├── .gitignore ├── Exploring Datasets Yourself.ipynb ├── Exploring Datasets.ipynb ├── LICENSE ├── README.md └── affairs.csv /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | 103 | # VS Code 104 | .vscode -------------------------------------------------------------------------------- /Exploring Datasets Yourself.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Exploring Datasets with Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "In this short demo we will analyse a given dataset from 1978, which contains information about politicians having affairs. \n", 15 | "\n", 16 | "To analyse it, we will use a [Jupyter Notebook](http://jupyter.org/), which is basically a *REPL++* for Python. Entering a command with shift executes the line and prints the result." 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": null, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": null, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "To work with common files like CSV, JSON, Excel files etc., we will use [Pandas](http://pandas.pydata.org/), _an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language_™. Let's import it!" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": null, 43 | "metadata": { 44 | "collapsed": true 45 | }, 46 | "outputs": [], 47 | "source": [] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "Our dataset is given as a CSV file. Pandas provides an easy way to read our file with `read_csv`. The path of the file to read is relative to our notebook file. The path can also be an URL, supporting HTTP, FTP and also S3 if your data is stored inside an AWS S3 Bucket!" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": null, 59 | "metadata": { 60 | "collapsed": true 61 | }, 62 | "outputs": [], 63 | "source": [] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "The first thing we will check is the size of our dataset. We can use `info()` to get the number of entries of each column." 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": null, 75 | "metadata": {}, 76 | "outputs": [], 77 | "source": [] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "Now we know how many data is inside our file. Pandas is smart enough to parse the column titles by itself and estimate the data types of each column.\n", 84 | "\n", 85 | "You may be curious how the data looks like. Let's see by using `head()`, which will print the first 5 rows." 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": null, 91 | "metadata": {}, 92 | "outputs": [], 93 | "source": [] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "We can access a column of our dataset by using bracket notation and the name of the column." 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "## Select a column by dtypes\n" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": null, 119 | "metadata": {}, 120 | "outputs": [], 121 | "source": [] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "For categorical features like `sex`, you can also get the distributions of each value by using `value_counts()`." 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "But what about numerical values? It definitly makes no sense to count each distinct value. Therefore, we can use `describe()`." 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "metadata": {}, 161 | "source": [ 162 | "You can also access values like `mean` or `max` directly with the corrsponding methods. Let's see who is the oldest cheater!" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": {}, 169 | "outputs": [], 170 | "source": [] 171 | }, 172 | { 173 | "cell_type": "markdown", 174 | "metadata": {}, 175 | "source": [ 176 | "This works for the whole dataframe as well. Pandas knows which values are numerical based on the datatype and hides the categorical features for you." 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": null, 182 | "metadata": {}, 183 | "outputs": [], 184 | "source": [] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "There is also an easy way to filter your dataset. Let's say we want to have a subset of our data containing only woman. This is also possible with the bracket notation!" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": null, 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": {}, 203 | "source": [ 204 | "The above statement returns a new dataframe (not a copy, modifying this data will modify the original as well), which can be accessed like before. Let's see how the numerical distribution is for our females." 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": null, 210 | "metadata": {}, 211 | "outputs": [], 212 | "source": [] 213 | }, 214 | { 215 | "cell_type": "markdown", 216 | "metadata": {}, 217 | "source": [ 218 | "We can also create new rows. Specify the new column name in brackets and provide a function to set the data. We will create a new column containing True or False, wheather or not the person is below 30." 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": { 224 | "collapsed": true 225 | }, 226 | "source": [ 227 | "## Filtering by large categories" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [] 243 | }, 244 | { 245 | "cell_type": "markdown", 246 | "metadata": {}, 247 | "source": [ 248 | "We can use this to normalize our columns with better values. Take for example `religious`. The number have the following meaning: 1 = not, 2 = mildly, 3 = fairly, 4 = strongly. We can easily replace them inline with the following code." 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "metadata": { 255 | "collapsed": true 256 | }, 257 | "outputs": [], 258 | "source": [ 259 | "rel_meanings = ['not', 'mildly', 'fairly', 'strongly']" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": null, 265 | "metadata": { 266 | "collapsed": true 267 | }, 268 | "outputs": [], 269 | "source": [] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": null, 274 | "metadata": {}, 275 | "outputs": [], 276 | "source": [] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": null, 281 | "metadata": { 282 | "collapsed": true 283 | }, 284 | "outputs": [], 285 | "source": [] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "metadata": {}, 290 | "source": [ 291 | "## Sorting" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "This should be enought about Pandas. Let's get some visualisations!" 299 | ] 300 | }, 301 | { 302 | "cell_type": "markdown", 303 | "metadata": {}, 304 | "source": [ 305 | "## Visualize Data" 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "metadata": {}, 311 | "source": [ 312 | "To visualize our data, we will use [Seaborn](https://seaborn.pydata.org), a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Let's import it." 313 | ] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": null, 318 | "metadata": { 319 | "collapsed": true 320 | }, 321 | "outputs": [], 322 | "source": [] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": {}, 327 | "source": [ 328 | "To see our charts directly in our notebook, we have to execute the following:" 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": null, 334 | "metadata": { 335 | "collapsed": true 336 | }, 337 | "outputs": [], 338 | "source": [ 339 | "%matplotlib inline\n", 340 | "sns.set()\n", 341 | "sns.set_context('talk')" 342 | ] 343 | }, 344 | { 345 | "cell_type": "markdown", 346 | "metadata": {}, 347 | "source": [ 348 | "Seaborn together with Pandas makes it pretty easy to create charts to analyze our data. We can pass our Dataframes and Series directly into Seaborn methods. We will see how in the following sections." 349 | ] 350 | }, 351 | { 352 | "cell_type": "markdown", 353 | "metadata": {}, 354 | "source": [ 355 | "### Univariate Plotting" 356 | ] 357 | }, 358 | { 359 | "cell_type": "markdown", 360 | "metadata": {}, 361 | "source": [ 362 | "Let's start by visualizing the distribution of the age our our people. We can achieve this with a simple method called `distplot` by passing our series of ages as argument." 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": null, 368 | "metadata": {}, 369 | "outputs": [], 370 | "source": [] 371 | }, 372 | { 373 | "cell_type": "markdown", 374 | "metadata": {}, 375 | "source": [ 376 | "The chart above calculates a kernel density as well. To get a real histogram, we have to disable the `kde` feature. We can also increase to number of buckets for our histogram by setting `bins` to 50." 