├── .ipynb_checkpoints ├── DataFrames 1-checkpoint.ipynb ├── DataFrames 2-checkpoint.ipynb ├── DataFrames 3-checkpoint.ipynb ├── GroupBy-checkpoint.ipynb ├── Input and Output-checkpoint.ipynb ├── Merge, Join and Concat-checkpoint.ipynb ├── Multindex-checkpoint.ipynb ├── Options and Settings-checkpoint.ipynb ├── Panels-checkpoint.ipynb ├── Read_CSV-checkpoint.ipynb ├── Series-checkpoint.ipynb ├── Test-checkpoint.ipynb ├── Untitled-checkpoint.ipynb ├── Visualization-checkpoint.ipynb ├── Working with Date and Time-checkpoint.ipynb └── Working with Text Data-checkpoint.ipynb ├── Apple Health Data.ipynb ├── Data - Multiple Worksheets.xlsx ├── Data - Single Worksheet.xlsx ├── DataFrames 1.ipynb ├── DataFrames 2.ipynb ├── DataFrames 3.ipynb ├── Electronic Production India.ipynb ├── GroupBy.ipynb ├── Input and Output.ipynb ├── Merge, Join and Concat.ipynb ├── Multindex.ipynb ├── Options and Settings.ipynb ├── Panels.ipynb ├── Production_India_Plotly.ipynb ├── README.md ├── Read_CSV.ipynb ├── Restaurant - Customers.csv ├── Restaurant - Foods.csv ├── Restaurant - Week 1 Sales.csv ├── Restaurant - Week 1 Satisfaction.csv ├── Restaurant - Week 2 Sales.csv ├── Series.ipynb ├── State Name.xlsx ├── TN Population.ipynb ├── Test.ipynb ├── Untitled.ipynb ├── Visualization.ipynb ├── Working with Date and Time.ipynb ├── Working with Text Data.ipynb ├── bigmac.csv ├── chicago.csv ├── crime_india.csv ├── ecommerce.csv ├── employees.csv ├── foods.csv ├── fortune1000.csv ├── google_stock_price.csv ├── jamesbond.csv ├── nba.csv ├── pokemon.csv ├── pyplot-maps.ipynb ├── quarters.csv ├── revenue.csv ├── salesmen.csv └── worldstats.csv /.ipynb_checkpoints/Input and Output-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "t = pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head()" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head(2)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": null, 39 | "metadata": { 40 | "collapsed": true 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "t.to_csv(\"test_Export.csv\")" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "collapsed": true 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "t.to_csv(\"test_Export.csv\",index=False)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 4, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "crime_india = pd.read_csv(\"https://data.gov.in/node/4223881/datastore/export/csv\")" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 6, 70 | "metadata": { 71 | "collapsed": true 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "crime_india.to_csv(\"crime_india.csv\",index=False,encoding=\"UTF8\")" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 7, 81 | "metadata": {}, 82 | "outputs": [ 83 | { 84 | "data": { 85 | "text/html": [ 86 | "
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First NameLast NameCityGender
0BrandonJamesMiamiM
1SeanHawkinsDenverM
2JudyDayLos AngelesF
3AshleyRuizSan FranciscoF
4StephanieGomezPortlandF
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" 149 | ], 150 | "text/plain": [ 151 | " First Name Last Name City Gender\n", 152 | "0 Brandon James Miami M\n", 153 | "1 Sean Hawkins Denver M\n", 154 | "2 Judy Day Los Angeles F\n", 155 | "3 Ashley Ruiz San Francisco F\n", 156 | "4 Stephanie Gomez Portland F" 157 | ] 158 | }, 159 | "execution_count": 7, 160 | "metadata": {}, 161 | "output_type": "execute_result" 162 | } 163 | ], 164 | "source": [ 165 | "pd.read_excel(\"Data - Multiple Worksheets.xlsx\")" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": 8, 171 | "metadata": {}, 172 | "outputs": [ 173 | { 174 | "data": { 175 | "text/html": [ 176 | "
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First NameLast NameCityGender
0ParkerPowerRaleighF
1PrestonPrescottPhiladelphiaF
2RonaldoDonaldoBangorM
3MeganStillerSan FranciscoM
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" 239 | ], 240 | "text/plain": [ 241 | " First Name Last Name City Gender\n", 242 | "0 Parker Power Raleigh F\n", 243 | "1 Preston Prescott Philadelphia F\n", 244 | "2 Ronaldo Donaldo Bangor M\n", 245 | "3 Megan Stiller San Francisco M\n", 246 | "4 Bustin Jieber Austin F" 247 | ] 248 | }, 249 | "execution_count": 8, 250 | "metadata": {}, 251 | "output_type": "execute_result" 252 | } 253 | ], 254 | "source": [ 255 | "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=1)" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": 9, 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "data": { 265 | "text/plain": [ 266 | "OrderedDict([(0, First Name Last Name City Gender\n", 267 | " 0 Brandon James Miami M\n", 268 | " 1 Sean Hawkins Denver M\n", 269 | " 2 Judy Day Los Angeles F\n", 270 | " 3 Ashley Ruiz San Francisco F\n", 271 | " 4 Stephanie Gomez Portland F),\n", 272 | " (1, First Name Last Name City Gender\n", 273 | " 0 Parker Power Raleigh F\n", 274 | " 1 Preston Prescott Philadelphia F\n", 275 | " 2 Ronaldo Donaldo Bangor M\n", 276 | " 3 Megan Stiller San Francisco M\n", 277 | " 4 Bustin Jieber Austin F)])" 278 | ] 279 | }, 280 | "execution_count": 9, 281 | "metadata": {}, 282 | "output_type": "execute_result" 283 | } 284 | ], 285 | "source": [ 286 | "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=[0,1])" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 15, 292 | "metadata": {}, 293 | "outputs": [], 294 | "source": [ 295 | "state = crime_india[\"State/UT (Col.3)\"]" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 17, 301 | "metadata": {}, 302 | "outputs": [], 303 | "source": [ 304 | "excel_file = pd.ExcelWriter(\"State Name.xlsx\")" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 18, 310 | "metadata": {}, 311 | "outputs": [], 312 | "source": [ 313 | "state.to_excel(excel_file,index=False)" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 19, 319 | "metadata": { 320 | "collapsed": true 321 | }, 322 | "outputs": [], 323 | "source": [ 324 | "excel_file.save()" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "metadata": { 331 | "collapsed": true 332 | }, 333 | "outputs": [], 334 | "source": [] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "metadata": { 340 | "collapsed": true 341 | }, 342 | "outputs": [], 343 | "source": [] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": null, 348 | "metadata": { 349 | "collapsed": true 350 | }, 351 | "outputs": [], 352 | "source": [] 353 | }, 354 | { 355 | "cell_type": "code", 356 | "execution_count": null, 357 | "metadata": { 358 | "collapsed": true 359 | }, 360 | "outputs": [], 361 | "source": [] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": null, 366 | "metadata": { 367 | "collapsed": true 368 | }, 369 | "outputs": [], 370 | "source": [] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": null, 375 | "metadata": { 376 | "collapsed": true 377 | }, 378 | "outputs": [], 379 | "source": [] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "metadata": { 385 | "collapsed": true 386 | }, 387 | "outputs": [], 388 | "source": [] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": null, 393 | "metadata": { 394 | "collapsed": true 395 | }, 396 | "outputs": [], 397 | "source": [] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": null, 402 | "metadata": { 403 | "collapsed": true 404 | }, 405 | "outputs": [], 406 | "source": [] 407 | }, 408 | { 409 | "cell_type": "code", 410 | "execution_count": null, 411 | "metadata": { 412 | "collapsed": true 413 | }, 414 | "outputs": [], 415 | "source": [] 416 | }, 417 | { 418 | "cell_type": "code", 419 | "execution_count": null, 420 | "metadata": { 421 | "collapsed": true 422 | }, 423 | "outputs": [], 424 | "source": [] 425 | }, 426 | { 427 | "cell_type": "code", 428 | "execution_count": null, 429 | "metadata": { 430 | "collapsed": true 431 | }, 432 | "outputs": [], 433 | "source": [] 434 | }, 435 | { 436 | "cell_type": "code", 437 | "execution_count": null, 438 | "metadata": { 439 | "collapsed": true 440 | }, 441 | "outputs": [], 442 | "source": [] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": null, 447 | "metadata": { 448 | "collapsed": true 449 | }, 450 | "outputs": [], 451 | "source": [] 452 | }, 453 | { 454 | "cell_type": "code", 455 | "execution_count": null, 456 | "metadata": { 457 | "collapsed": true 458 | }, 459 | "outputs": [], 460 | "source": [] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": { 466 | "collapsed": true 467 | }, 468 | "outputs": [], 469 | "source": [] 470 | } 471 | ], 472 | "metadata": { 473 | "kernelspec": { 474 | "display_name": "Python 3", 475 | "language": "python", 476 | "name": "python3" 477 | }, 478 | "language_info": { 479 | "codemirror_mode": { 480 | "name": "ipython", 481 | "version": 3 482 | }, 483 | "file_extension": ".py", 484 | "mimetype": "text/x-python", 485 | "name": "python", 486 | "nbconvert_exporter": "python", 487 | "pygments_lexer": "ipython3", 488 | "version": "3.6.4" 489 | } 490 | }, 491 | "nbformat": 4, 492 | "nbformat_minor": 2 493 | } 494 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Options and Settings-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import numpy as np" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 3, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "data = np.random.randint(1,100,[1000,50])" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 6, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/html": [ 32 | "
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"execution_count": null, 859 | "metadata": { 860 | "collapsed": true 861 | }, 862 | "outputs": [], 863 | "source": [] 864 | }, 865 | { 866 | "cell_type": "code", 867 | "execution_count": null, 868 | "metadata": { 869 | "collapsed": true 870 | }, 871 | "outputs": [], 872 | "source": [] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": null, 877 | "metadata": { 878 | "collapsed": true 879 | }, 880 | "outputs": [], 881 | "source": [] 882 | }, 883 | { 884 | "cell_type": "code", 885 | "execution_count": null, 886 | "metadata": { 887 | "collapsed": true 888 | }, 889 | "outputs": [], 890 | "source": [] 891 | }, 892 | { 893 | "cell_type": "code", 894 | "execution_count": null, 895 | "metadata": { 896 | "collapsed": true 897 | }, 898 | "outputs": [], 899 | "source": [] 900 | } 901 | ], 902 | "metadata": { 903 | "kernelspec": { 904 | "display_name": "Python 3", 905 | "language": "python", 906 | "name": "python3" 907 | }, 908 | "language_info": { 909 | "codemirror_mode": { 910 | "name": "ipython", 911 | "version": 3 912 | }, 913 | "file_extension": ".py", 914 | "mimetype": "text/x-python", 915 | "name": "python", 916 | "nbconvert_exporter": "python", 917 | "pygments_lexer": "ipython3", 918 | "version": "3.6.4" 919 | } 920 | }, 921 | "nbformat": 4, 922 | "nbformat_minor": 2 923 | } 924 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Read_CSV-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "ename": "TypeError", 21 | "evalue": "parser_f() missing 1 required positional argument: 'filepath_or_buffer'", 22 | "output_type": "error", 23 | "traceback": [ 24 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 25 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 26 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 27 | "\u001b[0;31mTypeError\u001b[0m: parser_f() missing 1 required positional argument: 'filepath_or_buffer'" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "pd.read_csv()" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [] 43 | } 44 | ], 45 | "metadata": { 46 | "kernelspec": { 47 | "display_name": "Python 3", 48 | "language": "python", 49 | "name": "python3" 50 | }, 51 | "language_info": { 52 | "codemirror_mode": { 53 | "name": "ipython", 54 | "version": 3 55 | }, 56 | "file_extension": ".py", 57 | "mimetype": "text/x-python", 58 | "name": "python", 59 | "nbconvert_exporter": "python", 60 | "pygments_lexer": "ipython3", 61 | "version": "3.6.4" 62 | } 63 | }, 64 | "nbformat": 4, 65 | "nbformat_minor": 2 66 | } 67 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Series-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Create series in python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "ice_cream = ['vanilla','chocolate','mousse','rocky road']\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 3, 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/plain": [ 30 | "0 vanilla\n", 31 | "1 chohcolate\n", 32 | "2 mousse\n", 33 | "3 banana\n", 34 | "dtype: object" 35 | ] 36 | }, 37 | "execution_count": 3, 38 | "metadata": {}, 39 | "output_type": "execute_result" 40 | } 41 | ], 42 | "source": [ 43 | "pd.Series(ice_cream)" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 5, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "data": { 53 | "text/plain": [ 54 | "0 1\n", 55 | "1 2\n", 56 | "2 8\n", 57 | "3 4\n", 58 | "4 5\n", 59 | "5 22\n", 60 | "6 8\n", 61 | "7 9\n", 62 | "dtype: int64" 63 | ] 64 | }, 65 | "execution_count": 5, 66 | "metadata": {}, 67 | "output_type": "execute_result" 68 | } 69 | ], 70 | "source": [ 71 | "lottery = [1,2,8,4,5,22,8,9]\n", 72 | "pd.Series(lottery)" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 6, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "0 True\n", 84 | "1 False\n", 85 | "2 False\n", 86 | "3 True\n", 87 | "4 True\n", 88 | "5 True\n", 89 | "dtype: bool" 90 | ] 91 | }, 92 | "execution_count": 6, 93 | "metadata": {}, 94 | "output_type": "execute_result" 95 | } 96 | ], 97 | "source": [ 98 | "registration = [True, False, False, True, True, True]\n", 99 | "pd.Series(registration)" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 7, 105 | "metadata": {}, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/plain": [ 110 | "Play Stadium\n", 111 | "Study Class\n", 112 | "Work Office\n", 113 | "dtype: object" 114 | ] 115 | }, 116 | "execution_count": 7, 117 | "metadata": {}, 118 | "output_type": "execute_result" 119 | } 120 | ], 121 | "source": [ 122 | "merriam = {\"Work\":\"Office\",\"Play\":\"Stadium\",\"Study\":\"Class\"}\n", 123 | "pd.Series(merriam)" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 8, 129 | "metadata": { 130 | "collapsed": true 131 | }, 132 | "outputs": [], 133 | "source": [ 134 | "## Intro to Attributes" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 9, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "data": { 144 | "text/plain": [ 145 | "0 Yo\n", 146 | "1 Yep\n", 147 | "2 thats\n", 148 | "3 awesomwe\n", 149 | "dtype: object" 150 | ] 151 | }, 152 | "execution_count": 9, 153 | "metadata": {}, 154 | "output_type": "execute_result" 155 | } 156 | ], 157 | "source": [ 158 | "about_me = [\"Yo\",\"Yep\",\"thats\",\"awesomwe\"]\n", 159 | "s = pd.Series(about_me)\n", 160 | "s" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 10, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "data": { 170 | "text/plain": [ 171 | "array(['Yo', 'Yep', 'thats', 'awesomwe'], dtype=object)" 172 | ] 173 | }, 174 | "execution_count": 10, 175 | "metadata": {}, 176 | "output_type": "execute_result" 177 | } 178 | ], 179 | "source": [ 180 | "s.values" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 11, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "RangeIndex(start=0, stop=4, step=1)" 192 | ] 193 | }, 194 | "execution_count": 11, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "s.index" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 12, 206 | "metadata": {}, 207 | "outputs": [ 208 | { 209 | "data": { 210 | "text/plain": [ 211 | "dtype('O')" 212 | ] 213 | }, 214 | "execution_count": 12, 215 | "metadata": {}, 216 | "output_type": "execute_result" 217 | } 218 | ], 219 | "source": [ 220 | "s.dtype" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 13, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "0 1.44\n", 232 | "1 6.80\n", 233 | "2 9.65\n", 234 | "dtype: float64" 235 | ] 236 | }, 237 | "execution_count": 13, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "prices = [1.44,6.8,9.65]\n", 244 | "s = pd.Series(prices)\n", 245 | "s" 246 | ] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": 15, 251 | "metadata": {}, 252 | "outputs": [ 253 | { 254 | "data": { 255 | "text/plain": [ 256 | "17.89" 257 | ] 258 | }, 259 | "execution_count": 15, 260 | "metadata": {}, 261 | "output_type": "execute_result" 262 | } 263 | ], 264 | "source": [ 265 | "s.sum()" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 16, 271 | "metadata": {}, 272 | "outputs": [ 273 | { 274 | "data": { 275 | "text/plain": [ 276 | "5.963333333333334" 277 | ] 278 | }, 279 | "execution_count": 16, 280 | "metadata": {}, 281 | "output_type": "execute_result" 282 | } 283 | ], 284 | "source": [ 285 | "s.mean()" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [] 292 | } 293 | ], 294 | "metadata": { 295 | "kernelspec": { 296 | "display_name": "Python 3", 297 | "language": "python", 298 | "name": "python3" 299 | }, 300 | "language_info": { 301 | "codemirror_mode": { 302 | "name": "ipython", 303 | "version": 3 304 | }, 305 | "file_extension": ".py", 306 | "mimetype": "text/x-python", 307 | "name": "python", 308 | "nbconvert_exporter": "python", 309 | "pygments_lexer": "ipython3", 310 | "version": "3.6.4" 311 | } 312 | }, 313 | "nbformat": 4, 314 | "nbformat_minor": 2 315 | } 316 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Test-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /Data - Multiple Worksheets.