,263.600006,267.480011,267.480011,25199900 13 | 2021-01-21,269.260010,273.600006,267.489990,272.869995,272.869995,20838700 14 | 2021-01-22,272.010010,278.470001,272.000000,274.500000,274.500000,21954000 15 | 2021-01-25,278.140015,280.100006,271.510010,278.010010,278.010010,19087000 16 | 2021-01-26,278.140015,285.390015,277.809998,282.049988,282.049988,19373600 17 | 2021-01-27,282.529999,283.450012,268.119995,272.140015,272.140015,35346200 18 | 2021-01-28,277.179993,286.790009,264.700012,265.000000,265.000000,37758800 19 | 2021-01-29,265.299988,266.559998,254.850006,258.329987,258.329987,30389500 20 | 2021-02-01,259.519989,264.170013,254.910004,262.010010,262.010010,22914300 21 | 2021-02-02,264.000000,268.850006,263.269989,267.079987,267.079987,17320800 22 | 2021-02-03,265.619995,269.200012,263.839996,266.649994,266.649994,14223400 23 | 2021-02-04,267.010010,268.160004,264.000000,266.489990,266.489990,16060000 24 | 2021-02-05,266.799988,269.170013,265.670013,268.100006,268.100006,12441700 25 | 2021-02-08,268.750000,269.859985,265.000000,266.579987,266.579987,13755200 26 | 2021-02-09,266.440002,273.390015,265.750000,269.450012,269.450012,14613400 27 | 2021-02-10,272.450012,273.799988,267.720001,271.869995,271.869995,14687200 28 | 2021-02-11,271.890015,273.579987,268.489990,270.390015,270.390015,12801300 29 | 2021-02-12,270.519989,271.179993,268.339996,270.500000,270.500000,9069100 30 | 2021-02-16,270.799988,276.600006,270.049988,273.970001,273.970001,15417200 31 | 2021-02-17,271.239990,273.970001,269.579987,273.570007,273.570007,12763200 32 | 2021-02-18,269.570007,271.950012,266.029999,269.390015,269.390015,15249100 33 | 2021-02-19,269.859985,270.269989,260.149994,261.559998,261.559998,25622600 34 | 2021-02-22,257.950012,263.070007,257.529999,260.329987,260.329987,17434900 35 | 2021-02-23,259.500000,267.459991,254.960007,265.859985,265.859985,21924600 36 | 2021-02-24,262.329987,266.380005,258.390015,264.309998,264.309998,15736100 37 | 2021-02-25,262.299988,266.940002,254.039993,254.690002,254.690002,19053100 38 | 2021-02-26,256.470001,265.549988,256.470001,257.619995,257.619995,26593900 39 | 2021-03-01,260.820007,266.649994,257.899994,264.910004,264.910004,22117000 40 | 2021-03-02,265.779999,266.709991,258.649994,259.000000,259.000000,15151900 41 | 2021-03-03,260.290009,260.989990,255.100006,255.410004,255.410004,15476700 42 | 2021-03-04,256.470001,266.489990,253.500000,257.640015,257.640015,31570100 43 | 2021-03-05,260.670013,265.450012,255.610001,264.279999,264.279999,26808100 44 | 2021-03-08,265.549988,265.579987,255.050003,255.309998,255.309998,18372700 45 | 2021-03-09,261.179993,268.529999,259.809998,265.739990,265.739990,22018400 46 | 2021-03-10,268.640015,268.970001,262.869995,264.899994,264.899994,14197500 47 | 2021-03-11,268.109985,277.899994,267.820007,273.880005,273.880005,21816600 48 | 2021-03-12,269.140015,269.739990,264.019989,268.399994,268.399994,20590000 49 | 