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "Interesting! The ages of the people in this dataset seem to end with two or seven.\n", 391 | "\n", 392 | "We can do the same for every numerical column, e.g. the years of marriage." 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": null, 398 | "metadata": {}, 399 | "outputs": [], 400 | "source": [] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "The average age of our people is around 32, but the most people are married for more than 14 years!" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "### Bivariate Plotting" 414 | ] 415 | }, 416 | { 417 | "cell_type": "markdown", 418 | "metadata": {}, 419 | "source": [ 420 | "Numbers get even more interesting when we can compare them to other numbers! Lets start comparing the number of years married vs the number of affairs. Seaborn provides us with a method called `jointplot` for this use case." 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": null, 426 | "metadata": {}, 427 | "outputs": [], 428 | "source": [] 429 | }, 430 | { 431 | "cell_type": "markdown", 432 | "metadata": {}, 433 | "source": [ 434 | "To get a better feeling of how the number of affairs is affected by the number of years married, we can use a regression model by specifying `kind` as `reg`." 435 | ] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "execution_count": null, 440 | "metadata": {}, 441 | "outputs": [], 442 | "source": [] 443 | }, 444 | { 445 | "cell_type": "markdown", 446 | "metadata": {}, 447 | "source": [ 448 | "We can also use a kernel to kompare the density of two columns against each other, e.g. `age` and `ym`." 449 | ] 450 | }, 451 | { 452 | "cell_type": "code", 453 | "execution_count": null, 454 | "metadata": {}, 455 | "outputs": [], 456 | "source": [] 457 | }, 458 | { 459 | "cell_type": "markdown", 460 | "metadata": {}, 461 | "source": [ 462 | "We can get an even better comparison by plotting everything vs everything! Seaborn provides this with the `pairplot` method." 463 | ] 464 | }, 465 | { 466 | "cell_type": "code", 467 | "execution_count": null, 468 | "metadata": {}, 469 | "outputs": [], 470 | "source": [] 471 | }, 472 | { 473 | "cell_type": "markdown", 474 | "metadata": {}, 475 | "source": [ 476 | "You won't see any special in this data. We need to separate them by some kind of criteria. We can use our categorical values to do this! Seaborn uses a parameter called `hue` to do this. Let's separate our data by `sex` first. To make things even more interesting, let's create a regression for every plot, too!" 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": null, 482 | "metadata": {}, 483 | "outputs": [], 484 | "source": [] 485 | }, 486 | { 487 | "cell_type": "markdown", 488 | "metadata": {}, 489 | "source": [ 490 | "To get even better separation, we can use `lmplot` to compare just the fields we need.\n", 491 | "\n", 492 | "Let's say we're interested in the number of affairs vs years married. We also whant to separate them by `sex`, `child` and `religious`. We will use `sns.lmplot(x=\"ym\", y=\"nbaffairs\", hue=\"sex\", col=\"child\", row=\"religious\", data=affairs)` to achieve this." 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": null, 498 | "metadata": {}, 499 | "outputs": [], 500 | "source": [] 501 | }, 502 | { 503 | "cell_type": "markdown", 504 | "metadata": {}, 505 | "source": [ 506 | "Here are some categorical plots to explore the dataset even further." 507 | ] 508 | }, 509 | { 510 | "cell_type": "code", 511 | "execution_count": null, 512 | "metadata": {}, 513 | "outputs": [], 514 | "source": [] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": null, 519 | "metadata": {}, 520 | "outputs": [], 521 | "source": [] 522 | }, 523 | { 524 | "cell_type": "markdown", 525 | "metadata": {}, 526 | "source": [ 527 | "We can also get the correlations between the values by using Pandas builtin method `corr()`." 528 | ] 529 | }, 530 | { 531 | "cell_type": "code", 532 | "execution_count": null, 533 | "metadata": {}, 534 | "outputs": [], 535 | "source": [] 536 | }, 537 | { 538 | "cell_type": "markdown", 539 | "metadata": {}, 540 | "source": [ 541 | "Feed these stats into Seaborns `heatmap` method will provide us with the visual representation." 