├── 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", 102 | "\n", 115 | "\n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | "
WCTGHDLCSBPDBPFPGsex
067.0061.3483.2165.2360.1075.73female
161.8260.9978.1997.8193.3791.09female
273.4065.1473.2053.2769.0470.73female
371.25146.6684.0591.5056.6179.78female
4112.53228.9716.735.962.24165.43male
........................
99563.35122.1080.0959.5763.7172.61male
996114.51391.6812.827.2426.92184.00male
997114.90604.8414.7542.9326.26104.12female
99891.51554.3519.5417.501.69183.55male
99980.4485.3643.5294.8549.4570.12male
\n", 241 | "

1000 rows × 7 columns

\n", 242 | "
" 243 | ], 244 | "text/plain": [ 245 | " WC TG HDLC SBP DBP FPG sex\n", 246 | "0 67.00 61.34 83.21 65.23 60.10 75.73 female\n", 247 | "1 61.82 60.99 78.19 97.81 93.37 91.09 female\n", 248 | "2 73.40 65.14 73.20 53.27 69.04 70.73 female\n", 249 | "3 71.25 146.66 84.05 91.50 56.61 79.78 female\n", 250 | "4 112.53 228.97 16.73 5.96 2.24 165.43 male\n", 251 | ".. ... ... ... ... ... ... ...\n", 252 | "995 63.35 122.10 80.09 59.57 63.71 72.61 male\n", 253 | "996 114.51 391.68 12.82 7.24 26.92 184.00 male\n", 254 | "997 114.90 604.84 14.75 42.93 26.26 104.12 female\n", 255 | "998 91.51 554.35 19.54 17.50 1.69 183.55 male\n", 256 | "999 80.44 85.36 43.52 94.85 49.45 70.12 male\n", 257 | "\n", 258 | "[1000 rows x 7 columns]" 259 | ] 260 | }, 261 | "metadata": { 262 | "tags": [] 263 | }, 264 | "execution_count": 3 265 | } 266 | ] 267 | }, 268 | { 269 | "cell_type": "markdown", 270 | "metadata": { 271 | "id": "ACMO7wESCjXU" 272 | }, 273 | "source": [ 274 | "---" 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": { 280 | "id": "Q65bWyejCAT1" 281 | }, 282 | "source": [ 283 | "The following are examples that you can use for Text formatting ideas." 284 | ] 285 | }, 286 | { 287 | "cell_type": "markdown", 288 | "metadata": { 289 | "id": "_CSxDN-S_xZe" 290 | }, 291 | "source": [ 292 | "# **1. Section**\n", 293 | "\n", 294 | "Explanation text to describe what this section is doing." 295 | ] 296 | }, 297 | { 298 | "cell_type": "markdown", 299 | "metadata": { 300 | "id": "zprAygrP_6md" 301 | }, 302 | "source": [ 303 | "### **1.1. Sub-section**\n", 304 | "\n", 305 | "Explanation text to describe what this sub-section is doing." 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "metadata": { 311 | "id": "drbVEI8tAD2V" 312 | }, 313 | "source": [ 314 | "Format text by using **bold**, *italic* as well as ***both bold and italic at the same time***.\n", 315 | "\n", 316 | "Function names and variable names can be highlighted by using the tick symbol (`) before and after the text of interest.\n", 317 | "\n", 318 | "For example, `pd.read_csv()`." 319 | ] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "metadata": { 324 | "id": "iri5MuMbAUAY" 325 | }, 326 | "source": [ 327 | "" 328 | ], 329 | "execution_count": null, 330 | "outputs": [] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "metadata": { 335 | "id": "5cXppQFI_wdH" 336 | }, 337 | "source": [ 338 | "" 339 | ], 340 | "execution_count": 3, 341 | "outputs": [] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "metadata": { 346 | "id": "rd-lsi0G_YAC" 347 | }, 348 | "source": [ 349 | "" 350 | ], 351 | "execution_count": null, 352 | "outputs": [] 353 | } 354 | ] 355 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pandas_practice --------------------------------------------------------------------------------