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sivabalanb/Data-Analysis-with-Pandas-and-Python/2299ec036d0c5050b68bba14aaff8bb6723966bd/Data - Multiple Worksheets.xlsx -------------------------------------------------------------------------------- /Data - Single Worksheet.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sivabalanb/Data-Analysis-with-Pandas-and-Python/2299ec036d0c5050b68bba14aaff8bb6723966bd/Data - Single Worksheet.xlsx -------------------------------------------------------------------------------- /Input and Output.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "t = pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head()" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "pd.read_csv(\"https://data.cityofnewyork.us/api/views/ic3t-wcy2/rows.csv\").head(2)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": null, 39 | "metadata": { 40 | "collapsed": true 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "t.to_csv(\"test_Export.csv\")" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "collapsed": true 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "t.to_csv(\"test_Export.csv\",index=False)" 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" 239 | ], 240 | "text/plain": [ 241 | " First Name Last Name City Gender\n", 242 | "0 Parker Power Raleigh F\n", 243 | "1 Preston Prescott Philadelphia F\n", 244 | "2 Ronaldo Donaldo Bangor M\n", 245 | "3 Megan Stiller San Francisco M\n", 246 | "4 Bustin Jieber Austin F" 247 | ] 248 | }, 249 | "execution_count": 8, 250 | "metadata": {}, 251 | "output_type": "execute_result" 252 | } 253 | ], 254 | "source": [ 255 | "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=1)" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": 9, 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "data": { 265 | "text/plain": [ 266 | "OrderedDict([(0, First Name Last Name City Gender\n", 267 | " 0 Brandon James Miami M\n", 268 | " 1 Sean Hawkins Denver M\n", 269 | " 2 Judy Day Los Angeles F\n", 270 | " 3 Ashley Ruiz San Francisco F\n", 271 | " 4 Stephanie Gomez Portland F),\n", 272 | " (1, First Name Last Name City Gender\n", 273 | " 0 Parker Power Raleigh F\n", 274 | " 1 Preston Prescott Philadelphia F\n", 275 | " 2 Ronaldo Donaldo Bangor M\n", 276 | " 3 Megan Stiller San Francisco M\n", 277 | " 4 Bustin Jieber Austin F)])" 278 | ] 279 | }, 280 | "execution_count": 9, 281 | "metadata": {}, 282 | "output_type": "execute_result" 283 | } 284 | ], 285 | "source": [ 286 | "pd.read_excel(\"Data - Multiple Worksheets.xlsx\", sheetname=[0,1])" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 15, 292 | "metadata": {}, 293 | "outputs": [], 294 | "source": [ 295 | "state = crime_india[\"State/UT (Col.3)\"]" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 17, 301 | "metadata": {}, 302 | "outputs": [], 303 | "source": [ 304 | "excel_file = pd.ExcelWriter(\"State Name.xlsx\")" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 18, 310 | "metadata": {}, 311 | "outputs": [], 312 | "source": [ 313 | "state.to_excel(excel_file,index=False)" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 19, 319 | "metadata": { 320 | "collapsed": true 321 | }, 322 | "outputs": [], 323 | "source": [ 324 | "excel_file.save()" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "metadata": { 331 | "collapsed": true 332 | }, 333 | "outputs": [], 334 | "source": [] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "metadata": { 340 | "collapsed": true 341 | }, 342 | "outputs": [], 343 | "source": [] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": null, 348 | "metadata": { 349 | "collapsed": true 350 | }, 351 | "outputs": [], 352 | "source": [] 353 | }, 354 | { 355 | "cell_type": "code", 356 | "execution_count": null, 357 | "metadata": { 358 | "collapsed": true 359 | }, 360 | "outputs": [], 361 | "source": [] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": null, 366 | "metadata": { 367 | "collapsed": true 368 | }, 369 | "outputs": [], 370 | "source": [] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": null, 375 | "metadata": { 376 | "collapsed": true 377 | }, 378 | "outputs": [], 379 | "source": [] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "metadata": { 385 | "collapsed": true 386 | }, 387 | "outputs": [], 388 | "source": [] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": null, 393 | "metadata": { 394 | "collapsed": true 395 | }, 396 | "outputs": [], 397 | "source": [] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": null, 402 | "metadata": { 403 | "collapsed": true 404 | }, 405 | "outputs": [], 406 | "source": [] 407 | }, 408 | { 409 | "cell_type": "code", 410 | "execution_count": null, 411 | "metadata": { 412 | "collapsed": true 413 | }, 414 | "outputs": [], 415 | "source": [] 416 | }, 417 | { 418 | "cell_type": "code", 419 | "execution_count": null, 420 | "metadata": { 421 | "collapsed": true 422 | }, 423 | "outputs": [], 424 | "source": [] 425 | }, 426 | { 427 | "cell_type": "code", 428 | "execution_count": null, 429 | "metadata": { 430 | "collapsed": true 431 | }, 432 | "outputs": [], 433 | "source": [] 434 | }, 435 | { 436 | "cell_type": "code", 437 | "execution_count": null, 438 | "metadata": { 439 | "collapsed": true 440 | }, 441 | "outputs": [], 442 | "source": [] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": null, 447 | "metadata": { 448 | "collapsed": true 449 | }, 450 | "outputs": [], 451 | "source": [] 452 | }, 453 | { 454 | "cell_type": "code", 455 | "execution_count": null, 456 | "metadata": { 457 | "collapsed": true 458 | }, 459 | "outputs": [], 460 | "source": [] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": { 466 | "collapsed": true 467 | }, 468 | "outputs": [], 469 | "source": [] 470 | } 471 | ], 472 | "metadata": { 473 | "kernelspec": { 474 | "display_name": "Python 3", 475 | "language": "python", 476 | "name": "python3" 477 | }, 478 | "language_info": { 479 | "codemirror_mode": { 480 | "name": "ipython", 481 | "version": 3 482 | }, 483 | "file_extension": ".py", 484 | "mimetype": "text/x-python", 485 | "name": "python", 486 | "nbconvert_exporter": "python", 487 | "pygments_lexer": "ipython3", 488 | "version": "3.6.4" 489 | } 490 | }, 491 | "nbformat": 4, 492 | "nbformat_minor": 2 493 | } 494 | -------------------------------------------------------------------------------- /Options and Settings.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import numpy as np" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 3, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "data = np.random.randint(1,100,[1000,50])" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 6, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/html": [ 32 | "
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" 814 | ], 815 | "text/plain": [ 816 | " 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 \\\n", 817 | "0 21 37 22 42 18 22 51 21 70 54 ... 60 26 42 44 66 93 79 \n", 818 | ".. .. .. .. .. .. .. .. .. .. .. ... .. .. .. .. .. .. .. \n", 819 | "999 40 77 38 99 43 24 19 51 10 85 ... 48 64 91 2 50 90 97 \n", 820 | "\n", 821 | " 47 48 49 \n", 822 | "0 34 83 98 \n", 823 | ".. .. .. .. \n", 824 | "999 86 10 15 \n", 825 | "\n", 826 | "[1000 rows x 50 columns]" 827 | ] 828 | }, 829 | "execution_count": 28, 830 | "metadata": {}, 831 | "output_type": "execute_result" 832 | } 833 | ], 834 | "source": [ 835 | "df" 836 | ] 837 | }, 838 | { 839 | "cell_type": "code", 840 | "execution_count": null, 841 | "metadata": { 842 | "collapsed": true 843 | }, 844 | "outputs": [], 845 | "source": [] 846 | }, 847 | { 848 | "cell_type": "code", 849 | "execution_count": null, 850 | "metadata": { 851 | "collapsed": true 852 | }, 853 | "outputs": [], 854 | "source": [] 855 | }, 856 | { 857 | "cell_type": "code", 858 | "execution_count": null, 859 | "metadata": { 860 | "collapsed": true 861 | }, 862 | "outputs": [], 863 | "source": [] 864 | }, 865 | { 866 | "cell_type": "code", 867 | "execution_count": null, 868 | "metadata": { 869 | "collapsed": true 870 | }, 871 | "outputs": [], 872 | "source": [] 873 | }, 874 | { 875 | "cell_type": "code", 876 | "execution_count": null, 877 | "metadata": { 878 | "collapsed": true 879 | }, 880 | "outputs": [], 881 | "source": [] 882 | }, 883 | { 884 | "cell_type": "code", 885 | "execution_count": null, 886 | "metadata": { 887 | "collapsed": true 888 | }, 889 | "outputs": [], 890 | "source": [] 891 | }, 892 | { 893 | "cell_type": "code", 894 | "execution_count": null, 895 | "metadata": { 896 | "collapsed": true 897 | }, 898 | "outputs": [], 899 | "source": [] 900 | } 901 | ], 902 | "metadata": { 903 | "kernelspec": { 904 | "display_name": "Python 3", 905 | "language": "python", 906 | "name": "python3" 907 | }, 908 | "language_info": { 909 | "codemirror_mode": { 910 | "name": "ipython", 911 | "version": 3 912 | }, 913 | "file_extension": ".py", 914 | "mimetype": "text/x-python", 915 | "name": "python", 916 | "nbconvert_exporter": "python", 917 | "pygments_lexer": "ipython3", 918 | "version": "3.6.4" 919 | } 920 | }, 921 | "nbformat": 4, 922 | "nbformat_minor": 2 923 | } 924 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 | Data Analysis with Pandas and Python 3 |
4 | 5 |

🐼 Data Analysis with Pandas and Python

6 | 7 |

8 | Welcome to the repository for the **Data Analysis with Pandas and Python** course on Udemy by Boris Paskhaver. 🎓 9 |

10 | 11 |

12 | Language: Python 13 | Library: Pandas 14 |

15 | 16 |

17 | This repository serves as a comprehensive resource for learning and applying the fundamentals of data analysis using the powerful Pandas library in Python. It contains well-documented code examples, Jupyter Notebooks, and real-world datasets that are covered in the course. 💡📊 18 |

19 | 20 |

21 | Feel free to explore the code, review the notebooks, and experiment with the provided materials. Engage with the course community, start the repository, and fork it as you see fit. 🚀🔍 22 |

23 | 24 |

25 | Let's embark on this data-driven adventure together and unleash the power of Python and Pandas for insightful data analysis! 🚀✨ 26 |

27 | 28 |

29 | Good luck on your journey to learn Data Analysis with Pandas and Python! May you gain valuable insights and excel in your data-driven endeavors. 🎉🔥 30 |

31 | 32 | ## Table of Contents 33 | 34 | - [Installation](#installation) 35 | - [Usage](#usage) 36 | - [Contributing](#contributing) 37 | - [License](#license) 38 | 39 | ### Installation 40 | 41 | To use the projects in this repository, you need to have Python and the Pandas library installed on your machine. You can install Python from the [official Python website](https://www.python.org) and Pandas using pip, the Python package installer. 42 | 43 | 44 | ``` 45 | pip install pandas 46 | ``` 47 | It is recommended to use a virtual environment to keep the project dependencies isolated. You can create a virtual environment using venv or conda, depending on your preference. 48 | #### Usage 49 | 50 | Each project in this repository is located in its own Jupyter Notebook file (.ipynb). To use a specific project, open the notebook in Jupyter Notebook or JupyterLab. 51 | 52 | To run the code in the notebook, make sure you have the necessary dependencies installed. You can install the required dependencies by running the following command: 53 | 54 | 55 | 56 | After installing the dependencies, launch Jupyter Notebook or JupyterLab from the terminal: 57 | 58 | ``` 59 | jupyter notebook 60 | ``` 61 | or 62 | 63 | ``` 64 | jupyter lab 65 | ``` 66 | In your web browser, navigate to the URL provided by Jupyter Notebook/Lab and open the desired notebook (.ipynb) file. Execute the cells in the notebook to see the code and its outputs. 67 | 68 | Feel free to modify the code cells and adapt them to your specific needs. Each project contains comments and documentation to help you understand the code and its purpose. 69 | #### Contributing 70 | 71 | 🎉 Contributions to this repository are welcome! If you have a project or improvement to suggest, please follow these steps: 72 | 73 | 1. 🍴 Fork the repository. 74 | 2. 🔧 Create a new branch for your feature or bug fix. 75 | 3. 🚀 Implement your changes. 76 | 4. ✔️ Test your changes to ensure they work correctly. 77 | 5. 💾 Commit your changes and push them to your forked repository. 78 | 6. 📩 Submit a pull request detailing your changes. 79 | 80 | Please make sure to follow the repository's code style and include appropriate documentation for your changes. 81 | 82 | #### License 83 | 84 | 📄 The code in this repository is available under the MIT License. You are free to use, modify, and distribute it for personal or commercial purposes. Please refer to the [LICENSE](./LICENSE) file for more information. 