542 | ] 543 | }, 544 | { 545 | "cell_type": "code", 546 | "execution_count": null, 547 | "metadata": {}, 548 | "outputs": [], 549 | "source": [] 550 | }, 551 | { 552 | "cell_type": "code", 553 | "execution_count": null, 554 | "metadata": { 555 | "collapsed": true 556 | }, 557 | "outputs": [], 558 | "source": [] 559 | } 560 | ], 561 | "metadata": { 562 | "kernelspec": { 563 | "display_name": "Python 3.10.5 ('stock')", 564 | "language": "python", 565 | "name": "python3" 566 | }, 567 | "language_info": { 568 | "codemirror_mode": { 569 | "name": "ipython", 570 | "version": 3 571 | }, 572 | "file_extension": ".py", 573 | "mimetype": "text/x-python", 574 | "name": "python", 575 | "nbconvert_exporter": "python", 576 | "pygments_lexer": "ipython3", 577 | "version": "3.10.5" 578 | }, 579 | "vscode": { 580 | "interpreter": { 581 | "hash": "4c7353ac5fc7ccda46115f0ffae3090816759362459dfeec8994c7d7d0ddae2f" 582 | } 583 | } 584 | }, 585 | "nbformat": 4, 586 | "nbformat_minor": 2 587 | } 588 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Martin Seeler 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python Data Exploration 2 | 3 | This is an example of how to use Python and Jupyter Notebook to explore a given dataset with [Pandas](http://pandas.pydata.org/) and [Seaborn](https://seaborn.pydata.org/). 4 | 5 | ## Requirements 6 | 7 | To get started, create a virtual environment and install the requirements in it. 8 | 9 | ```bash 10 | $ python3 -m venv venv 11 | $ pip install pandas seaborn jupyterlab 12 | ``` 13 | 14 | After that, you can launch your notebook server by running 15 | 16 | ``` 17 | $ jupyter-lab 18 | ``` 19 | 20 | Of course you can use your own environment as well. Obviously you need Python. I'd recommend installing [Anaconda](anaconda.com), which provides you with all the fancy Python data-science libraries. 21 | 22 | If you have python installed, make sure to install [Jupyter Notebook](http://jupyter.org/), as well as the necessary python packages for Pandas and Seaborn via pip. 23 | 24 | You can see the steps in [Exploring Datasets.ipynb](https://github.com/MartinSeeler/python-data-exploration/blob/master/Exploring%20Datasets.ipynb). Github will render the Jupyter Notebooks so you don't have to download it. If you want to try it yourself, feel free to do it inside [Exploring Datasets Yourself.ipynb](https://github.com/MartinSeeler/python-data-exploration/blob/master/Exploring%20Datasets%20Yourself.ipynb). 25 | 26 | ## Source 27 | 28 | Fair, Ray. 1978. “A Theory of Extramarital Affairs,” Journal of Political Economy, February, 45-61. 29 | 30 | The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm 31 | -------------------------------------------------------------------------------- /affairs.csv: -------------------------------------------------------------------------------- 1 | "sex","age","ym","child","religious","education","occupation","rate","nbaffairs" 2 | "male",37,10,"no",3,18,7,4,0 3 | "female",27,4,"no",4,14,6,4,0 4 | "female",32,15,"yes",1,12,1,4,0 5 | "male",57,15,"yes",5,18,6,5,0 6 | "male",22,0.75,"no",2,17,6,3,0 7 | "female",32,1.5,"no",2,17,5,5,0 8 | "female",22,0.75,"no",2,12,1,3,0 9 | "male",57,15,"yes",2,14,4,4,0 10 | "female",32,15,"yes",4,16,1,2,0 11 | "male",22,1.5,"no",4,14,4,5,0 12 | "male",37,15,"yes",2,20,7,2,0 13 | "male",27,4,"yes",4,18,6,4,0 14 | "male",47,15,"yes",5,17,6,4,0 15 | "female",22,1.5,"no",2,17,5,4,0 16 | "female",27,4,"no",4,14,5,4,0 17 | "female",37,15,"yes",1,17,5,5,0 18 | "female",37,15,"yes",2,18,4,3,0 19 | "female",22,0.75,"no",3,16,5,4,0 20 | "female",22,1.5,"no",2,16,5,5,0 21 | "female",27,10,"yes",2,14,1,5,0 22 | "female",22,1.5,"no",2,16,5,5,0 23 | "female",22,1.5,"no",2,16,5,5,0 24 | "female",27,10,"yes",4,16,5,4,0 25 | "female",32,10,"yes",3,14,1,5,0 26 | "male",37,4,"yes",2,20,6,4,0 27 | "female",22,1.5,"no",2,18,5,5,0 28 | "female",27,7,"no",4,16,1,5,0 29 | "male",42,15,"yes",5,20,6,4,0 30 | "male",27,4,"yes",3,16,5,5,0 31 | "female",27,4,"yes",3,17,5,4,0 32 | "male",42,15,"yes",4,20,6,3,0 33 | "female",22,1.5,"no",3,16,5,5,0 34 | "male",27,0.417,"no",4,17,6,4,0 35 | "female",42,15,"yes",5,14,5,4,0 36 | "male",32,4,"yes",1,18,6,4,0 37 | 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