2021-03-15,269.079987,275.959991,268.500000,273.750000,273.750000,16844800 50 | 2021-03-16,276.079987,282.140015,274.799988,279.279999,279.279999,22178700 51 | 2021-03-17,275.709991,286.230011,275.410004,284.010010,284.010010,21073900 52 | 2021-03-18,279.869995,285.190002,278.279999,278.619995,278.619995,18728300 53 | 2021-03-19,281.220001,292.799988,281.200012,290.109985,290.109985,38905100 54 | 2021-03-22,290.450012,299.709991,286.750000,293.540009,293.540009,28273100 55 | 2021-03-23,293.149994,298.000000,289.809998,290.630005,290.630005,23000900 56 | 2021-03-24,291.000000,291.720001,281.160004,282.140015,282.140015,18675000 57 | 2021-03-25,280.980011,288.059998,277.750000,278.739990,278.739990,24505600 58 | 2021-03-26,278.299988,284.500000,277.769989,283.019989,283.019989,17620600 59 | 2021-03-29,285.769989,293.179993,284.700012,290.820007,290.820007,21718800 60 | 2021-03-30,289.829987,292.470001,286.700012,288.000000,288.000000,17474500 61 | 2021-03-31,289.989990,296.500000,288.609985,294.529999,294.529999,19498200 62 | 2021-04-01,298.399994,302.399994,296.600006,298.660004,298.660004,17616000 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/GOOG.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2021-01-05,1725.000000,1747.670044,1718.015015,1740.920044,1740.920044,1145300 3 | 2021-01-06,1702.630005,1748.000000,1699.000000,1735.290039,1735.290039,2602100 4 | 2021-01-07,1740.060059,1788.400024,1737.050049,1787.250000,1787.250000,2265000 5 | 2021-01-08,1787.979980,1809.839966,1773.535034,1807.209961,1807.209961,2050600 6 | 2021-01-11,1786.069946,1794.314941,1760.520020,1766.719971,1766.719971,1209700 7 | 2021-01-12,1753.920044,1778.040039,1725.310059,1746.550049,1746.550049,1357700 8 | 2021-01-13,1738.579956,1765.035034,1738.010010,1754.400024,1754.400024,1094100 9 | 2021-01-14,1753.619995,1775.010010,1733.400024,1740.180054,1740.180054,1179500 10 | 2021-01-15,1738.189941,1756.000000,1721.550049,1736.189941,1736.189941,1342200 11 | 2021-01-19,1752.250000,1809.275024,1741.459961,1790.859985,1790.859985,1734600 12 | 2021-01-20,1831.459961,1903.709961,1825.530029,1886.900024,1886.900024,2490300 13 | 2021-01-21,1898.000000,1934.859985,1887.109985,1891.250000,1891.250000,2063900 14 | 2021-01-22,1895.680054,1910.949951,1881.760010,1901.050049,1901.050049,1272100 15 | 2021-01-25,1920.670044,1929.579956,1867.530029,1899.400024,1899.400024,1927300 16 | 2021-01-26,1888.839966,1925.000000,1884.244995,1917.239990,1917.239990,1313100 17 | 2021-01-27,1882.530029,1890.000000,1809.000000,1830.790039,1830.790039,2748300 18 | 2021-01-28,1843.939941,1898.829956,1842.640015,1863.109985,1863.109985,1773100 19 | 2021-01-29,1846.170044,1857.280029,1810.199951,1835.739990,1835.739990,1612600 20 | 2021-02-01,1853.569946,1922.391968,1850.930054,1901.349976,1901.349976,1602200 21 | 2021-02-02,1922.560059,1955.760010,1914.489990,1927.510010,1927.510010,2273700 22 | 2021-02-03,2073.000000,2116.500000,2018.380005,2070.070068,2070.070068,4118200 