85 | 86 | -------------------------------------------------------------------------------- /Restaurant - Foods.csv: -------------------------------------------------------------------------------- 1 | Food ID,Food Item,Price 2 | 1,Sushi,3.99 3 | 2,Burrito,9.99 4 | 3,Taco,2.99 5 | 4,Quesadilla,4.25 6 | 5,Pizza,2.49 7 | 6,Pasta,13.99 8 | 7,Steak,24.99 9 | 8,Salad,11.25 10 | 9,Donut,0.99 11 | 10,Drink,1.75 -------------------------------------------------------------------------------- /Restaurant - Week 1 Sales.csv: -------------------------------------------------------------------------------- 1 | Customer ID,Food ID 2 | 537,9 3 | 97,4 4 | 658,1 5 | 202,2 6 | 155,9 7 | 213,8 8 | 600,1 9 | 503,5 10 | 71,3 11 | 174,3 12 | 961,9 13 | 966,5 14 | 641,4 15 | 288,2 16 | 149,4 17 | 954,2 18 | 147,9 19 | 155,1 20 | 550,6 21 | 101,7 22 | 549,6 23 | 75,6 24 | 78,7 25 | 514,5 26 | 833,7 27 | 329,8 28 | 586,10 29 | 341,1 30 | 519,10 31 | 680,9 32 | 419,7 33 | 20,1 34 | 822,6 35 | 226,10 36 | 203,2 37 | 77,1 38 | 628,4 39 | 296,9 40 | 821,9 41 | 697,8 42 | 264,7 43 | 477,9 44 | 524,4 45 | 121,3 46 | 290,7 47 | 100,1 48 | 260,6 49 | 798,9 50 | 462,3 51 | 896,7 52 | 953,1 53 | 682,10 54 | 809,7 55 | 450,10 56 | 772,7 57 | 304,3 58 | 159,6 59 | 189,4 60 | 876,2 61 | 864,8 62 | 799,8 63 | 68,6 64 | 812,8 65 | 30,2 66 | 921,2 67 | 941,6 68 | 108,3 69 | 315,4 70 | 358,10 71 | 249,9 72 | 491,9 73 | 110,8 74 | 737,3 75 | 836,6 76 | 749,9 77 | 758,8 78 | 527,3 79 | 677,10 80 | 741,3 81 | 540,3 82 | 433,7 83 | 250,10 84 | 504,1 85 | 819,4 86 | 910,7 87 | 351,10 88 | 282,7 89 | 117,5 90 | 937,10 91 | 63,6 92 | 144,2 93 | 393,7 94 | 380,7 95 | 515,7 96 | 233,3 97 | 357,3 98 | 3,2 99 | 875,2 100 | 352,6 101 | 93,3 102 | 323,1 103 | 21,4 104 | 64,10 105 | 912,10 106 | 327,6 107 | 399,2 108 | 459,6 109 | 418,7 110 | 669,3 111 | 259,9 112 | 816,7 113 | 761,9 114 | 410,7 115 | 304,2 116 | 772,10 117 | 80,2 118 | 363,9 119 | 504,5 120 | 728,6 121 | 71,8 122 | 479,6 123 | 922,1 124 | 244,2 125 | 319,9 126 | 909,1 127 | 919,10 128 | 51,2 129 | 26,9 130 | 472,8 131 | 77,9 132 | 608,6 133 | 160,5 134 | 645,8 135 | 574,10 136 | 374,4 137 | 100,7 138 | 762,8 139 | 45,7 140 | 332,7 141 | 338,10 142 | 140,8 143 | 567,2 144 | 602,1 145 | 10,2 146 | 77,2 147 | 74,1 148 | 1000,2 149 | 529,7 150 | 881,5 151 | 673,7 152 | 107,2 153 | 876,8 154 | 703,1 155 | 225,10 156 | 962,1 157 | 114,5 158 | 250,7 159 | 346,4 160 | 191,2 161 | 331,2 162 | 310,2 163 | 738,9 164 | 427,7 165 | 331,5 166 | 902,6 167 | 867,9 168 | 385,5 169 | 555,9 170 | 67,7 171 | 138,4 172 | 775,5 173 | 833,3 174 | 648,5 175 | 475,10 176 | 483,6 177 | 968,1 178 | 203,3 179 | 313,9 180 | 263,8 181 | 871,5 182 | 747,5 183 | 38,4 184 | 190,9 185 | 348,6 186 | 226,3 187 | 167,9 188 | 671,8 189 | 190,1 190 | 909,4 191 | 501,6 192 | 406,2 193 | 47,8 194 | 482,3 195 | 163,1 196 | 991,2 197 | 539,6 198 | 53,2 199 | 51,10 200 | 348,9 201 | 321,1 202 | 493,9 203 | 650,9 204 | 310,1 205 | 848,6 206 | 307,9 207 | 798,1 208 | 606,7 209 | 100,4 210 | 783,6 211 | 985,5 212 | 123,6 213 | 809,10 214 | 21,4 215 | 764,9 216 | 92,1 217 | 386,6 218 | 331,3 219 | 62,5 220 | 91,8 221 | 368,6 222 | 147,8 223 | 745,4 224 | 184,3 225 | 828,8 226 | 578,5 227 | 491,4 228 | 62,4 229 | 77,9 230 | 506,9 231 | 271,9 232 | 584,10 233 | 148,5 234 | 595,3 235 | 274,10 236 | 646,1 237 | 520,8 238 | 644,8 239 | 725,8 240 | 934,4 241 | 151,10 242 | 945,5 243 | 343,3 244 | 380,9 245 | 911,4 246 | 621,9 247 | 413,9 248 | 926,6 249 | 134,3 250 | 396,6 251 | 535,10 252 | -------------------------------------------------------------------------------- /Restaurant - Week 1 Satisfaction.csv: -------------------------------------------------------------------------------- 1 | Satisfaction Rating 2 | 2 3 | 7 4 | 3 5 | 7 6 | 10 7 | 3 8 | 2 9 | 5 10 | 10 11 | 7 12 | 7 13 | 5 14 | 3 15 | 2 16 | 7 17 | 4 18 | 6 19 | 1 20 | 5 21 | 5 22 | 3 23 | 2 24 | 3 25 | 10 26 | 4 27 | 9 28 | 1 29 | 4 30 | 4 31 | 9 32 | 8 33 | 9 34 | 7 35 | 1 36 | 1 37 | 3 38 | 3 39 | 4 40 | 3 41 | 9 42 | 9 43 | 8 44 | 8 45 | 2 46 | 3 47 | 10 48 | 3 49 | 8 50 | 6 51 | 9 52 | 9 53 | 3 54 | 4 55 | 8 56 | 8 57 | 4 58 | 10 59 | 4 60 | 10 61 | 9 62 | 10 63 | 6 64 | 6 65 | 5 66 | 5 67 | 9 68 | 9 69 | 1 70 | 9 71 | 6 72 | 8 73 | 1 74 | 8 75 | 8 76 | 1 77 | 8 78 | 1 79 | 6 80 | 2 81 | 8 82 | 2 83 | 2 84 | 3 85 | 8 86 | 4 87 | 3 88 | 5 89 | 1 90 | 1 91 | 5 92 | 3 93 | 8 94 | 9 95 | 2 96 | 6 97 | 5 98 | 7 99 | 4 100 | 6 101 | 4 102 | 7 103 | 1 104 | 2 105 | 3 106 | 1 107 | 9 108 | 8 109 | 2 110 | 5 111 | 7 112 | 10 113 | 6 114 | 7 115 | 1 116 | 7 117 | 1 118 | 4 119 | 9 120 | 2 121 | 7 122 | 5 123 | 4 124 | 7 125 | 4 126 | 1 127 | 7 128 | 4 129 | 1 130 | 5 131 | 4 132 | 1 133 | 9 134 | 5 135 | 6 136 | 2 137 | 2 138 | 1 139 | 5 140 | 9 141 | 6 142 | 8 143 | 3 144 | 7 145 | 10 146 | 3 147 | 1 148 | 1 149 | 5 150 | 7 151 | 1 152 | 6 153 | 10 154 | 6 155 | 4 156 | 8 157 | 5 158 | 4 159 | 1 160 | 4 161 | 3 162 | 7 163 | 5 164 | 9 165 | 1 166 | 5 167 | 7 168 | 3 169 | 8 170 | 6 171 | 10 172 | 4 173 | 5 174 | 1 175 | 3 176 | 1 177 | 6 178 | 2 179 | 4 180 | 8 181 | 8 182 | 9 183 | 10 184 | 10 185 | 10 186 | 6 187 | 10 188 | 4 189 | 6 190 | 2 191 | 3 192 | 2 193 | 3 194 | 10 195 | 8 196 | 5 197 | 1 198 | 9 199 | 4 200 | 5 201 | 4 202 | 1 203 | 9 204 | 8 205 | 9 206 | 8 207 | 6 208 | 6 209 | 5 210 | 9 211 | 3 212 | 5 213 | 4 214 | 5 215 | 1 216 | 10 217 | 6 218 | 3 219 | 5 220 | 7 221 | 10 222 | 3 223 | 8 224 | 4 225 | 2 226 | 5 227 | 2 228 | 6 229 | 4 230 | 9 231 | 2 232 | 4 233 | 8 234 | 4 235 | 2 236 | 9 237 | 4 238 | 6 239 | 6 240 | 7 241 | 10 242 | 8 243 | 1 244 | 2 245 | 8 246 | 2 247 | 1 248 | 2 249 | 8 250 | 10 251 | 3 -------------------------------------------------------------------------------- /Restaurant - Week 2 Sales.csv: -------------------------------------------------------------------------------- 1 | Customer ID,Food ID 2 | 688,10 3 | 813,7 4 | 495,10 5 | 189,5 6 | 267,3 7 | 310,5 8 | 761,2 9 | 443,5 10 | 729,9 11 | 741,8 12 | 847,6 13 | 156,10 14 | 550,7 15 | 620,10 16 | 272,4 17 | 511,2 18 | 8,6 19 | 534,7 20 | 909,6 21 | 732,9 22 | 372,4 23 | 713,1 24 | 496,3 25 | 381,9 26 | 13,2 27 | 101,4 28 | 325,7 29 | 674,2 30 | 564,8 31 | 578,5 32 | 21,4 33 | 755,7 34 | 509,7 35 | 639,2 36 | 170,2 37 | 668,3 38 | 321,3 39 | 629,2 40 | 767,6 41 | 799,7 42 | 253,8 43 | 473,1 44 | 537,5 45 | 343,5 46 | 761,3 47 | 922,1 48 | 780,6 49 | 198,10 50 | 937,10 51 | 479,3 52 | 706,7 53 | 281,6 54 | 726,7 55 | 343,2 56 | 277,3 57 | 969,3 58 | 543,4 59 | 275,8 60 | 787,7 61 | 424,4 62 | 612,6 63 | 379,9 64 | 647,1 65 | 942,3 66 | 528,10 67 | 673,4 68 | 853,3 69 | 111,3 70 | 736,8 71 | 503,8 72 | 445,6 73 | 580,10 74 | 670,6 75 | 359,6 76 | 132,8 77 | 819,5 78 | 829,6 79 | 928,10 80 | 592,5 81 | 163,10 82 | 694,7 83 | 98,5 84 | 791,2 85 | 398,5 86 | 709,4 87 | 56,3 88 | 397,1 89 | 239,6 90 | 304,3 91 | 893,3 92 | 581,1 93 | 693,3 94 | 827,4 95 | 815,6 96 | 574,9 97 | 752,10 98 | 596,8 99 | 489,9 100 | 792,4 101 | 861,1 102 | 303,9 103 | 548,9 104 | 517,2 105 | 784,6 106 | 621,6 107 | 529,10 108 | 204,10 109 | 136,1 110 | 994,2 111 | 186,3 112 | 253,7 113 | 859,7 114 | 496,8 115 | 423,2 116 | 211,4 117 | 884,7 118 | 81,10 119 | 437,10 120 | 526,1 121 | 951,5 122 | 508,4 123 | 236,5 124 | 127,1 125 | 198,7 126 | 877,10 127 | 816,2 128 | 666,9 129 | 415,5 130 | 24,8 131 | 720,1 132 | 240,2 133 | 919,8 134 | 459,1 135 | 938,10 136 | 46,6 137 | 458,9 138 | 630,6 139 | 75,4 140 | 287,5 141 | 927,4 142 | 522,8 143 | 805,8 144 | 365,6 145 | 257,1 146 | 957,10 147 | 692,4 148 | 547,10 149 | 798,5 150 | 913,2 151 | 35,8 152 | 968,4 153 | 222,10 154 | 122,2 155 | 73,8 156 | 653,9 157 | 937,2 158 | 171,2 159 | 193,2 160 | 668,9 161 | 996,10 162 | 222,10 163 | 70,2 164 | 77,7 165 | 743,8 166 | 589,4 167 | 867,10 168 | 831,4 169 | 905,1 170 | 977,7 171 | 746,10 172 | 922,2 173 | 866,9 174 | 746,10 175 | 495,6 176 | 486,6 177 | 55,9 178 | 312,2 179 | 604,3 180 | 122,10 181 | 936,9 182 | 503,9 183 | 628,7 184 | 55,4 185 | 858,5 186 | 175,6 187 | 530,7 188 | 850,10 189 | 681,10 190 | 750,6 191 | 578,5 192 | 287,8 193 | 39,10 194 | 458,3 195 | 30,4 196 | 827,10 197 | 462,8 198 | 361,6 199 | 27,4 200 | 234,1 201 | 570,8 202 | 751,2 203 | 463,10 204 | 253,5 205 | 633,1 206 | 622,9 207 | 959,4 208 | 810,2 209 | 80,4 210 | 155,3 211 | 488,2 212 | 482,1 213 | 495,2 214 | 188,8 215 | 861,5 216 | 305,2 217 | 869,7 218 | 131,1 219 | 520,7 220 | 208,4 221 | 145,5 222 | 495,2 223 | 794,6 224 | 734,10 225 | 540,3 226 | 735,2 227 | 940,8 228 | 571,7 229 | 888,2 230 | 664,6 231 | 343,7 232 | 143,4 233 | 505,3 234 | 54,8 235 | 959,2 236 | 367,8 237 | 883,8 238 | 251,9 239 | 855,4 240 | 233,3 241 | 559,10 242 | 734,1 243 | 677,3 244 | 276,4 245 | 45,8 246 | 945,4 247 | 783,10 248 | 556,10 249 | 547,9 250 | 252,9 251 | 249,6 252 | -------------------------------------------------------------------------------- /Series.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Create series in python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "ice_cream = ['vanilla','chocolate','mousse','rocky road']\n" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 3, 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/plain": [ 30 | "0 vanilla\n", 31 | "1 chohcolate\n", 32 | "2 mousse\n", 33 | "3 banana\n", 34 | "dtype: object" 35 | ] 36 | }, 37 | "execution_count": 3, 38 | "metadata": {}, 39 | "output_type": "execute_result" 40 | } 41 | ], 42 | "source": [ 43 | "pd.Series(ice_cream)" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 5, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "data": { 53 | "text/plain": [ 54 | "0 1\n", 55 | "1 2\n", 56 | "2 8\n", 57 | "3 4\n", 58 | "4 5\n", 59 | "5 22\n", 60 | "6 8\n", 61 | "7 9\n", 62 | "dtype: int64" 63 | ] 64 | }, 65 | "execution_count": 5, 66 | "metadata": {}, 67 | "output_type": "execute_result" 68 | } 69 | ], 70 | "source": [ 71 | "lottery = [1,2,8,4,5,22,8,9]\n", 72 | "pd.Series(lottery)" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 6, 78 | "metadata": {}, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "0 True\n", 84 | "1 False\n", 85 | "2 False\n", 86 | "3 True\n", 87 | "4 True\n", 88 | "5 True\n", 89 | "dtype: bool" 90 | ] 91 | }, 92 | "execution_count": 6, 93 | "metadata": {}, 94 | "output_type": "execute_result" 95 | } 96 | ], 97 | "source": [ 98 | "registration = [True, False, False, True, True, True]\n", 99 | "pd.Series(registration)" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 7, 105 | "metadata": {}, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/plain": [ 110 | "Play Stadium\n", 111 | "Study Class\n", 112 | "Work Office\n", 113 | "dtype: object" 114 | ] 115 | }, 116 | "execution_count": 7, 117 | "metadata": {}, 118 | "output_type": "execute_result" 119 | } 120 | ], 121 | "source": [ 122 | "merriam = {\"Work\":\"Office\",\"Play\":\"Stadium\",\"Study\":\"Class\"}\n", 123 | "pd.Series(merriam)" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 8, 129 | "metadata": { 130 | "collapsed": true 131 | }, 132 | "outputs": [], 133 | "source": [ 134 | "## Intro to Attributes" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 9, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "data": { 144 | "text/plain": [ 145 | "0 Yo\n", 146 | "1 Yep\n", 147 | "2 thats\n", 148 | "3 awesomwe\n", 149 | "dtype: object" 150 | ] 151 | }, 152 | "execution_count": 9, 153 | "metadata": {}, 154 | "output_type": "execute_result" 155 | } 156 | ], 157 | "source": [ 158 | "about_me = [\"Yo\",\"Yep\",\"thats\",\"awesomwe\"]\n", 159 | "s = pd.Series(about_me)\n", 160 | "s" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 10, 166 | "metadata": {}, 167 | "outputs": [ 168 | { 169 | "data": { 170 | "text/plain": [ 171 | "array(['Yo', 'Yep', 'thats', 'awesomwe'], dtype=object)" 172 | ] 173 | }, 174 | "execution_count": 10, 175 | "metadata": {}, 176 | "output_type": "execute_result" 177 | } 178 | ], 179 | "source": [ 180 | "s.values" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 11, 186 | "metadata": {}, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "RangeIndex(start=0, stop=4, step=1)" 192 | ] 193 | }, 194 | "execution_count": 11, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "s.index" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 12, 206 | "metadata": {}, 207 | "outputs": [ 208 | { 209 | "data": { 210 | "text/plain": [ 211 | "dtype('O')" 212 | ] 213 | }, 214 | "execution_count": 12, 215 | "metadata": {}, 216 | "output_type": "execute_result" 217 | } 218 | ], 219 | "source": [ 220 | "s.dtype" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 13, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "0 1.44\n", 232 | "1 6.80\n", 233 | "2 9.65\n", 234 | "dtype: float64" 235 | ] 236 | }, 237 | "execution_count": 13, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "prices = [1.44,6.8,9.65]\n", 244 | "s = pd.Series(prices)\n", 245 | "s" 246 | ] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": 15, 251 | "metadata": {}, 252 | "outputs": [ 253 | { 254 | "data": { 255 | "text/plain": [ 256 | "17.89" 257 | ] 258 | }, 259 | "execution_count": 15, 260 | "metadata": {}, 261 | "output_type": "execute_result" 262 | } 263 | ], 264 | "source": [ 265 | "s.sum()" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 16, 271 | "metadata": {}, 272 | "outputs": [ 273 | { 274 | "data": { 275 | "text/plain": [ 276 | "5.963333333333334" 277 | ] 278 | }, 279 | "execution_count": 16, 280 | "metadata": {}, 281 | "output_type": "execute_result" 282 | } 283 | ], 284 | "source": [ 285 | "s.mean()" 286 | ] 287 | }, 288 | { 289 | "cell_type": "markdown", 290 | "metadata": {}, 291 | "source": [] 292 | } 293 | ], 294 | "metadata": { 295 | "kernelspec": { 296 | "display_name": "Python 3", 297 | "language": "python", 298 | "name": "python3" 299 | }, 300 | "language_info": { 301 | "codemirror_mode": { 302 | "name": "ipython", 303 | "version": 3 304 | }, 305 | "file_extension": ".py", 306 | "mimetype": "text/x-python", 307 | "name": "python", 308 | "nbconvert_exporter": "python", 309 | "pygments_lexer": "ipython3", 310 | "version": "3.6.4" 311 | } 312 | }, 313 | "nbformat": 4, 314 | "nbformat_minor": 2 315 | } 316 | -------------------------------------------------------------------------------- /State Name.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sivabalanb/Data-Analysis-with-Pandas-and-Python/2299ec036d0c5050b68bba14aaff8bb6723966bd/State Name.xlsx -------------------------------------------------------------------------------- /Test.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 8, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "%matplotlib inline" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 9, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/plain": [ 32 | "'0.20.3'" 33 | ] 34 | }, 35 | "execution_count": 9, 36 | "metadata": {}, 37 | "output_type": "execute_result" 38 | } 39 | ], 40 | "source": [ 41 | "pd.__version__" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": null, 47 | "metadata": { 48 | "collapsed": true 49 | }, 50 | "outputs": [], 51 | "source": [] 52 | } 53 | ], 54 | "metadata": { 55 | "kernelspec": { 56 | "display_name": "Python 3", 57 | "language": "python", 58 | "name": "python3" 59 | }, 60 | "language_info": { 61 | "codemirror_mode": { 62 | "name": "ipython", 63 | "version": 3 64 | }, 65 | "file_extension": ".py", 66 | "mimetype": "text/x-python", 67 | "name": "python", 68 | "nbconvert_exporter": "python", 69 | "pygments_lexer": "ipython3", 70 | "version": "3.6.4" 71 | } 72 | }, 73 | "nbformat": 4, 74 | "nbformat_minor": 2 75 | } 76 | -------------------------------------------------------------------------------- /Untitled.