├── MetS_Report_Template.ipynb └── README.md /MetS_Report_Template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "MetS-Report-Template.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 | "metadata": { 21 | "id": "CFHo_JKH3vpr" 22 | }, 23 | "source": [ 24 | "# **Analysis of Metabolic Syndrome Data**\n", 25 | "\n", 26 | "Firstname Lastname\n", 27 | "\n", 28 | "Student ID xxxxxxx" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": { 34 | "id": "erL0mRWsBkDE" 35 | }, 36 | "source": [ 37 | "# **Instructions**\n", 38 | "\n", 39 | "To get you started, I included the following text and code cells for importing the library and dataset that you will use for analysis.\n", 40 | "\n", 41 | "Please apply your knowledge of Pandas to perform Exploratory Data Analysis where you explore the data, examine the DataFrames, filter DataFrame according to conditions/thresholds calculate summary statistics and create plots.\n", 42 | "\n" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": { 48 | "id": "sDwelBi351VK" 49 | }, 50 | "source": [ 51 | "# **Import libraries**" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "metadata": { 57 | "id": "7LFOqF5153hg" 58 | }, 59 | "source": [ 60 | "import pandas as pd\n", 61 | "\n", 62 | "# This is a comment, feel free to also document code in-text by using comments such as this.\n", 63 | "# Feel free to add additional libraries that you want to use here e.g. numpy" 64 | ], 65 | "execution_count": 2, 66 | "outputs": [] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": { 71 | "id": "KCH0fExG4AYk" 72 | }, 73 | "source": [ 74 | "# **Load dataset**\n", 75 | "\n", 76 | "The ***metabolic syndrome dataset*** (*metabolic_syndrome_synthetic_data.csv*) is retrieved from the **GitHub** source and the content is assigned to the `df` variable.\n", 77 | "\n", 78 | "Dataset URL: https://raw.githubusercontent.com/dataprofessor/data/master/metabolic_syndrome_synthetic_data.csv" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "metadata": { 84 | "colab": { 85 | "base_uri": "https://localhost:8080/", 86 | "height": 417 87 | }, 88 | "id": "XOea8qYt3s4u", 89 | "outputId": "9636565c-a9d2-4eb6-fa9e-313a9a7fd8be" 90 | }, 91 | "source": [ 92 | "df = pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/metabolic_syndrome_synthetic_data.csv')\n", 93 | "df" 94 | ], 95 | "execution_count": 3, 96 | "outputs": [ 97 | { 98 | "output_type": "execute_result", 99 | "data": { 100 | "text/html": [ 101 | "
\n", 119 | " | WC | \n", 120 | "TG | \n", 121 | "HDLC | \n", 122 | "SBP | \n", 123 | "DBP | \n", 124 | "FPG | \n", 125 | "sex | \n", 126 | "
---|---|---|---|---|---|---|---|
0 | \n", 131 | "67.00 | \n", 132 | "61.34 | \n", 133 | "83.21 | \n", 134 | "65.23 | \n", 135 | "60.10 | \n", 136 | "75.73 | \n", 137 | "female | \n", 138 | "
1 | \n", 141 | "61.82 | \n", 142 | "60.99 | \n", 143 | "78.19 | \n", 144 | "97.81 | \n", 145 | "93.37 | \n", 146 | "91.09 | \n", 147 | "female | \n", 148 | "
2 | \n", 151 | "73.40 | \n", 152 | "65.14 | \n", 153 | "73.20 | \n", 154 | "53.27 | \n", 155 | "69.04 | \n", 156 | "70.73 | \n", 157 | "female | \n", 158 | "
3 | \n", 161 | "71.25 | \n", 162 | "146.66 | \n", 163 | "84.05 | \n", 164 | "91.50 | \n", 165 | "56.61 | \n", 166 | "79.78 | \n", 167 | "female | \n", 168 | "
4 | \n", 171 | "112.53 | \n", 172 | "228.97 | \n", 173 | "16.73 | \n", 174 | "5.96 | \n", 175 | "2.24 | \n", 176 | "165.43 | \n", 177 | "male | \n", 178 | "
... | \n", 181 | "... | \n", 182 | "... | \n", 183 | "... | \n", 184 | "... | \n", 185 | "... | \n", 186 | "... | \n", 187 | "... | \n", 188 | "
995 | \n", 191 | "63.35 | \n", 192 | "122.10 | \n", 193 | "80.09 | \n", 194 | "59.57 | \n", 195 | "63.71 | \n", 196 | "72.61 | \n", 197 | "male | \n", 198 | "
996 | \n", 201 | "114.51 | \n", 202 | "391.68 | \n", 203 | "12.82 | \n", 204 | "7.24 | \n", 205 | "26.92 | \n", 206 | "184.00 | \n", 207 | "male | \n", 208 | "
997 | \n", 211 | "114.90 | \n", 212 | "604.84 | \n", 213 | "14.75 | \n", 214 | "42.93 | \n", 215 | "26.26 | \n", 216 | "104.12 | \n", 217 | "female | \n", 218 | "
998 | \n", 221 | "91.51 | \n", 222 | "554.35 | \n", 223 | "19.54 | \n", 224 | "17.50 | \n", 225 | "1.69 | \n", 226 | "183.55 | \n", 227 | "male | \n", 228 | "
999 | \n", 231 | "80.44 | \n", 232 | "85.36 | \n", 233 | "43.52 | \n", 234 | "94.85 | \n", 235 | "49.45 | \n", 236 | "70.12 | \n", 237 | "male | \n", 238 | "
1000 rows × 7 columns
\n", 242 | "