23 | 2021-02-04,2068.889893,2078.550049,2042.589966,2062.370117,2062.370117,1852300 24 | 2021-02-05,2070.000000,2102.510010,2059.330078,2098.000000,2098.000000,1533900 25 | 2021-02-08,2105.909912,2123.547119,2072.000000,2092.909912,2092.909912,1241900 26 | 2021-02-09,2078.540039,2105.129883,2078.540039,2083.510010,2083.510010,889900 27 | 2021-02-10,2094.209961,2108.370117,2063.090088,2095.379883,2095.379883,1135500 28 | 2021-02-11,2099.510010,2102.030029,2077.320068,2095.889893,2095.889893,945200 29 | 2021-02-12,2090.250000,2108.820068,2083.129883,2104.110107,2104.110107,855700 30 | 2021-02-16,2104.360107,2152.679932,2104.360107,2121.899902,2121.899902,1133800 31 | 2021-02-17,2100.000000,2133.659912,2098.919922,2128.310059,2128.310059,1070900 32 | 2021-02-18,2110.389893,2132.735107,2103.709961,2117.199951,2117.199951,1121600 33 | 2021-02-19,2119.270020,2130.530029,2097.409912,2101.139893,2101.139893,1457400 34 | 2021-02-22,2067.000000,2091.419922,2062.139893,2064.879883,2064.879883,1367500 35 | 2021-02-23,2025.010010,2082.010010,2002.020020,2070.860107,2070.860107,1667400 36 | 2021-02-24,2041.829956,2100.780029,2038.130005,2095.169922,2095.169922,1248300 37 | 2021-02-25,2067.449951,2094.879883,2021.290039,2031.359985,2031.359985,1828400 38 | 2021-02-26,2050.520020,2071.010010,2016.060059,2036.859985,2036.859985,2083500 39 | 2021-03-01,2056.520020,2086.520020,2046.099976,2081.510010,2081.510010,1404500 40 | 2021-03-02,2076.189941,2104.370117,2071.260010,2075.840088,2075.840088,1133600 41 | 2021-03-03,2067.209961,2088.518066,2010.000000,2026.709961,2026.709961,1483100 42 | 2021-03-04,2023.369995,2089.239990,2020.270020,2049.090088,2049.090088,2116100 43 | 2021-03-05,2073.120117,2118.110107,2046.415039,2108.540039,2108.540039,2193800 44 | 2021-03-08,2101.129883,2128.810059,2021.609985,2024.170044,2024.170044,1646000 45 | 2021-03-09,2070.000000,2078.040039,2047.829956,2052.699951,2052.699951,1696400 46 | 2021-03-10,2071.760010,2075.000000,2033.369995,2055.030029,2055.030029,1267800 47 | 2021-03-11,2074.060059,2125.699951,2072.379883,2114.770020,2114.770020,1238300 48 | 2021-03-12,2085.000000,2090.260010,2047.550049,2061.919922,2061.919922,1725000 49 | 2021-03-15,2062.300049,2067.060059,2043.510010,2066.489990,2066.489990,1298100 50 | 2021-03-16,2078.989990,2123.560059,2070.000000,2092.520020,2092.520020,1490600 51 | 2021-03-17,2076.030029,2109.780029,2054.000000,2091.080078,2091.080078,1297500 52 | 2021-03-18,2061.000000,2075.500000,2033.550049,2036.219971,2036.219971,1361400 53 | 2021-03-19,2042.050049,2053.100098,2017.680054,2043.199951,2043.199951,2314400 54 | 2021-03-22,2041.839966,2057.989990,2026.069946,2038.589966,2038.589966,1954800 55 | 2021-03-23,2051.699951,2072.302002,2039.219971,2052.959961,2052.959961,1367000 56 | 2021-03-24,2065.370117,2078.209961,2041.555054,2045.060059,2045.060059,1154000 57 | 2021-03-25,2044.810059,2058.870117,2010.729980,2044.359985,2044.359985,1418900 