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 8, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "fruit = ['apple','orange','mango','strawberry','blueberry']\n", 11 | "weekdays = ['Sun','Mon','Tue','Wed','Thur']\n" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 9, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "data": { 21 | "text/plain": [ 22 | "Sun apple\n", 23 | "Mon orange\n", 24 | "Tue mango\n", 25 | "Wed strawberry\n", 26 | "Thur blueberry\n", 27 | "dtype: object" 28 | ] 29 | }, 30 | "execution_count": 9, 31 | "metadata": {}, 32 | "output_type": "execute_result" 33 | } 34 | ], 35 | "source": [ 36 | "pd.Series(fruit,weekdays)" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": { 43 | "collapsed": true 44 | }, 45 | "outputs": [], 46 | "source": [] 47 | } 48 | ], 49 | "metadata": { 50 | "kernelspec": { 51 | "display_name": "Python 3", 52 | "language": "python", 53 | "name": "python3" 54 | }, 55 | "language_info": { 56 | "codemirror_mode": { 57 | "name": "ipython", 58 | "version": 3 59 | }, 60 | "file_extension": ".py", 61 | "mimetype": "text/x-python", 62 | "name": "python", 63 | "nbconvert_exporter": "python", 64 | "pygments_lexer": "ipython3", 65 | "version": "3.6.4" 66 | } 67 | }, 68 | "nbformat": 4, 69 | "nbformat_minor": 2 70 | } 71 | -------------------------------------------------------------------------------- /bigmac.csv: -------------------------------------------------------------------------------- 1 | Date,Country,Price in US Dollars 2 | 1/2016,Argentina,2.39 3 | 1/2016,Australia,3.74 4 | 1/2016,Brazil,3.35 5 | 1/2016,Britain,4.22 6 | 1/2016,Canada,4.14 7 | 1/2016,Chile,2.94 8 | 1/2016,China,2.68 9 | 1/2016,Colombia,2.43 10 | 1/2016,Costa Rica,4.02 11 | 1/2016,Czech Republic,2.98 12 | 1/2016,Denmark,4.32 13 | 1/2016,Egypt,2.16 14 | 1/2016,Euro area,4.0 15 | 1/2016,Hong Kong,2.48 16 | 1/2016,Hungary,3.08 17 | 1/2016,India,1.9 18 | 1/2016,Indonesia,2.19 19 | 1/2016,Israel,4.29 20 | 1/2016,Japan,3.12 21 | 1/2016,Malaysia,1.82 22 | 1/2016,Mexico,2.81 23 | 1/2016,New Zealand,3.91 24 | 1/2016,Norway,5.21 25 | 1/2016,Pakistan,2.86 26 | 1/2016,Peru,2.93 27 | 1/2016,Philippines,2.79 28 | 1/2016,Poland,2.37 29 | 1/2016,Russia,1.53 30 | 1/2016,Saudi Arabia,3.2 31 | 1/2016,Singapore,3.27 32 | 1/2016,South Africa,1.77 33 | 1/2016,South Korea,3.59 34 | 1/2016,Sri Lanka,2.43 35 | 1/2016,Sweden,5.23 36 | 1/2016,Switzerland,6.44 37 | 1/2016,Taiwan,2.08 38 | 1/2016,Thailand,3.09 39 | 1/2016,Turkey,3.41 40 | 1/2016,UAE,3.54 41 | 1/2016,Ukraine,1.54 42 | 1/2016,United States,4.93 43 | 1/2016,Uruguay,3.74 44 | 1/2016,Venezuela,0.66 45 | 1/2016,Vietnam,2.67 46 | 1/2016,Austria,3.76 47 | 1/2016,Belgium,4.25 48 | 1/2016,Estonia,3.23 49 | 1/2016,Finland,4.41 50 | 1/2016,France,4.41 51 | 1/2016,Germany,3.86 52 | 1/2016,Greece,3.6 53 | 1/2016,Ireland,4.25 54 | 1/2016,Italy,4.3 55 | 1/2016,Netherlands,3.71 56 | 1/2016,Portugal,3.23 57 | 1/2016,Spain,3.76 58 | 7/2015,Argentina,3.07 59 | 7/2015,Australia,3.92 60 | 7/2015,Brazil,4.28 61 | 7/2015,Britain,4.51 62 | 7/2015,Canada,4.54 63 | 7/2015,Chile,3.27 64 | 7/2015,China,2.74 65 | 7/2015,Colombia,2.92 66 | 7/2015,Costa Rica,4.03 67 | 7/2015,Czech Republic,2.83 68 | 7/2015,Denmark,5.08 69 | 7/2015,Egypt,2.16 70 | 7/2015,Euro area,4.05 71 | 7/2015,Hong Kong,2.48 72 | 7/2015,Hungary,3.18 73 | 7/2015,India,1.83 74 | 7/2015,Indonesia,2.29 75 | 7/2015,Israel,4.63 76 | 7/2015,Japan,2.99 77 | 7/2015,Malaysia,2.01 78 | 7/2015,Mexico,3.11 79 | 7/2015,New Zealand,3.91 80 | 7/2015,Norway,5.65 81 | 7/2015,Pakistan,3.44 82 | 7/2015,Peru,3.14 83 | 7/2015,Philippines,3.61 84 | 7/2015,Poland,2.54 85 | 7/2015,Russia,1.88 86 | 7/2015,Saudi Arabia,3.2 87 | 7/2015,Singapore,3.44 88 | 7/2015,South Africa,2.09 89 | 7/2015,South Korea,3.76 90 | 7/2015,Sri Lanka,2.61 91 | 7/2015,Sweden,5.13 92 | 7/2015,Switzerland,6.82 93 | 7/2015,Taiwan,2.55 94 | 7/2015,Thailand,3.17 95 | 7/2015,Turkey,3.87 96 | 7/2015,UAE,3.54 97 | 7/2015,Ukraine,1.55 98 | 7/2015,United States,4.79 99 | 7/2015,Uruguay,4.13 100 | 7/2015,Venezuela,0.67 101 | 7/2015,Vietnam,2.75 102 | 7/2015,Austria,3.71 103 | 7/2015,Belgium,4.05 104 | 7/2015,Estonia,3.23 105 | 7/2015,Finland,4.49 106 | 7/2015,France,4.49 107 | 7/2015,Germany,3.93 108 | 7/2015,Greece,3.34 109 | 7/2015,Ireland,4.05 110 | 7/2015,Italy,4.38 111 | 7/2015,Netherlands,3.78 112 | 7/2015,Portugal,3.29 113 | 7/2015,Spain,4.0 114 | 1/2015,Argentina,3.25 115 | 1/2015,Australia,4.32 116 | 1/2015,Brazil,5.21 117 | 1/2015,Britain,4.37 118 | 1/2015,Canada,4.64 119 | 1/2015,Chile,3.35 120 | 1/2015,China,2.77 121 | 1/2015,Colombia,3.34 122 | 1/2015,Costa Rica,4.01 123 | 1/2015,Czech Republic,2.92 124 | 1/2015,Denmark,5.38 125 | 1/2015,Egypt,2.3 126 | 1/2015,Euro area,4.26 127 | 1/2015,Hong Kong,2.43 128 | 1/2015,Hungary,3.17 129 | 1/2015,India,1.89 130 | 1/2015,Indonesia,2.24 131 | 1/2015,Israel,4.45 132 | 1/2015,Japan,3.14 133 | 1/2015,Malaysia,2.11 134 | 1/2015,Mexico,3.35 135 | 1/2015,New Zealand,4.49 136 | 1/2015,Norway,6.3 137 | 1/2015,Pakistan,2.98 138 | 1/2015,Peru,3.32 139 | 1/2015,Philippines,3.67 140 | 1/2015,Poland,2.48 141 | 1/2015,Russia,1.36 142 | 1/2015,Saudi Arabia,2.93 143 | 1/2015,Singapore,3.53 144 | 1/2015,South Africa,2.22 145 | 1/2015,South Korea,3.78 146 | 1/2015,Sri Lanka,2.65 147 | 1/2015,Sweden,4.97 148 | 1/2015,Switzerland,7.54 149 | 1/2015,Taiwan,2.51 150 | 1/2015,Thailand,3.04 151 | 1/2015,Turkey,3.96 152 | 1/2015,UAE,3.54 153 | 1/2015,Ukraine,1.2 154 | 1/2015,United States,4.79 155 | 1/2015,Uruguay,4.63 156 | 1/2015,Venezuela,2.53 157 | 1/2015,Vietnam,2.81 158 | 1/2015,Austria,3.93 159 | 1/2015,Belgium,4.29 160 | 1/2015,Estonia,3.36 161 | 1/2015,Finland,4.75 162 | 1/2015,France,4.52 163 | 1/2015,Germany,4.25 164 | 1/2015,Greece,3.53 165 | 1/2015,Ireland,4.04 166 | 1/2015,Italy,4.46 167 | 1/2015,Netherlands,4.0 168 | 1/2015,Portugal,3.48 169 | 1/2015,Spain,4.23 170 | 7/2014,Argentina,2.57 171 | 7/2014,Australia,4.81 172 | 7/2014,Brazil,5.86 173 | 7/2014,Britain,4.93 174 | 7/2014,Canada,5.25 175 | 7/2014,Chile,3.72 176 | 7/2014,China,2.73 177 | 7/2014,Colombia,4.65 178 | 7/2014,Costa Rica,4.0 179 | 7/2014,Czech Republic,3.46 180 | 7/2014,Denmark,5.15 181 | 7/2014,Egypt,2.37 182 | 7/2014,Euro area,4.95 183 | 7/2014,Hong Kong,2.43 184 | 7/2014,Hungary,3.77 185 | 7/2014,India,1.75 186 | 7/2014,Indonesia,2.43 187 | 7/2014,Israel,5.13 188 | 7/2014,Japan,3.64 189 | 7/2014,Lithuania,3.49 190 | 7/2014,Malaysia,2.41 191 | 7/2014,Mexico,3.25 192 | 7/2014,New Zealand,4.94 193 | 7/2014,Norway,7.76 194 | 7/2014,Pakistan,3.04 195 | 7/2014,Peru,3.59 196 | 7/2014,Philippines,3.7 197 | 7/2014,Poland,3.0 198 | 7/2014,Russia,2.55 199 | 7/2014,Saudi Arabia,2.93 200 | 7/2014,Singapore,3.8 201 | 7/2014,South Africa,2.33 202 | 7/2014,South Korea,4.0 203 | 7/2014,Sri Lanka,2.69 204 | 7/2014,Sweden,5.95 205 | 7/2014,Switzerland,6.83 206 | 7/2014,Taiwan,2.63 207 | 7/2014,Thailand,3.12 208 | 7/2014,Turkey,4.42 209 | 7/2014,UAE,3.54 210 | 7/2014,Ukraine,1.63 211 | 7/2014,United States,4.8 212 | 7/2014,Uruguay,4.92 213 | 7/2014,Venezuela,6.82 214 | 7/2014,Vietnam,2.83 215 | 7/2014,Austria,4.56 216 | 7/2014,Belgium,4.98 217 | 7/2014,Estonia,3.9 218 | 7/2014,Finland,5.52 219 | 7/2014,France,5.25 220 | 7/2014,Germany,4.94 221 | 7/2014,Greece,4.11 222 | 7/2014,Ireland,4.7 223 | 7/2014,Italy,5.18 224 | 7/2014,Netherlands,4.64 225 | 7/2014,Portugal,4.04 226 | 7/2014,Spain,4.91 227 | 1/2014,Argentina,3.03 228 | 1/2014,Australia,4.47 229 | 1/2014,Brazil,5.25 230 | 1/2014,Britain,4.63 231 | 1/2014,Canada,5.01 232 | 1/2014,Chile,3.69 233 | 1/2014,China,2.74 234 | 1/2014,Colombia,4.34 235 | 1/2014,Costa Rica,4.28 236 | 1/2014,Czech Republic,3.47 237 | 1/2014,Denmark,5.18 238 | 1/2014,Egypt,2.43 239 | 1/2014,Euro area,4.96 240 | 1/2014,Hong Kong,2.32 241 | 1/2014,Hungary,3.85 242 | 1/2014,India,1.54 243 | 1/2014,Indonesia,2.3 244 | 1/2014,Israel,5.02 245 | 1/2014,Japan,2.97 246 | 1/2014,Lithuania,3.46 247 | 1/2014,Malaysia,2.23 248 | 1/2014,Mexico,2.78 249 | 1/2014,New Zealand,4.57 250 | 1/2014,Norway,7.8 251 | 1/2014,Pakistan,3.04 252 | 1/2014,Peru,3.56 253 | 1/2014,Philippines,2.98 254 | 1/2014,Poland,3.0 255 | 1/2014,Russia,2.62 256 | 1/2014,Saudi Arabia,2.93 257 | 1/2014,Singapore,3.6 258 | 1/2014,South Africa,2.16 259 | 1/2014,South Korea,3.47 260 | 1/2014,Sri Lanka,2.68 261 | 1/2014,Sweden,6.29 262 | 1/2014,Switzerland,7.14 263 | 1/2014,Taiwan,2.62 264 | 1/2014,Thailand,2.92 265 | 1/2014,Turkey,3.76 266 | 1/2014,UAE,3.27 267 | 1/2014,Ukraine,2.27 268 | 1/2014,United States,4.62 269 | 1/2014,Uruguay,4.91 270 | 1/2014,Venezuela,7.15 271 | 1/2014,Vietnam,2.84 272 | 1/2014,Austria,4.6 273 | 1/2014,Belgium,5.36 274 | 1/2014,Estonia,3.8 275 | 1/2014,Finland,5.56 276 | 1/2014,France,5.15 277 | 1/2014,Germany,4.98 278 | 1/2014,Greece,4.14 279 | 1/2014,Ireland,4.69 280 | 1/2014,Italy,5.22 281 | 1/2014,Netherlands,4.68 282 | 1/2014,Portugal,4.07 283 | 1/2014,Spain,4.95 284 | 7/2013,Argentina,3.88 285 | 7/2013,Australia,4.62 286 | 7/2013,Brazil,5.28 287 | 7/2013,Britain,4.02 288 | 7/2013,Canada,5.26 289 | 7/2013,Chile,3.94 290 | 7/2013,China,2.61 291 | 7/2013,Colombia,4.48 292 | 7/2013,Costa Rica,4.31 293 | 7/2013,Czech Republic,3.49 294 | 7/2013,Denmark,4.91 295 | 7/2013,Egypt,2.39 296 | 7/2013,Euro area,4.66 297 | 7/2013,Hong Kong,2.19 298 | 7/2013,Hungary,3.76 299 | 7/2013,India,1.5 300 | 7/2013,Indonesia,2.8 301 | 7/2013,Israel,4.8 302 | 7/2013,Japan,3.2 303 | 7/2013,Latvia,3.09 304 | 7/2013,Lithuania,3.2 305 | 7/2013,Malaysia,2.3 306 | 7/2013,Mexico,2.86 307 | 7/2013,New Zealand,4.3 308 | 7/2013,Norway,7.51 309 | 7/2013,Pakistan,3.0 310 | 7/2013,Peru,3.59 311 | 7/2013,Philippines,2.65 312 | 7/2013,Poland,2.73 313 | 7/2013,Russia,2.64 314 | 7/2013,Saudi Arabia,2.67 315 | 7/2013,Singapore,3.69 316 | 7/2013,South Africa,2.24 317 | 7/2013,South Korea,3.43 318 | 7/2013,Sri Lanka,2.83 319 | 7/2013,Sweden,6.16 320 | 7/2013,Switzerland,6.72 321 | 7/2013,Taiwan,2.63 322 | 7/2013,Thailand,2.85 323 | 7/2013,Turkey,4.34 324 | 7/2013,UAE,3.27 325 | 7/2013,Ukraine,2.33 326 | 7/2013,United States,4.56 327 | 7/2013,Uruguay,4.98 328 | 7/2013,Venezuela,7.15 329 | 7/2013,Austria,4.36 330 | 7/2013,Belgium,4.76 331 | 7/2013,Estonia,3.54 332 | 7/2013,Finland,5.27 333 | 7/2013,France,5.01 334 | 7/2013,Germany,4.68 335 | 7/2013,Greece,3.34 336 | 7/2013,Ireland,4.45 337 | 7/2013,Italy,4.82 338 | 7/2013,Netherlands,4.44 339 | 7/2013,Portugal,3.79 340 | 7/2013,Spain,4.5 341 | 1/2013,Argentina,3.82 342 | 1/2013,Australia,4.9 343 | 1/2013,Brazil,5.64 344 | 1/2013,Britain,4.25 345 | 1/2013,Canada,5.39 346 | 1/2013,Chile,4.35 347 | 1/2013,China,2.57 348 | 1/2013,Colombia,4.85 349 | 1/2013,Costa Rica,4.39 350 | 1/2013,Czech Republic,3.72 351 | 1/2013,Denmark,5.18 352 | 1/2013,Egypt,2.39 353 | 1/2013,Euro area,4.88 354 | 1/2013,Hong Kong,2.19 355 | 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Africa,2.36 430 | 7/2012,South Korea,3.21 431 | 7/2012,Sri Lanka,2.21 432 | 7/2012,Sweden,5.73 433 | 7/2012,Switzerland,6.56 434 | 7/2012,Taiwan,2.48 435 | 7/2012,Thailand,2.59 436 | 7/2012,Turkey,4.52 437 | 7/2012,UAE,3.27 438 | 7/2012,Ukraine,1.86 439 | 7/2012,United States,4.33 440 | 7/2012,Uruguay,4.53 441 | 7/2012,Venezuela,7.92 442 | 7/2012,Austria,3.87 443 | 7/2012,Belgium,4.61 444 | 7/2012,Estonia,2.47 445 | 7/2012,Finland,4.55 446 | 7/2012,France,4.36 447 | 7/2012,Germany,4.41 448 | 7/2012,Greece,3.25 449 | 7/2012,Ireland,4.23 450 | 7/2012,Italy,4.36 451 | 7/2012,Netherlands,4.1 452 | 7/2012,Portugal,6.0 453 | 7/2012,Spain,4.24 454 | 1/2012,Argentina,4.64 455 | 1/2012,Australia,4.94 456 | 1/2012,Brazil,5.68 457 | 1/2012,Britain,3.82 458 | 1/2012,Canada,4.63 459 | 1/2012,Chile,4.05 460 | 1/2012,China,2.44 461 | 1/2012,Colombia,4.54 462 | 1/2012,Costa Rica,4.02 463 | 1/2012,Czech Republic,3.45 464 | 1/2012,Denmark,5.37 465 | 1/2012,Egypt,2.57 466 | 1/2012,Euro area,4.43 467 | 1/2012,Hong Kong,2.12 468 | 1/2012,Hungary,2.63 469 | 1/2012,India,1.62 470 | 1/2012,Indonesia,2.46 471 | 1/2012,Israel,4.13 472 | 1/2012,Japan,4.16 473 | 1/2012,Latvia,3.0 474 | 1/2012,Lithuania,2.87 475 | 1/2012,Malaysia,2.34 476 | 1/2012,Mexico,2.7 477 | 1/2012,New Zealand,4.05 478 | 1/2012,Norway,6.79 479 | 1/2012,Pakistan,2.89 480 | 1/2012,Peru,3.71 481 | 1/2012,Philippines,2.68 482 | 1/2012,Poland,2.58 483 | 1/2012,Russia,2.55 484 | 1/2012,Saudi Arabia,2.67 485 | 1/2012,Singapore,3.75 486 | 1/2012,South Africa,2.45 487 | 1/2012,South Korea,3.19 488 | 1/2012,Sri Lanka,2.55 489 | 1/2012,Sweden,5.91 490 | 1/2012,Switzerland,6.81 491 | 1/2012,Taiwan,2.5 492 | 1/2012,Thailand,2.46 493 | 1/2012,Turkey,3.54 494 | 1/2012,UAE,3.27 495 | 1/2012,Ukraine,2.11 496 | 1/2012,United States,4.2 497 | 1/2012,Uruguay,4.63 498 | 1/2012,Venezuela,6.99 499 | 1/2012,Austria,3.92 500 | 1/2012,Belgium,4.69 501 | 1/2012,Estonia,2.59 502 | 1/2012,Finland,4.76 503 | 1/2012,France,4.57 504 | 1/2012,Germany,4.48 505 | 1/2012,Greece,4.19 506 | 1/2012,Ireland,4.82 507 | 1/2012,Italy,4.44 508 | 1/2012,Netherlands,4.12 509 | 1/2012,Portugal,3.68 510 | 1/2012,Spain,4.44 511 | 7/2011,Argentina,4.84 512 | 7/2011,Australia,4.94 513 | 7/2011,Brazil,6.16 514 | 7/2011,Britain,3.89 515 | 7/2011,Canada,5.0 516 | 7/2011,Chile,4.0 517 | 7/2011,China,2.27 518 | 7/2011,Colombia,4.74 519 | 7/2011,Costa Rica,4.07 520 | 7/2011,Czech Republic,4.07 521 | 7/2011,Denmark,5.48 522 | 7/2011,Egypt,2.36 523 | 7/2011,Euro area,4.93 524 | 7/2011,Hong Kong,1.94 525 | 7/2011,Hungary,4.04 526 | 7/2011,India,1.89 527 | 7/2011,Indonesia,2.64 528 | 7/2011,Israel,4.67 529 | 7/2011,Japan,4.08 530 | 7/2011,Latvia,3.23 531 | 7/2011,Lithuania,3.03 532 | 7/2011,Malaysia,2.42 533 | 7/2011,Mexico,2.74 534 | 7/2011,New Zealand,4.41 535 | 7/2011,Norway,8.31 536 | 7/2011,Pakistan,2.38 537 | 7/2011,Peru,3.65 538 | 7/2011,Philippines,2.78 539 | 7/2011,Poland,3.09 540 | 7/2011,Russia,2.7 541 | 7/2011,Saudi Arabia,2.67 542 | 7/2011,Singapore,3.65 543 | 7/2011,South Africa,2.87 544 | 7/2011,South Korea,3.5 545 | 7/2011,Sri Lanka,2.56 546 | 7/2011,Sweden,7.64 547 | 7/2011,Switzerland,8.06 548 | 7/2011,Taiwan,2.6 549 | 7/2011,Thailand,2.35 550 | 7/2011,Turkey,3.77 551 | 7/2011,UAE,3.27 552 | 7/2011,Ukraine,2.06 553 | 7/2011,United States,4.07 554 | 7/2011,Uruguay,4.88 555 | 7/2011,Venezuela,6.52 556 | 7/2011,Austria,4.43 557 | 7/2011,Belgium,5.38 558 | 7/2011,Estonia,3.15 559 | 7/2011,Finland,5.38 560 | 7/2011,France,5.02 561 | 7/2011,Germany,4.87 562 | 7/2011,Greece,4.67 563 | 7/2011,Ireland,5.45 564 | 7/2011,Italy,5.02 565 | 7/2011,Netherlands,4.66 566 | 7/2011,Portugal,4.16 567 | 7/2011,Spain,5.02 568 | 7/2010,Argentina,3.56 569 | 7/2010,Australia,3.84 570 | 7/2010,Brazil,4.91 571 | 7/2010,Britain,3.48 572 | 7/2010,Canada,4.0 573 | 7/2010,Chile,3.34 574 | 7/2010,China,1.95 575 | 7/2010,Colombia,4.39 576 | 7/2010,Costa Rica,3.83 577 | 7/2010,Czech Republic,3.43 578 | 7/2010,Denmark,4.9 579 | 7/2010,Egypt,2.28 580 | 7/2010,Euro area,4.33 581 | 7/2010,Hong Kong,1.9 582 | 7/2010,Hungary,3.33 583 | 7/2010,Indonesia,2.51 584 | 7/2010,Israel,3.86 585 | 7/2010,Japan,3.67 586 | 7/2010,Latvia,2.8 587 | 7/2010,Lithuania,2.71 588 | 7/2010,Malaysia,2.19 589 | 7/2010,Mexico,2.5 590 | 7/2010,New Zealand,3.59 591 | 7/2010,Norway,7.2 592 | 7/2010,Pakistan,2.46 593 | 7/2010,Peru,3.54 594 | 7/2010,Philippines,2.19 595 | 7/2010,Poland,2.6 596 | 7/2010,Russia,2.33 597 | 7/2010,Saudi Arabia,2.67 598 | 7/2010,Singapore,3.08 599 | 7/2010,South Africa,2.45 600 | 7/2010,South Korea,2.82 601 | 7/2010,Sri Lanka,1.86 602 | 7/2010,Sweden,6.56 603 | 7/2010,Switzerland,6.19 604 | 7/2010,Taiwan,2.34 605 | 7/2010,Thailand,2.17 606 | 7/2010,Turkey,3.89 607 | 7/2010,UAE,2.99 608 | 7/2010,Ukraine,1.84 609 | 7/2010,United States,3.73 610 | 7/2010,Uruguay,3.74 611 | 1/2010,Argentina,1.84 612 | 1/2010,Australia,3.98 613 | 1/2010,Brazil,4.76 614 | 1/2010,Britain,3.67 615 | 1/2010,Canada,3.97 616 | 1/2010,Chile,3.18 617 | 1/2010,China,1.83 618 | 1/2010,Colombia,3.91 619 | 1/2010,Costa Rica,3.52 620 | 1/2010,Czech Republic,3.71 621 | 1/2010,Denmark,5.99 622 | 1/2010,Egypt,2.38 623 | 1/2010,Euro area,4.84 624 | 1/2010,Hong Kong,1.91 625 | 1/2010,Hungary,3.86 626 | 1/2010,Indonesia,2.24 627 | 1/2010,Israel,3.99 628 | 1/2010,Japan,3.5 629 | 1/2010,Latvia,3.09 630 | 1/2010,Lithuania,2.87 631 | 1/2010,Malaysia,2.08 632 | 1/2010,Mexico,2.5 633 | 1/2010,New Zealand,3.61 634 | 1/2010,Norway,7.02 635 | 1/2010,Pakistan,2.42 636 | 1/2010,Peru,2.81 637 | 1/2010,Philippines,2.21 638 | 1/2010,Poland,2.86 639 | 1/2010,Russia,2.34 640 | 1/2010,Saudi Arabia,2.67 641 | 1/2010,Singapore,3.19 642 | 1/2010,South Africa,2.46 643 | 1/2010,South Korea,2.98 644 | 1/2010,Sri Lanka,1.83 645 | 1/2010,Sweden,5.51 646 | 1/2010,Switzerland,6.3 647 | 1/2010,Taiwan,2.36 648 | 1/2010,Thailand,2.11 649 | 1/2010,Turkey,3.83 650 | 1/2010,UAE,2.99 651 | 1/2010,Ukraine,1.83 652 | 1/2010,United States,3.58 653 | 1/2010,Uruguay,3.32 654 | -------------------------------------------------------------------------------- /crime_india.csv: -------------------------------------------------------------------------------- 1 | S. No. (Col.1),Category (Col.2),State/UT (Col.3),Year - 2014 (Col.4),Year - 2015 (Col.5),Year - 2016 (Col.6),Percentage Share of State/UT (2016) (Col.7),Rank Based on Incidence/ Percentage share (2016) (Col.8),Mid-Year Projected Population (In Lakhs) (2016)+ (Col.9),Rate of Cognizable Crimes (IPC) (2016)++ (Col.10),Rank Based on Crime Rate (2016) (Col.11) 2 | 1,State,Andhra Pradesh,114604,110693,106774,3.6,13.0,517.4,206.4,15.0 3 | 2,State,Arunachal Pradesh,2843,2968,2534,0.1,29.0,13.2,192.3,17.0 4 | 3,State,Assam,94337,103616,102250,3.4,14.0,325.8,313.9,5.0 5 | 4,State,Bihar,177595,176973,164163,5.5,9.0,1043.0,157.4,22.0 6 | 5,State,Chhattisgarh,58200,56692,55029,1.8,17.0,259.9,211.7,14.0 7 | 6,State,Goa,4466,3074,2692,0.1,28.0,19.9,135.6,25.0 8 | 7,State,Gujarat,131385,126935,147122,4.9,11.0,630.8,233.2,11.0 9 | 8,State,Haryana,79947,84466,88527,3.0,15.0,276.1,320.6,4.0 10 | 9,State,Himachal Pradesh,14160,14007,13386,0.4,21.0,71.2,188.1,20.0 11 | 10,State,Jammu & Kashmir,23848,23583,24501,0.8,20.0,124.6,196.6,16.0 12 | 11,State,Jharkhand,45335,45050,40710,1.4,18.0,338.0,120.4,30.0 13 | 12,State,Karnataka,137338,138847,148402,5.0,10.0,625.7,237.2,10.0 14 | 13,State,Kerala,206789,257074,260097,8.7,4.0,357.5,727.6,2.0 15 | 14,State,Madhya Pradesh,272423,268614,264418,8.9,2.0,782.6,337.9,3.0 16 | 15,State,Maharashtra,249834,275414,261714,8.8,3.0,1205.5,217.1,13.0 17 | 16,State,Manipur,3641,3847,3170,0.1,26.0,26.0,121.9,28.0 18 | 17,State,Meghalaya,3679,4079,3366,0.1,25.0,27.8,120.9,29.0 19 | 18,State,Mizoram,2140,2228,2425,0.1,30.0,10.7,227.3,12.0 20 | 19,State,Nagaland,1157,1302,1376,0.0,31.0,23.9,57.6,34.0 21 | 20,State,Odisha,74569,83360,81460,2.7,16.0,425.9,191.3,18.0 22 | 21,State,Punjab,37162,37983,40007,1.3,19.0,292.0,137.0,24.0 23 | 22,State,Rajasthan,210418,198080,180398,6.1,6.0,732.8,246.2,8.0 24 | 23,State,Sikkim,1065,766,809,0.0,32.0,6.5,124.7,27.0 25 | 24,State,Tamil Nadu,193200,187558,179896,6.0,7.0,695.2,258.8,7.0 26 | 25,State,Telangana,106830,106282,108991,3.7,12.0,368.5,295.7,6.0 27 | 26,State,Tripura,5499,4692,3933,0.1,24.0,38.4,102.4,31.0 28 | 27,State,Uttar Pradesh,240475,241920,282171,9.5,1.0,2192.4,128.7,26.0 29 | 28,State,Uttarakhand,9156,10248,10867,0.4,22.0,106.8,101.8,32.0 30 | 29,State,West Bengal,185672,179501,176569,5.9,8.0,938.3,188.2,19.0 31 | Total (States),State,Total (States),2687767,2749852,2757757,92.7,,12476.2,221.0, 32 | 30,UT,A & N Island,746,862,802,0.0,33.0,5.5,144.8,23.0 33 | 31,UT,Chandigarh,3221,3248,2996,0.1,27.0,18.0,166.4,21.0 34 | 32,UT,Dadra & Nagar Haveli,277,269,244,0.0,35.0,4.3,57.4,35.0 35 | 33,UT,Daman & Diu,233,302,271,0.0,34.0,3.3,81.1,33.0 36 | 34,UT,Delhi,155654,191377,209519,7.0,5.0,214.9,974.9,1.0 37 | 35,UT,Lakshadweep,81,50,36,0.0,36.0,0.8,43.9,36.0 38 | 36,UT,Puducherry,3584,3440,4086,0.1,23.0,16.8,242.8,9.0 39 | Total (UTs),UT,Total (UTs),163796,199548,217954,7.3,,263.7,826.5, 40 | -------------------------------------------------------------------------------- /ecommerce.csv: -------------------------------------------------------------------------------- 1 | ID,order_date,delivery_date 2 | 1,5/24/98,2/5/99 3 | 2,4/22/92,3/6/98 4 | 4,2/10/91,8/26/92 5 | 5,7/21/92,11/20/97 6 | 7,9/2/93,6/10/98 7 | 8,6/10/93,11/11/93 8 | 9,1/25/90,10/2/94 9 | 10,2/23/92,12/30/98 10 | 11,7/12/96,7/14/97 11 | 18,6/18/95,10/13/97 12 | 19,5/10/98,5/19/98 13 | 20,10/17/92,10/6/98 14 | 23,5/30/92,8/15/99 15 | 26,4/11/96,5/4/98 16 | 30,10/22/98,1/11/99 17 | 32,1/20/90,7/24/98 18 | 33,9/21/94,10/12/96 19 | 35,9/10/93,4/28/96 20 | 36,5/15/90,2/14/94 21 | 39,3/26/90,1/25/93 22 | 41,2/6/92,5/10/96 23 | 46,9/5/95,7/19/96 24 | 50,5/3/91,7/17/99 25 | 52,9/2/94,5/14/97 26 | 53,11/29/95,6/23/98 27 | 54,8/7/96,4/4/98 28 | 58,2/11/95,4/16/95 29 | 59,9/29/95,12/16/95 30 | 60,2/14/93,8/6/95 31 | 63,12/22/90,9/2/95 32 | 64,11/25/90,5/14/98 33 | 66,9/8/92,12/29/98 34 | 67,5/4/90,2/4/93 35 | 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| 331,9/18/90,12/19/99 155 | 332,3/24/91,7/31/94 156 | 334,10/8/92,4/17/98 157 | 335,12/9/92,7/28/94 158 | 336,4/16/95,4/2/98 159 | 337,8/16/90,2/10/91 160 | 338,8/14/97,10/3/99 161 | 340,9/26/90,1/7/96 162 | 341,8/19/96,2/13/97 163 | 344,12/24/96,2/14/97 164 | 346,4/27/98,6/3/99 165 | 347,6/18/91,10/19/91 166 | 348,2/27/90,1/4/99 167 | 349,10/31/91,7/5/99 168 | 350,10/29/95,9/13/96 169 | 351,4/12/95,5/27/98 170 | 353,12/5/92,5/5/94 171 | 354,5/28/91,11/15/95 172 | 359,12/24/90,7/4/94 173 | 363,7/17/91,6/22/93 174 | 364,12/5/90,5/10/94 175 | 365,6/29/94,12/2/97 176 | 369,10/26/92,12/9/96 177 | 370,6/11/91,6/24/99 178 | 373,2/6/95,9/16/96 179 | 377,11/4/94,4/8/96 180 | 378,12/20/90,5/7/92 181 | 379,4/3/91,10/12/91 182 | 380,10/9/91,2/22/92 183 | 382,8/6/92,12/4/95 184 | 383,5/10/99,11/28/99 185 | 386,3/21/90,8/18/90 186 | 387,4/23/93,5/10/96 187 | 390,7/18/90,6/28/96 188 | 391,8/19/90,4/19/91 189 | 392,12/24/90,12/4/99 190 | 393,5/6/93,11/15/93 191 | 397,1/24/98,8/13/99 192 | 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986,12/10/90,12/16/92 498 | 990,6/24/91,2/2/96 499 | 991,9/9/91,3/30/98 500 | 993,11/16/90,4/27/98 501 | 994,6/3/93,6/13/93 502 | 997,1/4/90,10/3/91 -------------------------------------------------------------------------------- /jamesbond.csv: -------------------------------------------------------------------------------- 1 | Film,Year,Actor,Director,Box Office,Budget,Bond Actor Salary 2 | Dr. No,1962,Sean Connery,Terence Young,448.8,7,0.6 3 | From Russia with Love,1963,Sean Connery,Terence Young,543.8,12.6,1.6 4 | Goldfinger,1964,Sean Connery,Guy Hamilton,820.4,18.6,3.2 5 | Thunderball,1965,Sean Connery,Terence Young,848.1,41.9,4.7 6 | Casino Royale,1967,David Niven,Ken Hughes,315,85, 7 | You Only Live Twice,1967,Sean Connery,Lewis Gilbert,514.2,59.9,4.4 8 | On Her Majesty's Secret Service,1969,George Lazenby,Peter R. Hunt,291.5,37.3,0.6 9 | Diamonds Are Forever,1971,Sean Connery,Guy Hamilton,442.5,34.7,5.8 10 | Live and Let Die,1973,Roger Moore,Guy Hamilton,460.3,30.8, 11 | The Man with the Golden Gun,1974,Roger Moore,Guy Hamilton,334,27.7, 12 | The Spy Who Loved Me,1977,Roger Moore,Lewis Gilbert,533,45.1, 13 | Moonraker,1979,Roger Moore,Lewis Gilbert,535,91.5, 14 | For Your Eyes Only,1981,Roger Moore,John Glen,449.4,60.2, 15 | Never Say Never Again,1983,Sean Connery,Irvin Kershner,380,86, 16 | Octopussy,1983,Roger Moore,John Glen,373.8,53.9,7.8 17 | A View to a Kill,1985,Roger Moore,John Glen,275.2,54.5,9.1 18 | The Living Daylights,1987,Timothy Dalton,John Glen,313.5,68.8,5.2 19 | Licence to Kill,1989,Timothy Dalton,John Glen,250.9,56.7,7.9 20 | GoldenEye,1995,Pierce Brosnan,Martin Campbell,518.5,76.9,5.1 21 | Tomorrow Never Dies,1997,Pierce Brosnan,Roger Spottiswoode,463.2,133.9,10 22 | The World Is Not Enough,1999,Pierce Brosnan,Michael Apted,439.5,158.3,13.5 23 | Die Another Day,2002,Pierce Brosnan,Lee Tamahori,465.4,154.2,17.9 24 | Casino Royale,2006,Daniel Craig,Martin Campbell,581.5,145.3,3.3 25 | Quantum of Solace,2008,Daniel Craig,Marc Forster,514.2,181.4,8.1 26 | Skyfall,2012,Daniel Craig,Sam Mendes,943.5,170.2,14.5 27 | Spectre,2015,Daniel Craig,Sam Mendes,726.7,206.3, -------------------------------------------------------------------------------- /nba.csv: -------------------------------------------------------------------------------- 1 | Name,Team,Number,Position,Age,Height,Weight,College,Salary 2 | Avery Bradley,Boston Celtics,0.0,PG,25.0,6-2,180.0,Texas,7730337.0 3 | Jae Crowder,Boston Celtics,99.0,SF,25.0,6-6,235.0,Marquette,6796117.0 4 | John Holland,Boston Celtics,30.0,SG,27.0,6-5,205.0,Boston University, 5 | R.J. Hunter,Boston Celtics,28.0,SG,22.0,6-5,185.0,Georgia State,1148640.0 6 | Jonas Jerebko,Boston Celtics,8.0,PF,29.0,6-10,231.0,,5000000.0 7 | Amir Johnson,Boston Celtics,90.0,PF,29.0,6-9,240.0,,12000000.0 8 | Jordan Mickey,Boston Celtics,55.0,PF,21.0,6-8,235.0,LSU,1170960.0 9 | Kelly Olynyk,Boston Celtics,41.0,C,25.0,7-0,238.0,Gonzaga,2165160.0 10 | Terry Rozier,Boston Celtics,12.0,PG,22.0,6-2,190.0,Louisville,1824360.0 11 | Marcus Smart,Boston Celtics,36.0,PG,22.0,6-4,220.0,Oklahoma State,3431040.0 12 | Jared Sullinger,Boston Celtics,7.0,C,24.0,6-9,260.0,Ohio State,2569260.0 13 | Isaiah Thomas,Boston Celtics,4.0,PG,27.0,5-9,185.0,Washington,6912869.0 14 | Evan Turner,Boston Celtics,11.0,SG,27.0,6-7,220.0,Ohio State,3425510.0 15 | James Young,Boston Celtics,13.0,SG,20.0,6-6,215.0,Kentucky,1749840.0 16 | Tyler Zeller,Boston Celtics,44.0,C,26.0,7-0,253.0,North Carolina,2616975.0 17 | Bojan Bogdanovic,Brooklyn Nets,44.0,SG,27.0,6-8,216.0,,3425510.0 18 | Markel Brown,Brooklyn Nets,22.0,SG,24.0,6-3,190.0,Oklahoma State,845059.0 19 | Wayne Ellington,Brooklyn Nets,21.0,SG,28.0,6-4,200.0,North Carolina,1500000.0 20 | Rondae Hollis-Jefferson,Brooklyn Nets,24.0,SG,21.0,6-7,220.0,Arizona,1335480.0 21 | Jarrett Jack,Brooklyn Nets,2.0,PG,32.0,6-3,200.0,Georgia Tech,6300000.0 22 | Sergey Karasev,Brooklyn Nets,10.0,SG,22.0,6-7,208.0,,1599840.0 23 | Sean Kilpatrick,Brooklyn Nets,6.0,SG,26.0,6-4,219.0,Cincinnati,134215.0 24 | Shane Larkin,Brooklyn Nets,0.0,PG,23.0,5-11,175.0,Miami (FL),1500000.0 25 | Brook Lopez,Brooklyn Nets,11.0,C,28.0,7-0,275.0,Stanford,19689000.0 26 | Chris McCullough,Brooklyn Nets,1.0,PF,21.0,6-11,200.0,Syracuse,1140240.0 27 | Willie Reed,Brooklyn Nets,33.0,PF,26.0,6-10,220.0,Saint Louis,947276.0 28 | Thomas Robinson,Brooklyn Nets,41.0,PF,25.0,6-10,237.0,Kansas,981348.0 29 | Henry Sims,Brooklyn Nets,14.0,C,26.0,6-10,248.0,Georgetown,947276.0 30 | Donald Sloan,Brooklyn Nets,15.0,PG,28.0,6-3,205.0,Texas A&M,947276.0 31 | Thaddeus Young,Brooklyn Nets,30.0,PF,27.0,6-8,221.0,Georgia Tech,11235955.0 32 | Arron Afflalo,New York Knicks,4.0,SG,30.0,6-5,210.0,UCLA,8000000.0 33 | Lou Amundson,New York Knicks,17.0,PF,33.0,6-9,220.0,UNLV,1635476.0 34 | Thanasis Antetokounmpo,New York Knicks,43.0,SF,23.0,6-7,205.0,,30888.0 35 | Carmelo Anthony,New York Knicks,7.0,SF,32.0,6-8,240.0,Syracuse,22875000.0 36 | Jose Calderon,New York Knicks,3.0,PG,34.0,6-3,200.0,,7402812.0 37 | Cleanthony Early,New York Knicks,11.0,SF,25.0,6-8,210.0,Wichita State,845059.0 38 | Langston Galloway,New York Knicks,2.0,SG,24.0,6-2,200.0,Saint Joseph's,845059.0 39 | Jerian Grant,New York Knicks,13.0,PG,23.0,6-4,195.0,Notre Dame,1572360.0 40 | Robin Lopez,New York Knicks,8.0,C,28.0,7-0,255.0,Stanford,12650000.0 41 | Kyle O'Quinn,New York Knicks,9.0,PF,26.0,6-10,250.0,Norfolk State,3750000.0 42 | Kristaps Porzingis,New York Knicks,6.0,PF,20.0,7-3,240.0,,4131720.0 43 | Kevin Seraphin,New York Knicks,1.0,C,26.0,6-10,278.0,,2814000.0 44 | Lance Thomas,New York Knicks,42.0,SF,28.0,6-8,235.0,Duke,1636842.0 45 | Sasha Vujacic,New York Knicks,18.0,SG,32.0,6-7,195.0,,947276.0 46 | Derrick Williams,New York Knicks,23.0,PF,25.0,6-8,240.0,Arizona,4000000.0 47 | Tony Wroten,New York Knicks,5.0,SG,23.0,6-6,205.0,Washington,167406.0 48 | Elton Brand,Philadelphia 76ers,42.0,PF,37.0,6-9,254.0,Duke, 49 | Isaiah Canaan,Philadelphia 76ers,0.0,PG,25.0,6-0,201.0,Murray State,947276.0 50 | Robert Covington,Philadelphia 76ers,33.0,SF,25.0,6-9,215.0,Tennessee State,1000000.0 51 | Joel Embiid,Philadelphia 76ers,21.0,C,22.0,7-0,250.0,Kansas,4626960.0 52 | Jerami Grant,Philadelphia 76ers,39.0,SF,22.0,6-8,210.0,Syracuse,845059.0 53 | Richaun Holmes,Philadelphia 76ers,22.0,PF,22.0,6-10,245.0,Bowling Green,1074169.0 54 | Carl Landry,Philadelphia 76ers,7.0,PF,32.0,6-9,248.0,Purdue,6500000.0 55 | Kendall Marshall,Philadelphia 76ers,5.0,PG,24.0,6-4,200.0,North Carolina,2144772.0 56 | T.J. McConnell,Philadelphia 76ers,12.0,PG,24.0,6-2,200.0,Arizona,525093.0 57 | Nerlens Noel,Philadelphia 76ers,4.0,PF,22.0,6-11,228.0,Kentucky,3457800.0 58 | Jahlil Okafor,Philadelphia 76ers,8.0,C,20.0,6-11,275.0,Duke,4582680.0 59 | Ish Smith,Philadelphia 76ers,1.0,PG,27.0,6-0,175.0,Wake Forest,947276.0 60 | Nik Stauskas,Philadelphia 76ers,11.0,SG,22.0,6-6,205.0,Michigan,2869440.0 61 | Hollis Thompson,Philadelphia 76ers,31.0,SG,25.0,6-8,206.0,Georgetown,947276.0 62 | Christian Wood,Philadelphia 76ers,35.0,PF,20.0,6-11,220.0,UNLV,525093.0 63 | Bismack Biyombo,Toronto Raptors,8.0,C,23.0,6-9,245.0,,2814000.0 64 | Bruno Caboclo,Toronto Raptors,20.0,SF,20.0,6-9,205.0,,1524000.0 65 | DeMarre Carroll,Toronto Raptors,5.0,SF,29.0,6-8,212.0,Missouri,13600000.0 66 | DeMar DeRozan,Toronto Raptors,10.0,SG,26.0,6-7,220.0,USC,10050000.0 67 | James Johnson,Toronto Raptors,3.0,PF,29.0,6-9,250.0,Wake Forest,2500000.0 68 | Cory Joseph,Toronto Raptors,6.0,PG,24.0,6-3,190.0,Texas,7000000.0 69 | Kyle Lowry,Toronto Raptors,7.0,PG,30.0,6-0,205.0,Villanova,12000000.0 70 | Lucas Nogueira,Toronto Raptors,92.0,C,23.0,7-0,220.0,,1842000.0 71 | Patrick Patterson,Toronto Raptors,54.0,PF,27.0,6-9,235.0,Kentucky,6268675.0 72 | Norman Powell,Toronto Raptors,24.0,SG,23.0,6-4,215.0,UCLA,650000.0 73 | Terrence Ross,Toronto Raptors,31.0,SF,25.0,6-7,195.0,Washington,3553917.0 74 | Luis Scola,Toronto Raptors,4.0,PF,36.0,6-9,240.0,,2900000.0 75 | Jason Thompson,Toronto Raptors,1.0,PF,29.0,6-11,250.0,Rider,245177.0 76 | Jonas Valanciunas,Toronto Raptors,17.0,C,24.0,7-0,255.0,,4660482.0 77 | Delon Wright,Toronto Raptors,55.0,PG,24.0,6-5,190.0,Utah,1509360.0 78 | Leandro Barbosa,Golden State Warriors,19.0,SG,33.0,6-3,194.0,,2500000.0 79 | Harrison Barnes,Golden State Warriors,40.0,SF,24.0,6-8,225.0,North Carolina,3873398.0 80 | Andrew Bogut,Golden State Warriors,12.0,C,31.0,7-0,260.0,Utah,13800000.0 81 | Ian Clark,Golden State Warriors,21.0,SG,25.0,6-3,175.0,Belmont,947276.0 82 | Stephen Curry,Golden State Warriors,30.0,PG,28.0,6-3,190.0,Davidson,11370786.0 83 | Festus Ezeli,Golden State Warriors,31.0,C,26.0,6-11,265.0,Vanderbilt,2008748.0 84 | Draymond Green,Golden State Warriors,23.0,PF,26.0,6-7,230.0,Michigan State,14260870.0 85 | Andre Iguodala,Golden State Warriors,9.0,SF,32.0,6-6,215.0,Arizona,11710456.0 86 | Shaun Livingston,Golden State Warriors,34.0,PG,30.0,6-7,192.0,,5543725.0 87 | Kevon Looney,Golden State Warriors,36.0,SF,20.0,6-9,220.0,UCLA,1131960.0 88 | James Michael McAdoo,Golden State Warriors,20.0,SF,23.0,6-9,240.0,North Carolina,845059.0 89 | Brandon Rush,Golden State Warriors,4.0,SF,30.0,6-6,220.0,Kansas,1270964.0 90 | Marreese Speights,Golden State Warriors,5.0,C,28.0,6-10,255.0,Florida,3815000.0 91 | Klay Thompson,Golden State Warriors,11.0,SG,26.0,6-7,215.0,Washington State,15501000.0 92 | Anderson Varejao,Golden State Warriors,18.0,PF,33.0,6-11,273.0,,289755.0 93 | Cole Aldrich,Los Angeles Clippers,45.0,C,27.0,6-11,250.0,Kansas,1100602.0 94 | Jeff Ayres,Los Angeles Clippers,19.0,PF,29.0,6-9,250.0,Arizona State,111444.0 95 | Jamal Crawford,Los Angeles Clippers,11.0,SG,36.0,6-5,195.0,Michigan,5675000.0 96 | Branden Dawson,Los Angeles Clippers,22.0,SF,23.0,6-6,225.0,Michigan State,525093.0 97 | Jeff Green,Los Angeles Clippers,8.0,SF,29.0,6-9,235.0,Georgetown,9650000.0 98 | Blake Griffin,Los Angeles Clippers,32.0,PF,27.0,6-10,251.0,Oklahoma,18907726.0 99 | Wesley Johnson,Los Angeles Clippers,33.0,SF,28.0,6-7,215.0,Syracuse,1100602.0 100 | DeAndre Jordan,Los Angeles Clippers,6.0,C,27.0,6-11,265.0,Texas A&M,19689000.0 101 | Luc Richard Mbah a Moute,Los Angeles Clippers,12.0,PF,29.0,6-8,230.0,UCLA,947276.0 102 | Chris Paul,Los Angeles Clippers,3.0,PG,31.0,6-0,175.0,Wake Forest,21468695.0 103 | Paul Pierce,Los Angeles Clippers,34.0,SF,38.0,6-7,235.0,Kansas,3376000.0 104 | Pablo Prigioni,Los Angeles Clippers,9.0,PG,39.0,6-3,185.0,,947726.0 105 | JJ Redick,Los Angeles Clippers,4.0,SG,31.0,6-4,190.0,Duke,7085000.0 106 | Austin Rivers,Los Angeles Clippers,25.0,PG,23.0,6-4,200.0,Duke,3110796.0 107 | C.J. Wilcox,Los Angeles Clippers,30.0,SG,25.0,6-5,195.0,Washington,1159680.0 108 | Brandon Bass,Los Angeles Lakers,2.0,PF,31.0,6-8,250.0,LSU,3000000.0 109 | Tarik Black,Los Angeles Lakers,28.0,C,24.0,6-9,250.0,Kansas,845059.0 110 | Anthony Brown,Los Angeles Lakers,3.0,SF,23.0,6-7,210.0,Stanford,700000.0 111 | Kobe Bryant,Los Angeles Lakers,24.0,SF,37.0,6-6,212.0,,25000000.0 112 | Jordan Clarkson,Los Angeles Lakers,6.0,PG,24.0,6-5,194.0,Missouri,845059.0 113 | Roy Hibbert,Los Angeles Lakers,17.0,C,29.0,7-2,270.0,Georgetown,15592217.0 114 | Marcelo Huertas,Los Angeles Lakers,9.0,PG,33.0,6-3,200.0,,525093.0 115 | Ryan Kelly,Los Angeles Lakers,4.0,PF,25.0,6-11,230.0,Duke,1724250.0 116 | Larry Nance Jr.,Los Angeles Lakers,7.0,PF,23.0,6-9,230.0,Wyoming,1155600.0 117 | Julius Randle,Los Angeles Lakers,30.0,PF,21.0,6-9,250.0,Kentucky,3132240.0 118 | D'Angelo Russell,Los Angeles Lakers,1.0,PG,20.0,6-5,195.0,Ohio State,5103120.0 119 | Robert Sacre,Los Angeles Lakers,50.0,C,27.0,7-0,270.0,Gonzaga,981348.0 120 | Louis Williams,Los Angeles Lakers,23.0,SG,29.0,6-1,175.0,,7000000.0 121 | Metta World Peace,Los Angeles Lakers,37.0,SF,36.0,6-7,260.0,St. John's,947276.0 122 | Nick Young,Los Angeles Lakers,0.0,SF,31.0,6-7,210.0,USC,5219169.0 123 | Eric Bledsoe,Phoenix Suns,2.0,PG,26.0,6-1,190.0,Kentucky,13500000.0 124 | Devin Booker,Phoenix Suns,1.0,SG,19.0,6-6,206.0,Kentucky,2127840.0 125 | Chase Budinger,Phoenix Suns,10.0,SF,28.0,6-7,209.0,Arizona,206192.0 126 | Tyson Chandler,Phoenix Suns,4.0,C,33.0,7-1,240.0,,13000000.0 127 | Archie Goodwin,Phoenix Suns,20.0,SG,21.0,6-5,200.0,Kentucky,1160160.0 128 | John Jenkins,Phoenix Suns,23.0,SG,25.0,6-4,215.0,Vanderbilt,981348.0 129 | Brandon Knight,Phoenix Suns,3.0,PG,24.0,6-3,189.0,Kentucky,13500000.0 130 | Alex Len,Phoenix Suns,21.0,C,22.0,7-1,260.0,Maryland,3807120.0 131 | Jon Leuer,Phoenix Suns,30.0,PF,27.0,6-10,228.0,Wisconsin,1035000.0 132 | Phil Pressey,Phoenix Suns,25.0,PG,25.0,5-11,175.0,Missouri,55722.0 133 | Ronnie Price,Phoenix Suns,14.0,PG,32.0,6-2,190.0,Utah Valley,947276.0 134 | Mirza Teletovic,Phoenix Suns,35.0,PF,30.0,6-9,242.0,,5500000.0 135 | P.J. Tucker,Phoenix Suns,17.0,SF,31.0,6-6,245.0,Texas,5500000.0 136 | T.J. Warren,Phoenix Suns,12.0,SF,22.0,6-8,230.0,North Carolina State,2041080.0 137 | Alan Williams,Phoenix Suns,15.0,C,23.0,6-8,260.0,UC Santa Barbara,83397.0 138 | Quincy Acy,Sacramento Kings,13.0,SF,25.0,6-7,240.0,Baylor,981348.0 139 | James Anderson,Sacramento Kings,5.0,SG,27.0,6-6,213.0,Oklahoma State,1015421.0 140 | Marco Belinelli,Sacramento Kings,3.0,SG,30.0,6-5,210.0,,6060606.0 141 | Caron Butler,Sacramento Kings,31.0,SF,36.0,6-7,228.0,Connecticut,1449187.0 142 | Omri Casspi,Sacramento Kings,18.0,SF,27.0,6-9,225.0,,2836186.0 143 | Willie Cauley-Stein,Sacramento Kings,0.0,C,22.0,7-0,240.0,Kentucky,3398280.0 144 | Darren Collison,Sacramento Kings,7.0,PG,28.0,6-0,175.0,UCLA,5013559.0 145 | DeMarcus Cousins,Sacramento Kings,15.0,C,25.0,6-11,270.0,Kentucky,15851950.0 146 | Seth Curry,Sacramento Kings,30.0,SG,25.0,6-2,185.0,Duke,947276.0 147 | Duje Dukan,Sacramento Kings,26.0,PF,24.0,6-9,220.0,Wisconsin,525093.0 148 | Rudy Gay,Sacramento Kings,8.0,SF,29.0,6-8,230.0,Connecticut,12403101.0 149 | Kosta Koufos,Sacramento Kings,41.0,C,27.0,7-0,265.0,Ohio State,7700000.0 150 | Ben McLemore,Sacramento Kings,23.0,SG,23.0,6-5,195.0,Kansas,3156600.0 151 | Eric Moreland,Sacramento Kings,25.0,PF,24.0,6-10,238.0,Oregon State,845059.0 152 | Rajon Rondo,Sacramento Kings,9.0,PG,30.0,6-1,186.0,Kentucky,9500000.0 153 | Cameron Bairstow,Chicago Bulls,41.0,PF,25.0,6-9,250.0,New Mexico,845059.0 154 | Aaron Brooks,Chicago Bulls,0.0,PG,31.0,6-0,161.0,Oregon,2250000.0 155 | Jimmy Butler,Chicago Bulls,21.0,SG,26.0,6-7,220.0,Marquette,16407500.0 156 | Mike Dunleavy,Chicago Bulls,34.0,SG,35.0,6-9,230.0,Duke,4500000.0 157 | Cristiano Felicio,Chicago Bulls,6.0,PF,23.0,6-10,275.0,,525093.0 158 | Pau Gasol,Chicago Bulls,16.0,C,35.0,7-0,250.0,,7448760.0 159 | Taj Gibson,Chicago Bulls,22.0,PF,30.0,6-9,225.0,USC,8500000.0 160 | Justin Holiday,Chicago Bulls,7.0,SG,27.0,6-6,185.0,Washington,947276.0 161 | Doug McDermott,Chicago Bulls,3.0,SF,24.0,6-8,225.0,Creighton,2380440.0 162 | Nikola Mirotic,Chicago Bulls,44.0,PF,25.0,6-10,220.0,,5543725.0 163 | E'Twaun Moore,Chicago Bulls,55.0,SG,27.0,6-4,191.0,Purdue,1015421.0 164 | Joakim Noah,Chicago Bulls,13.0,C,31.0,6-11,232.0,Florida,13400000.0 165 | Bobby Portis,Chicago Bulls,5.0,PF,21.0,6-11,230.0,Arkansas,1391160.0 166 | Derrick Rose,Chicago Bulls,1.0,PG,27.0,6-3,190.0,Memphis,20093064.0 167 | Tony Snell,Chicago Bulls,20.0,SF,24.0,6-7,200.0,New Mexico,1535880.0 168 | Matthew Dellavedova,Cleveland Cavaliers,8.0,PG,25.0,6-4,198.0,Saint Mary's,1147276.0 169 | Channing Frye,Cleveland Cavaliers,9.0,PF,33.0,6-11,255.0,Arizona,8193029.0 170 | Kyrie Irving,Cleveland Cavaliers,2.0,PG,24.0,6-3,193.0,Duke,16407501.0 171 | LeBron James,Cleveland Cavaliers,23.0,SF,31.0,6-8,250.0,,22970500.0 172 | Richard Jefferson,Cleveland Cavaliers,24.0,SF,35.0,6-7,233.0,Arizona,947276.0 173 | Dahntay Jones,Cleveland Cavaliers,30.0,SG,35.0,6-6,225.0,Duke, 174 | James Jones,Cleveland Cavaliers,1.0,SG,35.0,6-8,218.0,Miami (FL),947276.0 175 | Sasha Kaun,Cleveland Cavaliers,14.0,C,31.0,6-11,260.0,Kansas,1276000.0 176 | Kevin Love,Cleveland Cavaliers,0.0,PF,27.0,6-10,251.0,UCLA,19689000.0 177 | Jordan McRae,Cleveland Cavaliers,12.0,SG,25.0,6-5,179.0,Tennessee,111196.0 178 | Timofey Mozgov,Cleveland Cavaliers,20.0,C,29.0,7-1,275.0,,4950000.0 179 | Iman Shumpert,Cleveland Cavaliers,4.0,SG,25.0,6-5,220.0,Georgia Tech,8988765.0 180 | J.R. Smith,Cleveland Cavaliers,5.0,SG,30.0,6-6,225.0,,5000000.0 181 | Tristan Thompson,Cleveland Cavaliers,13.0,C,25.0,6-9,238.0,Texas,14260870.0 182 | Mo Williams,Cleveland Cavaliers,52.0,PG,33.0,6-1,198.0,Alabama,2100000.0 183 | Joel Anthony,Detroit Pistons,50.0,C,33.0,6-9,245.0,UNLV,2500000.0 184 | Aron Baynes,Detroit Pistons,12.0,C,29.0,6-10,260.0,Washington State,6500000.0 185 | Steve Blake,Detroit Pistons,22.0,PG,36.0,6-3,172.0,Maryland,2170465.0 186 | Lorenzo Brown,Detroit Pistons,17.0,PG,25.0,6-5,189.0,North Carolina State,111444.0 187 | Reggie Bullock,Detroit Pistons,25.0,SF,25.0,6-7,205.0,North Carolina,1252440.0 188 | Kentavious Caldwell-Pope,Detroit Pistons,5.0,SG,23.0,6-5,205.0,Georgia,2891760.0 189 | Spencer Dinwiddie,Detroit Pistons,8.0,PG,23.0,6-6,200.0,Colorado,845059.0 190 | Andre Drummond,Detroit Pistons,0.0,C,22.0,6-11,279.0,Connecticut,3272091.0 191 | Tobias Harris,Detroit Pistons,34.0,SF,23.0,6-9,235.0,Tennessee,16000000.0 192 | Darrun Hilliard,Detroit Pistons,6.0,SF,23.0,6-6,205.0,Villanova,600000.0 193 | Reggie Jackson,Detroit Pistons,1.0,PG,26.0,6-3,208.0,Boston College,13913044.0 194 | Stanley Johnson,Detroit Pistons,3.0,SF,20.0,6-7,245.0,Arizona,2841960.0 195 | Jodie Meeks,Detroit Pistons,20.0,SG,28.0,6-4,210.0,Kentucky,6270000.0 196 | Marcus Morris,Detroit Pistons,13.0,PF,26.0,6-9,235.0,Kansas,5000000.0 197 | Anthony Tolliver,Detroit Pistons,43.0,PF,31.0,6-8,240.0,Creighton,3000000.0 198 | Lavoy Allen,Indiana Pacers,5.0,PF,27.0,6-9,255.0,Temple,4050000.0 199 | Rakeem Christmas,Indiana Pacers,25.0,PF,24.0,6-9,250.0,Syracuse,1007026.0 200 | Monta Ellis,Indiana Pacers,11.0,SG,30.0,6-3,185.0,,10300000.0 201 | Paul George,Indiana Pacers,13.0,SF,26.0,6-9,220.0,Fresno State,17120106.0 202 | George Hill,Indiana Pacers,3.0,PG,30.0,6-3,188.0,IUPUI,8000000.0 203 | Jordan Hill,Indiana Pacers,27.0,C,28.0,6-10,235.0,Arizona,4000000.0 204 | Solomon Hill,Indiana Pacers,44.0,SF,25.0,6-7,225.0,Arizona,1358880.0 205 | Ty Lawson,Indiana Pacers,10.0,PG,28.0,5-11,195.0,North Carolina,211744.0 206 | Ian Mahinmi,Indiana Pacers,28.0,C,29.0,6-11,250.0,,4000000.0 207 | C.J. Miles,Indiana Pacers,0.0,SF,29.0,6-6,231.0,,4394225.0 208 | Glenn Robinson III,Indiana Pacers,40.0,SG,22.0,6-7,222.0,Michigan,1100000.0 209 | Rodney Stuckey,Indiana Pacers,2.0,PG,30.0,6-5,205.0,Eastern Washington,7000000.0 210 | Myles Turner,Indiana Pacers,33.0,PF,20.0,6-11,243.0,Texas,2357760.0 211 | Shayne Whittington,Indiana Pacers,42.0,PF,25.0,6-11,250.0,Western Michigan,845059.0 212 | Joe Young,Indiana Pacers,1.0,PG,23.0,6-2,180.0,Oregon,1007026.0 213 | Giannis Antetokounmpo,Milwaukee Bucks,34.0,SF,21.0,6-11,222.0,,1953960.0 214 | Jerryd Bayless,Milwaukee Bucks,19.0,PG,27.0,6-3,200.0,Arizona,3000000.0 215 | Michael Carter-Williams,Milwaukee Bucks,5.0,PG,24.0,6-6,190.0,Syracuse,2399040.0 216 | Jared Cunningham,Milwaukee Bucks,9.0,SG,25.0,6-4,195.0,Oregon State,947276.0 217 | Tyler Ennis,Milwaukee Bucks,11.0,PG,21.0,6-3,194.0,Syracuse,1662360.0 218 | John Henson,Milwaukee Bucks,31.0,PF,25.0,6-11,229.0,North Carolina,2943221.0 219 | Damien Inglis,Milwaukee Bucks,17.0,SF,21.0,6-8,246.0,,855000.0 220 | O.J. Mayo,Milwaukee Bucks,3.0,SG,28.0,6-5,210.0,USC,8000000.0 221 | Khris Middleton,Milwaukee Bucks,22.0,SG,24.0,6-8,234.0,Texas A&M,14700000.0 222 | Greg Monroe,Milwaukee Bucks,15.0,C,26.0,6-11,265.0,Georgetown,16407500.0 223 | Steve Novak,Milwaukee Bucks,6.0,SF,32.0,6-10,225.0,Marquette,295327.0 224 | Johnny O'Bryant III,Milwaukee Bucks,77.0,PF,23.0,6-9,257.0,LSU,845059.0 225 | Jabari Parker,Milwaukee Bucks,12.0,PF,21.0,6-8,250.0,Duke,5152440.0 226 | Miles Plumlee,Milwaukee Bucks,18.0,C,27.0,6-11,249.0,Duke,2109294.0 227 | Greivis Vasquez,Milwaukee Bucks,21.0,PG,29.0,6-6,217.0,Maryland,6600000.0 228 | Rashad Vaughn,Milwaukee Bucks,20.0,SG,19.0,6-6,202.0,UNLV,1733040.0 229 | Justin Anderson,Dallas Mavericks,1.0,SG,22.0,6-6,228.0,Virginia,1449000.0 230 | J.J. Barea,Dallas Mavericks,5.0,PG,31.0,6-0,185.0,Northeastern,4290000.0 231 | Jeremy Evans,Dallas Mavericks,21.0,SF,28.0,6-9,200.0,Western Kentucky,1100602.0 232 | Raymond Felton,Dallas Mavericks,2.0,PG,31.0,6-1,205.0,North Carolina,3950313.0 233 | Devin Harris,Dallas Mavericks,34.0,PG,33.0,6-3,185.0,Wisconsin,4053446.0 234 | David Lee,Dallas Mavericks,42.0,PF,33.0,6-9,245.0,Florida,2085671.0 235 | Wesley Matthews,Dallas Mavericks,23.0,SG,29.0,6-5,220.0,Marquette,16407500.0 236 | JaVale McGee,Dallas Mavericks,11.0,C,28.0,7-0,270.0,Nevada,1270964.0 237 | Salah Mejri,Dallas Mavericks,50.0,C,29.0,7-2,245.0,,525093.0 238 | Dirk Nowitzki,Dallas Mavericks,41.0,PF,37.0,7-0,245.0,,8333334.0 239 | Zaza Pachulia,Dallas Mavericks,27.0,C,32.0,6-11,275.0,,5200000.0 240 | Chandler Parsons,Dallas Mavericks,25.0,SF,27.0,6-10,230.0,Florida,15361500.0 241 | Dwight Powell,Dallas Mavericks,7.0,PF,24.0,6-11,240.0,Stanford,845059.0 242 | Charlie Villanueva,Dallas Mavericks,3.0,PF,31.0,6-11,232.0,Connecticut,947276.0 243 | Deron Williams,Dallas Mavericks,8.0,PG,31.0,6-3,200.0,Illinois,5378974.0 244 | Trevor Ariza,Houston Rockets,1.0,SF,30.0,6-8,215.0,UCLA,8193030.0 245 | Michael Beasley,Houston Rockets,8.0,SF,27.0,6-10,235.0,Kansas State,306527.0 246 | Patrick Beverley,Houston Rockets,2.0,PG,27.0,6-1,185.0,Arkansas,6486486.0 247 | Corey Brewer,Houston Rockets,33.0,SG,30.0,6-9,186.0,Florida,8229375.0 248 | Clint Capela,Houston Rockets,15.0,PF,22.0,6-10,240.0,,1242720.0 249 | Sam Dekker,Houston Rockets,7.0,SF,22.0,6-9,230.0,Wisconsin,1646400.0 250 | Andrew Goudelock,Houston Rockets,0.0,PG,27.0,6-3,200.0,Charleston,200600.0 251 | James Harden,Houston Rockets,13.0,SG,26.0,6-5,220.0,Arizona State,15756438.0 252 | Montrezl Harrell,Houston Rockets,35.0,PF,22.0,6-8,240.0,Louisville,1000000.0 253 | Dwight Howard,Houston Rockets,12.0,C,30.0,6-11,265.0,,22359364.0 254 | Terrence Jones,Houston Rockets,6.0,PF,24.0,6-9,252.0,Kentucky,2489530.0 255 | K.J. McDaniels,Houston Rockets,32.0,SG,23.0,6-6,205.0,Clemson,3189794.0 256 | Donatas Motiejunas,Houston Rockets,20.0,PF,25.0,7-0,222.0,,2288205.0 257 | Josh Smith,Houston Rockets,5.0,C,30.0,6-9,225.0,,947276.0 258 | Jason Terry,Houston Rockets,31.0,SG,38.0,6-2,185.0,Arizona,947276.0 259 | Jordan Adams,Memphis Grizzlies,3.0,SG,21.0,6-5,209.0,UCLA,1404600.0 260 | Tony Allen,Memphis Grizzlies,9.0,SG,34.0,6-4,213.0,Oklahoma State,5158539.0 261 | Chris Andersen,Memphis Grizzlies,7.0,PF,37.0,6-10,245.0,Blinn College,5000000.0 262 | Matt Barnes,Memphis Grizzlies,22.0,SF,36.0,6-7,226.0,UCLA,3542500.0 263 | Vince Carter,Memphis Grizzlies,15.0,SG,39.0,6-6,220.0,North Carolina,4088019.0 264 | Mike Conley,Memphis Grizzlies,11.0,PG,28.0,6-1,175.0,Ohio State,9588426.0 265 | Bryce Cotton,Memphis Grizzlies,8.0,PG,23.0,6-1,165.0,Providence,700902.0 266 | Jordan Farmar,Memphis Grizzlies,4.0,PG,29.0,6-2,180.0,UCLA, 267 | Marc Gasol,Memphis Grizzlies,33.0,C,31.0,7-1,255.0,,19688000.0 268 | JaMychal Green,Memphis Grizzlies,0.0,PF,25.0,6-9,227.0,Alabama,845059.0 269 | P.J. Hairston,Memphis Grizzlies,19.0,SF,23.0,6-6,230.0,North Carolina,1201440.0 270 | Jarell Martin,Memphis Grizzlies,10.0,PF,22.0,6-10,239.0,LSU,1230840.0 271 | Ray McCallum,Memphis Grizzlies,5.0,PG,24.0,6-3,190.0,Detroit, 272 | Xavier Munford,Memphis Grizzlies,14.0,PG,24.0,6-3,180.0,Rhode Island, 273 | Zach Randolph,Memphis Grizzlies,50.0,PF,34.0,6-9,260.0,Michigan State,9638555.0 274 | Lance Stephenson,Memphis Grizzlies,1.0,SF,25.0,6-5,230.0,Cincinnati,9000000.0 275 | Alex Stepheson,Memphis Grizzlies,35.0,PF,28.0,6-10,270.0,USC, 276 | Brandan Wright,Memphis Grizzlies,34.0,PF,28.0,6-10,210.0,North Carolina,5464000.0 277 | Alexis Ajinca,New Orleans Pelicans,42.0,C,28.0,7-2,248.0,,4389607.0 278 | Ryan Anderson,New Orleans Pelicans,33.0,PF,28.0,6-10,240.0,California,8500000.0 279 | Omer Asik,New Orleans Pelicans,3.0,C,29.0,7-0,255.0,,9213483.0 280 | Luke Babbitt,New Orleans Pelicans,8.0,SF,26.0,6-9,225.0,Nevada,1100602.0 281 | Norris Cole,New Orleans Pelicans,30.0,PG,27.0,6-2,175.0,Cleveland State,3036927.0 282 | Dante Cunningham,New Orleans Pelicans,44.0,PF,29.0,6-8,230.0,Villanova,2850000.0 283 | Anthony Davis,New Orleans Pelicans,23.0,PF,23.0,6-10,253.0,Kentucky,7070730.0 284 | Bryce Dejean-Jones,New Orleans Pelicans,31.0,SG,23.0,6-6,203.0,Iowa State,169883.0 285 | Toney Douglas,New Orleans Pelicans,16.0,PG,30.0,6-2,195.0,Florida State,1164858.0 286 | James Ennis,New Orleans Pelicans,4.0,SF,25.0,6-7,210.0,Long Beach State,845059.0 287 | Tyreke Evans,New Orleans Pelicans,1.0,SG,26.0,6-6,220.0,Memphis,10734586.0 288 | Tim Frazier,New Orleans Pelicans,2.0,PG,25.0,6-1,170.0,Penn State,845059.0 289 | Alonzo Gee,New Orleans Pelicans,15.0,SF,29.0,6-6,225.0,Alabama,1320000.0 290 | Eric Gordon,New Orleans Pelicans,10.0,SG,27.0,6-4,215.0,Indiana,15514031.0 291 | Jordan Hamilton,New Orleans Pelicans,25.0,SG,25.0,6-7,220.0,Texas,1015421.0 292 | Jrue Holiday,New Orleans Pelicans,11.0,PG,25.0,6-4,205.0,UCLA,10595507.0 293 | Orlando Johnson,New Orleans Pelicans,0.0,SG,27.0,6-5,220.0,UC Santa Barbara,55722.0 294 | Kendrick Perkins,New Orleans Pelicans,5.0,C,31.0,6-10,270.0,,947276.0 295 | Quincy Pondexter,New Orleans Pelicans,20.0,SF,28.0,6-7,220.0,Washington,3382023.0 296 | LaMarcus Aldridge,San Antonio Spurs,12.0,PF,30.0,6-11,240.0,Texas,19689000.0 297 | Kyle Anderson,San Antonio Spurs,1.0,SF,22.0,6-9,230.0,UCLA,1142880.0 298 | Matt Bonner,San Antonio Spurs,15.0,C,36.0,6-10,235.0,Florida,947276.0 299 | Boris Diaw,San Antonio Spurs,33.0,C,34.0,6-8,250.0,,7500000.0 300 | Tim Duncan,San Antonio Spurs,21.0,C,40.0,6-11,250.0,Wake Forest,5250000.0 301 | Manu Ginobili,San Antonio Spurs,20.0,SG,38.0,6-6,205.0,,2814000.0 302 | Danny Green,San Antonio Spurs,14.0,SG,28.0,6-6,215.0,North Carolina,10000000.0 303 | Kawhi Leonard,San Antonio Spurs,2.0,SF,24.0,6-7,230.0,San Diego State,16407500.0 304 | Boban Marjanovic,San Antonio Spurs,40.0,C,27.0,7-3,290.0,,1200000.0 305 | Kevin Martin,San Antonio Spurs,23.0,SG,33.0,6-7,199.0,Western Carolina,200600.0 306 | Andre Miller,San Antonio Spurs,24.0,PG,40.0,6-3,200.0,Utah,250750.0 307 | Patty Mills,San Antonio Spurs,8.0,PG,27.0,6-0,185.0,Saint Mary's,3578947.0 308 | Tony Parker,San