58 | 2021-03-26,2038.859985,2050.989990,2014.020020,2035.550049,2035.550049,1493500 59 | 2021-03-29,2027.880005,2058.429932,2015.619995,2055.949951,2055.949951,1229800 60 | 2021-03-30,2057.629883,2070.780029,2044.030029,2055.540039,2055.540039,1036600 61 | 2021-03-31,2059.120117,2093.326904,2056.745117,2068.629883,2068.629883,1459900 62 | 2021-04-01,2097.949951,2142.939941,2096.889893,2137.750000,2137.750000,1699000 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/Gold.csv: -------------------------------------------------------------------------------- 1 | NOC,Country,Total 2 | USA,United States,2088.0 3 | URS,Soviet Union,838.0 4 | GBR,United Kingdom,498.0 5 | FRA,France,378.0 6 | GER,Germany,407.0 7 | AUS,Australia,293.0 8 | ITA,Italy,460.0 9 | HUN,Hungary,400.0 10 | SWE,Sweden,347.0 11 | NED,Netherlands,212.0 12 | ROU,Romania,155.0 13 | JPN,Japan,206.0 14 | RUS,Russia,192.0 15 | CAN,Canada,154.0 16 | GDR,East Germany,329.0 17 | POL,Poland,103.0 18 | FIN,Finland,124.0 19 | CHN,China,234.0 20 | FRG,West Germany,143.0 21 | BRA,Brazil,59.0 22 | DEN,Denmark,147.0 23 | BEL,Belgium,91.0 24 | NOR,Norway,194.0 25 | SUI,Switzerland,73.0 26 | BUL,Bulgaria,53.0 27 | KOR,"Korea, South",140.0 28 | YUG,Yugoslavia,143.0 29 | CUB,Cuba,160.0 30 | TCH,Czechoslovakia,80.0 31 | ESP,Spain,92.0 32 | EUA,Unified team of Germany,68.0 33 | ARG,Argentina,68.0 34 | UKR,Ukraine,32.0 35 | EUN,Unified team,92.0 36 | NZL,New Zealand,76.0 37 | BLR,Belarus,14.0 38 | GRE,Greece,34.0 39 | MEX,Mexico,14.0 40 | AUT,Austria,21.0 41 | LTU,Lithuania,4.0 42 | JAM,Jamaica,23.0 43 | RSA,South Africa,24.0 44 | PAK,Pakistan,42.0 45 | IND,India,128.0 46 | PRK,"Korea, North",10.0 47 | URU,Uruguay,44.0 48 | NGR,Nigeria,19.0 49 | KEN,Kenya,26.0 50 | TUR,Turkey,37.0 51 | IRI,Iran,11.0 52 | CHI,Chile,3.0 53 | EST,Estonia,9.0 54 | CRO,Croatia,31.0 55 | POR,Portugal,4.0 56 | GHA,Ghana, 57 | KAZ,Kazakhstan,9.0 58 | ETH,Ethiopia,18.0 59 | SRB,Serbia, 60 | TPE,Taiwan,2.0 61 | CZE,Czech Republic,10.0 62 | INA,Indonesia,9.0 63 | TRI,Trinidad and Tobago,1.0 64 | SLO,Slovenia,4.0 65 | GEO,Georgia,5.0 66 | ZZX,Mixed teams,23.0 67 | EGY,Egypt,7.0 68 | MGL,Mongolia,2.0 69 | MAR,Morocco,6.0 70 | THA,Thailand,7.0 71 | RU1,0,1.0 72 | AZE,Azerbaijan,4.0 73 | SVK,Slovakia,10.0 74 | IRL,Ireland,8.0 75 | UZB,Uzbekistan,4.0 76 | ALG,Algeria,4.0 77 | VEN,Venezuela,1.0 78 | COL,Colombia,1.0 79 | PHI,Philippines, 80 | ARM,Armenia,1.0 81 | HAI,Haiti, 82 | BOH,Bohemia, 83 | BAH,Bahamas,9.0 84 | ANZ,Australasia,20.0 85 | ISR,Israel,1.0 86 | PUR,Puerto Rico*, 87 | BWI,British West Indies, 88 | LAT,Latvia,2.0 89 | MAS,Malaysia, 90 | MDA,Moldova, 91 | TUN,Tunisia,2.0 92 | ISL,Iceland, 93 | UGA,Uganda,1.0 94 | LIB,Lebanon, 95 | CRC,Costa Rica,1.0 96 | IOP,Independent Olympic Participants (1992), 97 | KGZ,Kyrgyzstan, 98 | PAN,Panama,1.0 99 | QAT,Qatar, 100 | ZIM,Zimbabwe,18.0 101 | CMR,Cameroon,20.0 102 | DOM,Dominican Republic,2.0 103 | SYR,Syria,1.0 104 | KSA,Saudi Arabia, 105 | TJK,Tajikistan, 106 | ZAM,Zambia, 107 | MOZ,Mozambique,1.0 108 | SUR,Suriname,1.0 109 | AFG,Afghanistan, 110 | BAR,Barbados, 111 | BER,Bermuda*, 112 | DJI,Djibouti, 113 | ERI,Eritrea, 114 | GUY,Guyana, 115 | IRQ,Iraq, 116 | KUW,Kuwait, 117 | MKD,Macedonia, 118 | MRI,Mauritius, 119 | NIG,Niger, 120 | TOG,Togo, 121 | PAR,Paraguay, 122 | PER,Peru,1.0 123 | SCG,Serbia, 124 | NAM,Namibia, 125 | SIN,Singapore, 126 | HKG,Hong Kong*,1.0 127 | SRI,Sri Lanka, 128 | TAN,Tanzania, 129 | VIE,Vietnam, 130 | ECU,Ecuador,1.0 131 | LUX,Luxembourg,1.0 132 | AHO,Netherlands Antilles*, 133 | CIV,Cote d'Ivoire, 134 | ISV,Virgin Islands*, 135 | SEN,Senegal, 136 | SUD,Sudan, 137 | TGA,Tonga, 138 | BDI,Burundi,1.0 139 | UAE,United Arab Emirates,1.0 140 | -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/monthly_max_temp.csv: -------------------------------------------------------------------------------- 1 | Month,Max TemperatureF 2 | Jan,68 3 | Feb,60 4 | Mar,68 5 | Apr,84 6 | May,88 7 | Jun,89 8 | Jul,91 9 | Aug,86 10 | Sep,90 11 | Oct,84 12 | Nov,72 13 | Dec,68 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/monthly_mean_temp.csv: -------------------------------------------------------------------------------- 1 | Month,Mean TemperatureF 2 | Apr,53.1 3 | Aug,70.0 4 | Dec,34.935483870967744 5 | Feb,28.714285714285715 6 | Jan,32.354838709677416 7 | Jul,72.87096774193549 8 | Jun,70.13333333333334 9 | Mar,35.0 10 | May,62.612903225806456 11 | Nov,39.8 12 | Oct,55.451612903225815 13 | Sep,63.76666666666666 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/population_00.csv: -------------------------------------------------------------------------------- 1 | 2 | Zip Code ZCTA,2010 Census Population 3 | 57538,322 4 | 59916,130 5 | 37660,40038 6 | 2860,45199 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/population_01.csv: -------------------------------------------------------------------------------- 1 | Zip Code ZCTA,2010 Census Population 2 | 66407,479 3 | 72732,4716 4 | 50579,2405 5 | 46421,30670 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/population_02.csv: -------------------------------------------------------------------------------- 1 | Zip Code ZCTA,2010 Census Population 2 | 12776,2180 3 | 76092,26669 4 | 98360,12221 5 | 49464,27481 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/q1_rainfall_2013.csv: -------------------------------------------------------------------------------- 1 | Month,Precipitation 2 | Jan,0.096129 3 | Feb,0.067143 4 | Mar,0.061613 -------------------------------------------------------------------------------- /DataAnalysis-using-Python-master/data/q1_rainfall_2014.csv: -------------------------------------------------------------------------------- 1 | Month,Precipitation 2 | Jan,0.050323 3 | Feb,0.082143 4 | Mar,0.070968 --------------------------------------------------------------------------------