Antonio Spurs,9.0,PG,34.0,6-2,185.0,,13437500.0 309 | Jonathon Simmons,San Antonio Spurs,17.0,SG,26.0,6-6,195.0,Houston,525093.0 310 | David West,San Antonio Spurs,30.0,PF,35.0,6-9,250.0,Xavier,1499187.0 311 | Kent Bazemore,Atlanta Hawks,24.0,SF,26.0,6-5,201.0,Old Dominion,2000000.0 312 | Tim Hardaway Jr.,Atlanta Hawks,10.0,SG,24.0,6-6,205.0,Michigan,1304520.0 313 | Kirk Hinrich,Atlanta Hawks,12.0,SG,35.0,6-4,190.0,Kansas,2854940.0 314 | Al Horford,Atlanta Hawks,15.0,C,30.0,6-10,245.0,Florida,12000000.0 315 | Kris Humphries,Atlanta Hawks,43.0,PF,31.0,6-9,235.0,Minnesota,1000000.0 316 | Kyle Korver,Atlanta Hawks,26.0,SG,35.0,6-7,212.0,Creighton,5746479.0 317 | Paul Millsap,Atlanta Hawks,4.0,PF,31.0,6-8,246.0,Louisiana Tech,18671659.0 318 | Mike Muscala,Atlanta Hawks,31.0,PF,24.0,6-11,240.0,Bucknell,947276.0 319 | Lamar Patterson,Atlanta Hawks,13.0,SG,24.0,6-5,225.0,Pittsburgh,525093.0 320 | Dennis Schroder,Atlanta Hawks,17.0,PG,22.0,6-1,172.0,,1763400.0 321 | Mike Scott,Atlanta Hawks,32.0,PF,27.0,6-8,237.0,Virginia,3333333.0 322 | Thabo Sefolosha,Atlanta Hawks,25.0,SF,32.0,6-7,220.0,,4000000.0 323 | Tiago Splitter,Atlanta Hawks,11.0,C,31.0,6-11,245.0,,9756250.0 324 | Walter Tavares,Atlanta Hawks,22.0,C,24.0,7-3,260.0,,1000000.0 325 | Jeff Teague,Atlanta Hawks,0.0,PG,27.0,6-2,186.0,Wake Forest,8000000.0 326 | Nicolas Batum,Charlotte Hornets,5.0,SG,27.0,6-8,200.0,,13125306.0 327 | Troy Daniels,Charlotte Hornets,30.0,SG,24.0,6-4,205.0,Virginia Commonwealth,947276.0 328 | Jorge Gutierrez,Charlotte Hornets,12.0,PG,27.0,6-3,189.0,California,189455.0 329 | Tyler Hansbrough,Charlotte Hornets,50.0,PF,30.0,6-9,250.0,North Carolina,947276.0 330 | Aaron Harrison,Charlotte Hornets,9.0,SG,21.0,6-6,210.0,Kentucky,525093.0 331 | Spencer Hawes,Charlotte Hornets,0.0,PF,28.0,7-1,245.0,Washington,6110034.0 332 | Al Jefferson,Charlotte Hornets,25.0,C,31.0,6-10,289.0,,13500000.0 333 | Frank Kaminsky III,Charlotte Hornets,44.0,C,23.0,7-0,240.0,Wisconsin,2612520.0 334 | Michael Kidd-Gilchrist,Charlotte Hornets,14.0,SF,22.0,6-7,232.0,Kentucky,6331404.0 335 | Jeremy Lamb,Charlotte Hornets,3.0,SG,24.0,6-5,185.0,Connecticut,3034356.0 336 | Courtney Lee,Charlotte Hornets,1.0,SG,30.0,6-5,200.0,Western Kentucky,5675000.0 337 | Jeremy Lin,Charlotte Hornets,7.0,PG,27.0,6-3,200.0,Harvard,2139000.0 338 | Kemba Walker,Charlotte Hornets,15.0,PG,26.0,6-1,184.0,Connecticut,12000000.0 339 | Marvin Williams,Charlotte Hornets,2.0,PF,29.0,6-9,237.0,North Carolina,7000000.0 340 | Cody Zeller,Charlotte Hornets,40.0,C,23.0,7-0,240.0,Indiana,4204200.0 341 | Chris Bosh,Miami Heat,1.0,PF,32.0,6-11,235.0,Georgia Tech,22192730.0 342 | Luol Deng,Miami Heat,9.0,SF,31.0,6-9,220.0,Duke,10151612.0 343 | Goran Dragic,Miami Heat,7.0,PG,30.0,6-3,190.0,,14783000.0 344 | Gerald Green,Miami Heat,14.0,SF,30.0,6-7,205.0,,947276.0 345 | Udonis Haslem,Miami Heat,40.0,PF,36.0,6-8,235.0,Florida,2854940.0 346 | Joe Johnson,Miami Heat,2.0,SF,34.0,6-7,240.0,Arkansas,261894.0 347 | Tyler Johnson,Miami Heat,8.0,SG,24.0,6-4,186.0,Fresno State,845059.0 348 | Josh McRoberts,Miami Heat,4.0,PF,29.0,6-10,240.0,Duke,5543725.0 349 | Josh Richardson,Miami Heat,0.0,SG,22.0,6-6,200.0,Tennessee,525093.0 350 | Amar'e Stoudemire,Miami Heat,5.0,PF,33.0,6-10,245.0,,947276.0 351 | Dwyane Wade,Miami Heat,3.0,SG,34.0,6-4,220.0,Marquette,20000000.0 352 | Briante Weber,Miami Heat,12.0,PG,23.0,6-2,165.0,Virginia Commonwealth, 353 | Hassan Whiteside,Miami Heat,21.0,C,26.0,7-0,265.0,Marshall,981348.0 354 | Justise Winslow,Miami Heat,20.0,SF,20.0,6-7,225.0,Duke,2481720.0 355 | Dorell Wright,Miami Heat,11.0,SF,30.0,6-9,205.0,, 356 | Dewayne Dedmon,Orlando Magic,3.0,C,26.0,7-0,245.0,USC,947276.0 357 | Evan Fournier,Orlando Magic,10.0,SG,23.0,6-7,205.0,,2288205.0 358 | Aaron Gordon,Orlando Magic,0.0,PF,20.0,6-9,220.0,Arizona,4171680.0 359 | Mario Hezonja,Orlando Magic,23.0,SG,21.0,6-8,218.0,,3741480.0 360 | Ersan Ilyasova,Orlando Magic,7.0,PF,29.0,6-10,235.0,,7900000.0 361 | Brandon Jennings,Orlando Magic,55.0,PG,26.0,6-1,169.0,,8344497.0 362 | Devyn Marble,Orlando Magic,11.0,SF,23.0,6-6,200.0,Iowa,845059.0 363 | Shabazz Napier,Orlando Magic,13.0,PG,24.0,6-1,175.0,Connecticut,1294440.0 364 | Andrew Nicholson,Orlando Magic,44.0,PF,26.0,6-9,250.0,St. Bonaventure,2380593.0 365 | Victor Oladipo,Orlando Magic,5.0,SG,24.0,6-4,210.0,Indiana,5192520.0 366 | Elfrid Payton,Orlando Magic,4.0,PG,22.0,6-4,185.0,Louisiana-Lafayette,2505720.0 367 | Jason Smith,Orlando Magic,14.0,PF,30.0,7-0,240.0,Colorado State,4300000.0 368 | Nikola Vucevic,Orlando Magic,9.0,C,25.0,7-0,260.0,USC,11250000.0 369 | C.J. Watson,Orlando Magic,32.0,PG,32.0,6-2,175.0,Tennessee,5000000.0 370 | Alan Anderson,Washington Wizards,6.0,SG,33.0,6-6,220.0,Michigan State,4000000.0 371 | Bradley Beal,Washington Wizards,3.0,SG,22.0,6-5,207.0,Florida,5694674.0 372 | Jared Dudley,Washington Wizards,1.0,SF,30.0,6-7,225.0,Boston College,4375000.0 373 | Jarell Eddie,Washington Wizards,8.0,SG,24.0,6-7,218.0,Virginia Tech,561716.0 374 | Drew Gooden,Washington Wizards,90.0,PF,34.0,6-10,250.0,Kansas,3300000.0 375 | Marcin Gortat,Washington Wizards,13.0,C,32.0,6-11,240.0,,11217391.0 376 | JJ Hickson,Washington Wizards,21.0,C,27.0,6-9,242.0,North Carolina State,273038.0 377 | Nene Hilario,Washington Wizards,42.0,C,33.0,6-11,250.0,,13000000.0 378 | Markieff Morris,Washington Wizards,5.0,PF,26.0,6-10,245.0,Kansas,8000000.0 379 | Kelly Oubre Jr.,Washington Wizards,12.0,SF,20.0,6-7,205.0,Kansas,1920240.0 380 | Otto Porter Jr.,Washington Wizards,22.0,SF,23.0,6-8,198.0,Georgetown,4662960.0 381 | Ramon Sessions,Washington Wizards,7.0,PG,30.0,6-3,190.0,Nevada,2170465.0 382 | Garrett Temple,Washington Wizards,17.0,SG,30.0,6-6,195.0,LSU,1100602.0 383 | Marcus Thornton,Washington Wizards,15.0,SF,29.0,6-4,205.0,LSU,200600.0 384 | John Wall,Washington Wizards,2.0,PG,25.0,6-4,195.0,Kentucky,15851950.0 385 | Darrell Arthur,Denver Nuggets,0.0,PF,28.0,6-9,235.0,Kansas,2814000.0 386 | D.J. Augustin,Denver Nuggets,12.0,PG,28.0,6-0,183.0,Texas,3000000.0 387 | Will Barton,Denver Nuggets,5.0,SF,25.0,6-6,175.0,Memphis,3533333.0 388 | Wilson Chandler,Denver Nuggets,21.0,SF,29.0,6-8,225.0,DePaul,10449438.0 389 | Kenneth Faried,Denver Nuggets,35.0,PF,26.0,6-8,228.0,Morehead State,11235955.0 390 | Danilo Gallinari,Denver Nuggets,8.0,SF,27.0,6-10,225.0,,14000000.0 391 | Gary Harris,Denver Nuggets,14.0,SG,21.0,6-4,210.0,Michigan State,1584480.0 392 | Nikola Jokic,Denver Nuggets,15.0,C,21.0,6-10,250.0,,1300000.0 393 | Joffrey Lauvergne,Denver Nuggets,77.0,C,24.0,6-11,220.0,,1709719.0 394 | Mike Miller,Denver Nuggets,3.0,SG,36.0,6-8,218.0,Florida,947276.0 395 | Emmanuel Mudiay,Denver Nuggets,0.0,PG,20.0,6-5,200.0,,3102240.0 396 | Jameer Nelson,Denver Nuggets,1.0,PG,34.0,6-0,190.0,Saint Joseph's,4345000.0 397 | Jusuf Nurkic,Denver Nuggets,23.0,C,21.0,7-0,280.0,,1842000.0 398 | JaKarr Sampson,Denver Nuggets,9.0,SG,23.0,6-9,214.0,St. John's,258489.0 399 | Axel Toupane,Denver Nuggets,6.0,SG,23.0,6-7,210.0,, 400 | Nemanja Bjelica,Minnesota Timberwolves,88.0,PF,28.0,6-10,240.0,,3950001.0 401 | Gorgui Dieng,Minnesota Timberwolves,5.0,C,26.0,6-11,241.0,Louisville,1474440.0 402 | Kevin Garnett,Minnesota Timberwolves,21.0,PF,40.0,6-11,240.0,,8500000.0 403 | Tyus Jones,Minnesota Timberwolves,1.0,PG,20.0,6-2,195.0,Duke,1282080.0 404 | Zach LaVine,Minnesota Timberwolves,8.0,PG,21.0,6-5,189.0,UCLA,2148360.0 405 | Shabazz Muhammad,Minnesota Timberwolves,15.0,SF,23.0,6-6,223.0,UCLA,2056920.0 406 | Adreian Payne,Minnesota Timberwolves,33.0,PF,25.0,6-10,237.0,Michigan State,1938840.0 407 | Nikola Pekovic,Minnesota Timberwolves,14.0,C,30.0,6-11,307.0,,12100000.0 408 | Tayshaun Prince,Minnesota Timberwolves,12.0,SF,36.0,6-9,212.0,Kentucky,947276.0 409 | Ricky Rubio,Minnesota Timberwolves,9.0,PG,25.0,6-4,194.0,,12700000.0 410 | Damjan Rudez,Minnesota Timberwolves,10.0,SF,29.0,6-9,230.0,,1149500.0 411 | Greg Smith,Minnesota Timberwolves,4.0,PF,25.0,6-10,250.0,Fresno State, 412 | Karl-Anthony Towns,Minnesota Timberwolves,32.0,C,20.0,7-0,244.0,Kentucky,5703600.0 413 | Andrew Wiggins,Minnesota Timberwolves,22.0,SG,21.0,6-8,199.0,Kansas,5758680.0 414 | Steven Adams,Oklahoma City Thunder,12.0,C,22.0,7-0,255.0,Pittsburgh,2279040.0 415 | Nick Collison,Oklahoma City Thunder,4.0,PF,35.0,6-10,255.0,Kansas,3750000.0 416 | Kevin Durant,Oklahoma City Thunder,35.0,SF,27.0,6-9,240.0,Texas,20158622.0 417 | Randy Foye,Oklahoma City Thunder,6.0,SG,32.0,6-4,213.0,Villanova,3135000.0 418 | Josh Huestis,Oklahoma City Thunder,34.0,SF,24.0,6-7,230.0,Stanford,1140240.0 419 | Serge Ibaka,Oklahoma City Thunder,9.0,PF,26.0,6-10,245.0,,12250000.0 420 | Enes Kanter,Oklahoma City Thunder,11.0,C,24.0,6-11,245.0,Kentucky,16407500.0 421 | Mitch McGary,Oklahoma City Thunder,33.0,PF,24.0,6-10,255.0,Michigan,1463040.0 422 | Nazr Mohammed,Oklahoma City Thunder,13.0,C,38.0,6-10,250.0,Kentucky,222888.0 423 | Anthony Morrow,Oklahoma City Thunder,2.0,SG,30.0,6-5,210.0,Georgia Tech,3344000.0 424 | Cameron Payne,Oklahoma City Thunder,22.0,PG,21.0,6-3,185.0,Murray State,2021520.0 425 | Andre Roberson,Oklahoma City Thunder,21.0,SG,24.0,6-7,210.0,Colorado,1210800.0 426 | Kyle Singler,Oklahoma City Thunder,5.0,SF,28.0,6-8,228.0,Duke,4500000.0 427 | Dion Waiters,Oklahoma City Thunder,3.0,SG,24.0,6-4,220.0,Syracuse,5138430.0 428 | Russell Westbrook,Oklahoma City Thunder,0.0,PG,27.0,6-3,200.0,UCLA,16744218.0 429 | Cliff Alexander,Portland Trail Blazers,34.0,PF,20.0,6-8,240.0,Kansas,525093.0 430 | Al-Farouq Aminu,Portland Trail Blazers,8.0,SF,25.0,6-9,215.0,Wake Forest,8042895.0 431 | Pat Connaughton,Portland Trail Blazers,5.0,SG,23.0,6-5,206.0,Notre Dame,625093.0 432 | Allen Crabbe,Portland Trail Blazers,23.0,SG,24.0,6-6,210.0,California,947276.0 433 | Ed Davis,Portland Trail Blazers,17.0,C,27.0,6-10,240.0,North Carolina,6980802.0 434 | Maurice Harkless,Portland Trail Blazers,4.0,SF,23.0,6-9,215.0,St. John's,2894059.0 435 | Gerald Henderson,Portland Trail Blazers,9.0,SG,28.0,6-5,215.0,Duke,6000000.0 436 | Chris Kaman,Portland Trail Blazers,35.0,C,34.0,7-0,265.0,Central Michigan,5016000.0 437 | Meyers Leonard,Portland Trail Blazers,11.0,PF,24.0,7-1,245.0,Illinois,3075880.0 438 | Damian Lillard,Portland Trail Blazers,0.0,PG,25.0,6-3,195.0,Weber State,4236287.0 439 | C.J. McCollum,Portland Trail Blazers,3.0,SG,24.0,6-4,200.0,Lehigh,2525160.0 440 | Luis Montero,Portland Trail Blazers,44.0,SG,23.0,6-7,185.0,Westchester CC,525093.0 441 | Mason Plumlee,Portland Trail Blazers,24.0,C,26.0,6-11,235.0,Duke,1415520.0 442 | Brian Roberts,Portland Trail Blazers,2.0,PG,30.0,6-1,173.0,Dayton,2854940.0 443 | Noah Vonleh,Portland Trail Blazers,21.0,PF,20.0,6-9,240.0,Indiana,2637720.0 444 | Trevor Booker,Utah Jazz,33.0,PF,28.0,6-8,228.0,Clemson,4775000.0 445 | Trey Burke,Utah Jazz,3.0,PG,23.0,6-1,191.0,Michigan,2658240.0 446 | Alec Burks,Utah Jazz,10.0,SG,24.0,6-6,214.0,Colorado,9463484.0 447 | Dante Exum,Utah Jazz,11.0,PG,20.0,6-6,190.0,,3777720.0 448 | Derrick Favors,Utah Jazz,15.0,PF,24.0,6-10,265.0,Georgia Tech,12000000.0 449 | Rudy Gobert,Utah Jazz,27.0,C,23.0,7-1,245.0,,1175880.0 450 | Gordon Hayward,Utah Jazz,20.0,SF,26.0,6-8,226.0,Butler,15409570.0 451 | Rodney Hood,Utah Jazz,5.0,SG,23.0,6-8,206.0,Duke,1348440.0 452 | Joe Ingles,Utah Jazz,2.0,SF,28.0,6-8,226.0,,2050000.0 453 | Chris Johnson,Utah Jazz,23.0,SF,26.0,6-6,206.0,Dayton,981348.0 454 | Trey Lyles,Utah Jazz,41.0,PF,20.0,6-10,234.0,Kentucky,2239800.0 455 | Shelvin Mack,Utah Jazz,8.0,PG,26.0,6-3,203.0,Butler,2433333.0 456 | Raul Neto,Utah Jazz,25.0,PG,24.0,6-1,179.0,,900000.0 457 | Tibor Pleiss,Utah Jazz,21.0,C,26.0,7-3,256.0,,2900000.0 458 | Jeff Withey,Utah Jazz,24.0,C,26.0,7-0,231.0,Kansas,947276.0 459 | ,,,,,,,, 460 | -------------------------------------------------------------------------------- /pokemon.csv: -------------------------------------------------------------------------------- 1 | Pokemon,Type 2 | Bulbasaur,Grass 3 | Ivysaur,Grass 4 | Venusaur,Grass 5 | Charmander,Fire 6 | Charmeleon,Fire 7 | Charizard,Fire 8 | Squirtle,Water 9 | Wartortle,Water 10 | Blastoise,Water 11 | Caterpie,Bug 12 | Metapod,Bug 13 | Butterfree,Bug 14 | Weedle,Bug 15 | Kakuna,Bug 16 | Beedrill,Bug 17 | Pidgey,Normal 18 | Pidgeotto,Normal 19 | Pidgeot,Normal 20 | Rattata,Normal 21 | Raticate,Normal 22 | Spearow,Normal 23 | Fearow,Normal 24 | Ekans,Poison 25 | Arbok,Poison 26 | Pikachu,Electric 27 | Raichu,Electric 28 | Sandshrew,Ground 29 | Sandslash,Ground 30 | Nidoran,Poison 31 | Nidorina,Poison 32 | Nidoqueen,Poison 33 | Nidoran♂,Poison 34 | Nidorino,Poison 35 | Nidoking,Poison 36 | Clefairy,Fairy 37 | Clefable,Fairy 38 | Vulpix,Fire 39 | Ninetales,Fire 40 | Jigglypuff,Normal 41 | Wigglytuff,Normal 42 | Zubat,Poison 43 | Golbat,Poison 44 | Oddish,Grass 45 | Gloom,Grass 46 | Vileplume,Grass 47 | Paras,Bug 48 | Parasect,Bug 49 | Venonat,Bug 50 | Venomoth,Bug 51 | Diglett,Ground 52 | Dugtrio,Ground 53 | Meowth,Normal 54 | Persian,Normal 55 | Psyduck,Water 56 | Golduck,Water 57 | Mankey,Fighting 58 | Primeape,Fighting 59 | Growlithe,Fire 60 | Arcanine,Fire 61 | Poliwag,Water 62 | Poliwhirl,Water 63 | Poliwrath,Water 64 | Abra,Psychic 65 | Kadabra,Psychic 66 | Alakazam,Psychic 67 | Machop,Fighting 68 | Machoke,Fighting 69 | Machamp,Fighting 70 | Bellsprout,Grass 71 | Weepinbell,Grass 72 | Victreebel,Grass 73 | Tentacool,Water 74 | Tentacruel,Water 75 | Geodude,Rock 76 | Graveler,Rock 77 | Golem,Rock 78 | Ponyta,Fire 79 | Rapidash,Fire 80 | Slowpoke,Water 81 | Slowbro,Water 82 | Magnemite,Electric 83 | Magneton,Electric 84 | Farfetch'd,Normal 85 | Doduo,Normal 86 | Dodrio,Normal 87 | Seel,Water 88 | Dewgong,Water 89 | Grimer,Poison 90 | Muk,Poison 91 | Shellder,Water 92 | Cloyster,Water 93 | Gastly,Ghost 94 | Haunter,Ghost 95 | Gengar,Ghost 96 | Onix,Rock 97 | Drowzee,Psychic 98 | Hypno,Psychic 99 | Krabby,Water 100 | Kingler,Water 101 | Voltorb,Electric 102 | Electrode,Electric 103 | Exeggcute,Grass 104 | Exeggutor,Grass 105 | Cubone,Ground 106 | Marowak,Ground 107 | Hitmonlee,Fighting 108 | Hitmonchan,Fighting 109 | Lickitung,Normal 110 | Koffing,Poison 111 | Weezing,Poison 112 | Rhyhorn,Ground 113 | Rhydon,Ground 114 | Chansey,Normal 115 | Tangela,Grass 116 | Kangaskhan,Normal 117 | Horsea,Water 118 | Seadra,Water 119 | Goldeen,Water 120 | Seaking,Water 121 | Staryu,Water 122 | Starmie,Water 123 | Mr. Mime,Psychic 124 | Scyther,Bug 125 | Jynx,Ice 126 | Electabuzz,Electric 127 | Magmar,Fire 128 | Pinsir,Bug 129 | Tauros,Normal 130 | Magikarp,Water 131 | Gyarados,Water 132 | Lapras,Water 133 | Ditto,Normal 134 | Eevee,Normal 135 | Vaporeon,Water 136 | Jolteon,Electric 137 | Flareon,Fire 138 | Porygon,Normal 139 | Omanyte,Rock 140 | Omastar,Rock 141 | Kabuto,Rock 142 | Kabutops,Rock 143 | Aerodactyl,Rock 144 | Snorlax,Normal 145 | Articuno,Ice 146 | Zapdos,Electric 147 | Moltres,Fire 148 | Dratini,Dragon 149 | Dragonair,Dragon 150 | Dragonite,Dragon 151 | Mewtwo,Psychic 152 | Mew,Psychic 153 | Chikorita,Grass 154 | Bayleef,Grass 155 | Meganium,Grass 156 | Cyndaquil,Fire 157 | Quilava,Fire 158 | Typhlosion,Fire 159 | Totodile,Water 160 | Croconaw,Water 161 | Feraligatr,Water 162 | Sentret,Normal 163 | Furret,Normal 164 | Hoothoot,Normal 165 | Noctowl,Normal 166 | Ledyba,Bug 167 | Ledian,Bug 168 | Spinarak,Bug 169 | Ariados,Bug 170 | Crobat,Poison 171 | Chinchou,Water 172 | Lanturn,Water 173 | Pichu,Electric 174 | Cleffa,Fairy 175 | Igglybuff,Normal 176 | Togepi,Fairy 177 | Togetic,Fairy 178 | Natu,Psychic 179 | Xatu,Psychic 180 | Mareep,Electric 181 | Flaaffy,Electric 182 | Ampharos,Electric 183 | Bellossom,Grass 184 | Marill,Water 185 | Azumarill,Water 186 | Sudowoodo,Rock 187 | Politoed,Water 188 | Hoppip,Grass 189 | Skiploom,Grass 190 | Jumpluff,Grass 191 | Aipom,Normal 192 | Sunkern,Grass 193 | Sunflora,Grass 194 | Yanma,Bug 195 | Wooper,Water 196 | Quagsire,Water 197 | Espeon,Psychic 198 | Umbreon,Dark 199 | Murkrow,Dark 200 | Slowking,Water 201 | Misdreavus,Ghost 202 | Unown,Psychic 203 | Wobbuffet,Psychic 204 | Girafarig,Normal 205 | Pineco,Bug 206 | Forretress,Bug 207 | Dunsparce,Normal 208 | Gligar,Ground 209 | Steelix,Steel 210 | Snubbull,Fairy 211 | Granbull,Fairy 212 | Qwilfish,Water 213 | Scizor,Bug 214 | Shuckle,Bug 215 | Heracross,Bug 216 | Sneasel,Dark 217 | Teddiursa,Normal 218 | Ursaring,Normal 219 | Slugma,Fire 220 | Magcargo,Fire 221 | Swinub,Ice 222 | Piloswine,Ice 223 | Corsola,Water 224 | Remoraid,Water 225 | Octillery,Water 226 | Delibird,Ice 227 | Mantine,Water 228 | Skarmory,Steel 229 | Houndour,Dark 230 | Houndoom,Dark 231 | Kingdra,Water 232 | Phanpy,Ground 233 | Donphan,Ground 234 | Porygon2,Normal 235 | Stantler,Normal 236 | Smeargle,Normal 237 | Tyrogue,Fighting 238 | Hitmontop,Fighting 239 | Smoochum,Ice 240 | Elekid,Electric 241 | Magby,Fire 242 | Miltank,Normal 243 | Blissey,Normal 244 | Raikou,Electric 245 | Entei,Fire 246 | Suicune,Water 247 | Larvitar,Rock 248 | Pupitar,Rock 249 | Tyranitar,Rock 250 | Lugia,Psychic 251 | Ho-oh,Fire 252 | Celebi,Psychic 253 | Treecko,Grass 254 | Grovyle,Grass 255 | Sceptile,Grass 256 | Torchic,Fire 257 | Combusken,Fire 258 | Blaziken,Fire 259 | Mudkip,Water 260 | Marshtomp,Water 261 | Swampert,Water 262 | Poochyena,Dark 263 | Mightyena,Dark 264 | Zigzagoon,Normal 265 | Linoone,Normal 266 | Wurmple,Bug 267 | Silcoon,Bug 268 | Beautifly,Bug 269 | Cascoon,Bug 270 | Dustox,Bug 271 | Lotad,Water 272 | Lombre,Water 273 | Ludicolo,Water 274 | Seedot,Grass 275 | Nuzleaf,Grass 276 | Shiftry,Grass 277 | Taillow,Normal 278 | Swellow,Normal 279 | Wingull,Water 280 | Pelipper,Water 281 | Ralts,Psychic 282 | Kirlia,Psychic 283 | Gardevoir,Psychic 284 | Surskit,Bug 285 | Masquerain,Bug 286 | Shroomish,Grass 287 | Breloom,Grass 288 | Slakoth,Normal 289 | Vigoroth,Normal 290 | Slaking,Normal 291 | Nincada,Bug 292 | Ninjask,Bug 293 | Shedinja,Bug 294 | Whismur,Normal 295 | Loudred,Normal 296 | Exploud,Normal 297 | Makuhita,Fighting 298 | Hariyama,Fighting 299 | Azurill,Normal 300 | Nosepass,Rock 301 | Skitty,Normal 302 | Delcatty,Normal 303 | Sableye,Dark 304 | Mawile,Steel 305 | Aron,Steel 306 | Lairon,Steel 307 | Aggron,Steel 308 | Meditite,Fighting 309 | Medicham,Fighting 310 | Electrike,Electric 311 | Manectric,Electric 312 | Plusle,Electric 313 | Minun,Electric 314 | Volbeat,Bug 315 | Illumise,Bug 316 | Roselia,Grass 317 | Gulpin,Poison 318 | Swalot,Poison 319 | Carvanha,Water 320 | Sharpedo,Water 321 | Wailmer,Water 322 | Wailord,Water 323 | Numel,Fire 324 | Camerupt,Fire 325 | Torkoal,Fire 326 | Spoink,Psychic 327 | Grumpig,Psychic 328 | Spinda,Normal 329 | Trapinch,Ground 330 | Vibrava,Ground 331 | Flygon,Ground 332 | Cacnea,Grass 333 | Cacturne,Grass 334 | Swablu,Normal 335 | Altaria,Dragon 336 | Zangoose,Normal 337 | Seviper,Poison 338 | Lunatone,Rock 339 | Solrock,Rock 340 | Barboach,Water 341 | Whiscash,Water 342 | Corphish,Water 343 | Crawdaunt,Water 344 | Baltoy,Ground 345 | Claydol,Ground 346 | Lileep,Rock 347 | Cradily,Rock 348 | Anorith,Rock 349 | Armaldo,Rock 350 | Feebas,Water 351 | Milotic,Water 352 | Castform,Normal 353 | Kecleon,Normal 354 | Shuppet,Ghost 355 | Banette,Ghost 356 | Duskull,Ghost 357 | Dusclops,Ghost 358 | Tropius,Grass 359 | Chimecho,Psychic 360 | Absol,Dark 361 | Wynaut,Psychic 362 | Snorunt,Ice 363 | Glalie,Ice 364 | Spheal,Ice 365 | Sealeo,Ice 366 | Walrein,Ice 367 | Clamperl,Water 368 | Huntail,Water 369 | Gorebyss,Water 370 | Relicanth,Water 371 | Luvdisc,Water 372 | Bagon,Dragon 373 | Shelgon,Dragon 374 | Salamence,Dragon 375 | Beldum,Steel 376 | Metang,Steel 377 | Metagross,Steel 378 | Regirock,Rock 379 | Regice,Ice 380 | Registeel,Steel 381 | Latias,Dragon 382 | Latios,Dragon 383 | Kyogre,Water 384 | Groudon,Ground 385 | Rayquaza,Dragon 386 | Jirachi,Steel 387 | Deoxys,Psychic 388 | Turtwig,Grass 389 | Grotle,Grass 390 | Torterra,Grass 391 | Chimchar,Fire 392 | Monferno,Fire 393 | Infernape,Fire 394 | Piplup,Water 395 | Prinplup,Water 396 | Empoleon,Water 397 | Starly,Normal 398 | Staravia,Normal 399 | Staraptor,Normal 400 | Bidoof,Normal 401 | Bibarel,Normal 402 | Kricketot,Bug 403 | Kricketune,Bug 404 | Shinx,Electric 405 | Luxio,Electric 406 | Luxray,Electric 407 | Budew,Grass 408 | Roserade,Grass 409 | Cranidos,Rock 410 | Rampardos,Rock 411 | Shieldon,Rock 412 | Bastiodon,Rock 413 | Burmy,Bug 414 | Wormadam,Bug 415 | Mothim,Bug 416 | Combee,Bug 417 | Vespiquen,Bug 418 | Pachirisu,Electric 419 | Buizel,Water 420 | Floatzel,Water 421 | Cherubi,Grass 422 | Cherrim,Grass 423 | Shellos,Water 424 | Gastrodon,Water 425 | Ambipom,Normal 426 | Drifloon,Ghost 427 | Drifblim,Ghost 428 | Buneary,Normal 429 | Lopunny,Normal 430 | Mismagius,Ghost 431 | Honchkrow,Dark 432 | Glameow,Normal 433 | Purugly,Normal 434 | Chingling,Psychic 435 | Stunky,Poison 436 | Skuntank,Poison 437 | Bronzor,Steel 438 | Bronzong,Steel 439 | Bonsly,Rock 440 | Mime Jr.,Psychic 441 | Happiny,Normal 442 | Chatot,Normal 443 | Spiritomb,Ghost 444 | Gible,Dragon 445 | Gabite,Dragon 446 | Garchomp,Dragon 447 | Munchlax,Normal 448 | Riolu,Fighting 449 | Lucario,Fighting 450 | Hippopotas,Ground 451 | Hippowdon,Ground 452 | Skorupi,Poison 453 | Drapion,Poison 454 | Croagunk,Poison 455 | Toxicroak,Poison 456 | Carnivine,Grass 457 | Finneon,Water 458 | Lumineon,Water 459 | Mantyke,Water 460 | Snover,Grass 461 | Abomasnow,Grass 462 | Weavile,Dark 463 | Magnezone,Electric 464 | Lickilicky,Normal 465 | Rhyperior,Ground 466 | Tangrowth,Grass 467 | Electivire,Electric 468 | Magmortar,Fire 469 | Togekiss,Fairy 470 | Yanmega,Bug 471 | Leafeon,Grass 472 | Glaceon,Ice 473 | Gliscor,Ground 474 | Mamoswine,Ice 475 | Porygon-Z,Normal 476 | Gallade,Psychic 477 | Probopass,Rock 478 | Dusknoir,Ghost 479 | Froslass,Ice 480 | Rotom,Electric 481 | Uxie,Psychic 482 | Mesprit,Psychic 483 | Azelf,Psychic 484 | Dialga,Steel 485 | Palkia,Water 486 | Heatran,Fire 487 | Regigigas,Normal 488 | Giratina,Ghost 489 | Cresselia,Psychic 490 | Phione,Water 491 | Manaphy,Water 492 | Darkrai,Dark 493 | Shaymin,Grass 494 | Arceus,Normal 495 | Victini,Psychic 496 | Snivy,Grass 497 | Servine,Grass 498 | Serperior,Grass 499 | Tepig,Fire 500 | Pignite,Fire 501 | Emboar,Fire 502 | Oshawott,Water 503 | Dewott,Water 504 | Samurott,Water 505 | Patrat,Normal 506 | Watchog,Normal 507 | Lillipup,Normal 508 | Herdier,Normal 509 | Stoutland,Normal 510 | Purrloin,Dark 511 | Liepard,Dark 512 | Pansage,Grass 513 | Simisage,Grass 514 | Pansear,Fire 515 | Simisear,Fire 516 | Panpour,Water 517 | Simipour,Water 518 | Munna,Psychic 519 | Musharna,Psychic 520 | Pidove,Normal 521 | Tranquill,Normal 522 | Unfezant,Normal 523 | Blitzle,Electric 524 | Zebstrika,Electric 525 | Roggenrola,Rock 526 | Boldore,Rock 527 | Gigalith,Rock 528 | Woobat,Psychic 529 | Swoobat,Psychic 530 | Drilbur,Ground 531 | Excadrill,Ground 532 | Audino,Normal 533 | Timburr,Fighting 534 | Gurdurr,Fighting 535 | Conkeldurr,Fighting 536 | Tympole,Water 537 | Palpitoad,Water 538 | Seismitoad,Water 539 | Throh,Fighting 540 | Sawk,Fighting 541 | Sewaddle,Bug 542 | Swadloon,Bug 543 | Leavanny,Bug 544 | Venipede,Bug 545 | Whirlipede,Bug 546 | Scolipede,Bug 547 | Cottonee,Grass 548 | Whimsicott,Grass 549 | Petilil,Grass 550 | Lilligant,Grass 551 | Basculin,Water 552 | Sandile,Ground 553 | Krokorok,Ground 554 | Krookodile,Ground 555 | Darumaka,Fire 556 | Darmanitan,Fire 557 | Maractus,Grass 558 | Dwebble,Bug 559 | Crustle,Bug 560 | Scraggy,Dark 561 | Scrafty,Dark 562 | Sigilyph,Psychic 563 | Yamask,Ghost 564 | Cofagrigus,Ghost 565 | Tirtouga,Water 566 | Carracosta,Water 567 | Archen,Rock 568 | Archeops,Rock 569 | Trubbish,Poison 570 | Garbodor,Poison 571 | Zorua,Dark 572 | Zoroark,Dark 573 | Minccino,Normal 574 | Cinccino,Normal 575 | Gothita,Psychic 576 | Gothorita,Psychic 577 | Gothitelle,Psychic 578 | Solosis,Psychic 579 | Duosion,Psychic 580 | Reuniclus,Psychic 581 | Ducklett,Water 582 | Swanna,Water 583 | Vanillite,Ice 584 | Vanillish,Ice 585 | Vanilluxe,Ice 586 | Deerling,Normal 587 | Sawsbuck,Normal 588 | Emolga,Electric 589 | Karrablast,Bug 590 | Escavalier,Bug 591 | Foongus,Grass 592 | Amoonguss,Grass 593 | Frillish,Water 594 | Jellicent,Water 595 | Alomomola,Water 596 | Joltik,Bug 597 | Galvantula,Bug 598 | Ferroseed,Grass 599 | Ferrothorn,Grass 600 | Klink,Steel 601 | Klang,Steel 602 | Klinklang,Steel 603 | Tynamo,Electric 604 | Eelektrik,Electric 605 | Eelektross,Electric 606 | Elgyem,Psychic 607 | Beheeyem,Psychic 608 | Litwick,Ghost 609 | Lampent,Ghost 610 | Chandelure,Ghost 611 | Axew,Dragon 612 | Fraxure,Dragon 613 | Haxorus,Dragon 614 | Cubchoo,Ice 615 | Beartic,Ice 616 | Cryogonal,Ice 617 | Shelmet,Bug 618 | Accelgor,Bug 619 | Stunfisk,Ground 620 | Mienfoo,Fighting 621 | Mienshao,Fighting 622 | Druddigon,Dragon 623 | Golett,Ground 624 | Golurk,Ground 625 | Pawniard,Dark 626 | Bisharp,Dark 627 | Bouffalant,Normal 628 | Rufflet,Normal 629 | Braviary,Normal 630 | Vullaby,Dark 631 | Mandibuzz,Dark 632 | Heatmor,Fire 633 | Durant,Bug 634 | Deino,Dark 635 | Zweilous,Dark 636 | Hydreigon,Dark 637 | Larvesta,Bug 638 | Volcarona,Bug 639 | Cobalion,Steel 640 | Terrakion,Rock 641 | Virizion,Grass 642 | Tornadus,Flying 643 | Thundurus,Electric 644 | Reshiram,Dragon 645 | Zekrom,Dragon 646 | Landorus,Ground 647 | Kyurem,Dragon 648 | Keldeo,Water 649 | Meloetta,Normal 650 | Genesect,Bug 651 | Chespin,Grass 652 | Quilladin,Grass 653 | Chesnaught,Grass 654 | Fennekin,Fire 655 | Braixen,Fire 656 | Delphox,Fire 657 | Froakie,Water 658 | Frogadier,Water 659 | Greninja,Water 660 | Bunnelby,Normal 661 | Diggersby,Normal 662 | Fletchling,Normal 663 | Fletchinder,Fire 664 | Talonflame,Fire 665 | Scatterbug,Bug 666 | Spewpa,Bug 667 | Vivillon,Bug 668 | Litleo,Fire 669 | Pyroar,Fire 670 | Flabébé,Fairy 671 | Floette,Fairy 672 | Florges,Fairy 673 | Skiddo,Grass 674 | Gogoat,Grass 675 | Pancham,Fighting 676 | Pangoro,Fighting 677 | Furfrou,Normal 678 | Espurr,Psychic 679 | Meowstic,Psychic 680 | Honedge,Steel 681 | Doublade,Steel 682 | Aegislash,Steel 683 | Spritzee,Fairy 684 | Aromatisse,Fairy 685 | Swirlix,Fairy 686 | Slurpuff,Fairy 687 | Inkay,Dark 688 | Malamar,Dark 689 | Binacle,Rock 690 | Barbaracle,Rock 691 | Skrelp,Poison 692 | Dragalge,Poison 693 | Clauncher,Water 694 | Clawitzer,Water 695 | Helioptile,Electric 696 | Heliolisk,Electric 697 | Tyrunt,Rock 698 | Tyrantrum,Rock 699 | Amaura,Rock 700 | Aurorus,Rock 701 | Sylveon,Fairy 702 | Hawlucha,Fighting 703 | Dedenne,Electric 704 | Carbink,Rock 705 | Goomy,Dragon 706 | Sliggoo,Dragon 707 | Goodra,Dragon 708 | Klefki,Steel 709 | Phantump,Ghost 710 | Trevenant,Ghost 711 | Pumpkaboo,Ghost 712 | Gourgeist,Ghost 713 | Bergmite,Ice 714 | Avalugg,Ice 715 | Noibat,Flying 716 | Noivern,Flying 717 | Xerneas,Fairy 718 | Yveltal,Dark 719 | Zygarde,Dragon 720 | Diancie,Rock 721 | Hoopa,Psychic 722 | Volcanion,Fire 723 | -------------------------------------------------------------------------------- /pyplot-maps.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 6, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import plotly.plotly as py\n", 10 | "import pandas as pd\n", 11 | "import plotly \n", 12 | "from plotly.graph_objs import *\n", 13 | "\n", 14 | "\n", 15 | "df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')\n", 16 | "df_airports.head()\n", 17 | "\n", 18 | "df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')\n", 19 | "df_flight_paths.head()\n", 20 | "\n", 21 | "airports = [ dict(\n", 22 | " type = 'scattergeo',\n", 23 | " locationmode = 'USA-states',\n", 24 | " lon = df_airports['long'],\n", 25 | " lat = df_airports['lat'],\n", 26 | " hoverinfo = 'text',\n", 27 | " text = df_airports['airport'],\n", 28 | " mode = 'markers',\n", 29 | " marker = dict( \n", 30 | " size=2, \n", 31 | " color='rgb(255, 0, 0)',\n", 32 | " line = dict(\n", 33 | " width=3,\n", 34 | " color='rgba(68, 68, 68, 0)'\n", 35 | " )\n", 36 | " ))]\n", 37 | " \n", 38 | "flight_paths = []\n", 39 | "for i in range( len( df_flight_paths ) ):\n", 40 | " flight_paths.append(\n", 41 | " dict(\n", 42 | " type = 'scattergeo',\n", 43 | " locationmode = 'USA-states',\n", 44 | " lon = [ df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i] ],\n", 45 | " lat = [ df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i] ],\n", 46 | " mode = 'lines',\n", 47 | " line = dict(\n", 48 | " width = 1,\n", 49 | " color = 'red',\n", 50 | " ),\n", 51 | " opacity = float(df_flight_paths['cnt'][i])/float(df_flight_paths['cnt'].max()),\n", 52 | " )\n", 53 | " )\n", 54 | " \n", 55 | "layout = dict(\n", 56 | " title = 'Feb. 2011 American Airline flight paths
(Hover for airport names)',\n", 57 | " showlegend = False, \n", 58 | " geo = dict(\n", 59 | " scope='north america',\n", 60 | " projection=dict( type='azimuthal equal area' ),\n", 61 | " showland = True,\n", 62 | " landcolor = 'rgb(243, 243, 243)',\n", 63 | " countrycolor = 'rgb(204, 204, 204)',\n", 64 | " ),\n", 65 | " )\n", 66 | " \n", 67 | "fig = dict( data=flight_paths + airports, layout=layout )\n", 68 | "plot_url = plotly.offline.plot(fig)" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": null, 74 | "metadata": { 75 | "collapsed": true 76 | }, 77 | "outputs": [], 78 | "source": [] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": { 84 | "collapsed": true 85 | }, 86 | "outputs": [], 87 | "source": [] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": { 93 | "collapsed": true 94 | }, 95 | "outputs": [], 96 | "source": [] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": null, 101 | "metadata": { 102 | "collapsed": true 103 | }, 104 | "outputs": [], 105 | "source": [] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": { 111 | "collapsed": true 112 | }, 113 | "outputs": [], 114 | "source": [] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": null, 119 | "metadata": { 120 | "collapsed": true 121 | }, 122 | "outputs": [], 123 | "source": [] 124 | } 125 | ], 126 | "metadata": { 127 | "kernelspec": { 128 | "display_name": "Python 3", 129 | "language": "python", 130 | "name": "python3" 131 | }, 132 | "language_info": { 133 | "codemirror_mode": { 134 | "name": "ipython", 135 | "version": 3 136 | }, 137 | "file_extension": ".py", 138 | "mimetype": "text/x-python", 139 | "name": "python", 140 | "nbconvert_exporter": "python", 141 | "pygments_lexer": "ipython3", 142 | "version": "3.6.4" 143 | } 144 | }, 145 | "nbformat": 4, 146 | "nbformat_minor": 2 147 | } 148 | -------------------------------------------------------------------------------- /quarters.csv: -------------------------------------------------------------------------------- 1 | Salesman,Q1,Q2,Q3,Q4 2 | Boris,602908,233879,354479,32704 3 | Bob,43790,514863,297151,544493 4 | Tommy,392668,113579,430882,247231 5 | Travis,834663,266785,749238,570524 6 | Donald,580935,411379,110390,651572 7 | Ted,656644,70803,375948,321388 8 | Jeb,486141,600753,742716,404995 9 | Stacy,479662,742806,770712,2501 10 | Morgan,992673,879183,37945,293710 -------------------------------------------------------------------------------- /revenue.csv: -------------------------------------------------------------------------------- 1 | Date,New York,Los Angeles,Miami 2 | 1/1/16,985,122,499 3 | 1/2/16,738,788,534 4 | 1/3/16,14,20,933 5 | 1/4/16,730,904,885 6 | 1/5/16,114,71,253 7 | 1/6/16,936,502,497 8 | 1/7/16,123,996,115 9 | 1/8/16,935,492,886 10 | 1/9/16,846,954,823 11 | 1/10/16,54,285,216 --------------------------------------------------------------------------------