├── MasteringPandas-chap3_DataStructures.ipynb
├── MasteringPandas-chap4_Indexing.ipynb
├── MasteringPandas-chap5_Grouping_Merging_Reshaping.ipynb
├── MasteringPandas-chap6_MissingDataTimeSeriesPlotting.ipynb
├── MasteringPandas_chap11_MachineLearning.ipynb
├── MasteringPandas_chap7-ClassicalStatistics.ipynb
├── MasteringPandas_chap8-BayesianStatistics.ipynb
└── README.md


/MasteringPandas-chap5_Grouping_Merging_Reshaping.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 numpy as np\n",
  12 |     "import pandas as pd"
  13 |    ]
  14 |   },
  15 |   {
  16 |    "cell_type": "markdown",
  17 |    "metadata": {},
  18 |    "source": [
  19 |     "<h1>Grouping of Data</h1>"
  20 |    ]
  21 |   },
  22 |   {
  23 |    "cell_type": "markdown",
  24 |    "metadata": {},
  25 |    "source": [
  26 |     "<h2>groupby Operation</h2>"
  27 |    ]
  28 |   },
  29 |   {
  30 |    "cell_type": "markdown",
  31 |    "metadata": {},
  32 |    "source": [
  33 |     "<b>Read in and examine data </b>"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "code",
  38 |    "execution_count": 5,
  39 |    "metadata": {
  40 |     "collapsed": false
  41 |    },
  42 |    "outputs": [],
  43 |    "source": [
  44 |     "uefaDF=pd.read_csv('./data/euro_winners.csv')"
  45 |    ]
  46 |   },
  47 |   {
  48 |    "cell_type": "code",
  49 |    "execution_count": 6,
  50 |    "metadata": {
  51 |     "collapsed": false
  52 |    },
  53 |    "outputs": [
  54 |     {
  55 |      "data": {
  56 |       "text/html": [
  57 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
  58 |        "<table border=\"1\" class=\"dataframe\">\n",
  59 |        "  <thead>\n",
  60 |        "    <tr style=\"text-align: right;\">\n",
  61 |        "      <th></th>\n",
  62 |        "      <th>Season</th>\n",
  63 |        "      <th>Nation</th>\n",
  64 |        "      <th>Winners</th>\n",
  65 |        "      <th>Score</th>\n",
  66 |        "      <th>Runners-up</th>\n",
  67 |        "      <th>Runner-UpNation</th>\n",
  68 |        "      <th>Venue</th>\n",
  69 |        "      <th>Attendance</th>\n",
  70 |        "    </tr>\n",
  71 |        "  </thead>\n",
  72 |        "  <tbody>\n",
  73 |        "    <tr>\n",
  74 |        "      <th>0</th>\n",
  75 |        "      <td> 1955–56</td>\n",
  76 |        "      <td> Spain</td>\n",
  77 |        "      <td> Real Madrid</td>\n",
  78 |        "      <td> 4–3</td>\n",
  79 |        "      <td>      Stade de Reims</td>\n",
  80 |        "      <td>  France</td>\n",
  81 |        "      <td>            Parc des Princes,Paris</td>\n",
  82 |        "      <td>  38239</td>\n",
  83 |        "    </tr>\n",
  84 |        "    <tr>\n",
  85 |        "      <th>1</th>\n",
  86 |        "      <td> 1956–57</td>\n",
  87 |        "      <td> Spain</td>\n",
  88 |        "      <td> Real Madrid</td>\n",
  89 |        "      <td> 2–0</td>\n",
  90 |        "      <td>          Fiorentina</td>\n",
  91 |        "      <td>   Italy</td>\n",
  92 |        "      <td> Santiago Bernabéu Stadium, Madrid</td>\n",
  93 |        "      <td> 124000</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>2</th>\n",
  97 |        "      <td> 1957–58</td>\n",
  98 |        "      <td> Spain</td>\n",
  99 |        "      <td> Real Madrid</td>\n",
 100 |        "      <td> 3–2</td>\n",
 101 |        "      <td>               Milan</td>\n",
 102 |        "      <td>   Italy</td>\n",
 103 |        "      <td>           Heysel Stadium,Brussels</td>\n",
 104 |        "      <td>  67000</td>\n",
 105 |        "    </tr>\n",
 106 |        "    <tr>\n",
 107 |        "      <th>3</th>\n",
 108 |        "      <td> 1958–59</td>\n",
 109 |        "      <td> Spain</td>\n",
 110 |        "      <td> Real Madrid</td>\n",
 111 |        "      <td> 2–0</td>\n",
 112 |        "      <td>      Stade de Reims</td>\n",
 113 |        "      <td>  France</td>\n",
 114 |        "      <td>           Neckarstadion,Stuttgart</td>\n",
 115 |        "      <td>  72000</td>\n",
 116 |        "    </tr>\n",
 117 |        "    <tr>\n",
 118 |        "      <th>4</th>\n",
 119 |        "      <td> 1959–60</td>\n",
 120 |        "      <td> Spain</td>\n",
 121 |        "      <td> Real Madrid</td>\n",
 122 |        "      <td> 7–3</td>\n",
 123 |        "      <td> Eintracht Frankfurt</td>\n",
 124 |        "      <td> Germany</td>\n",
 125 |        "      <td>              Hampden Park,Glasgow</td>\n",
 126 |        "      <td> 127621</td>\n",
 127 |        "    </tr>\n",
 128 |        "  </tbody>\n",
 129 |        "</table>\n",
 130 |        "</div>"
 131 |       ],
 132 |       "text/plain": [
 133 |        "    Season Nation      Winners Score           Runners-up Runner-UpNation  \\\n",
 134 |        "0  1955–56  Spain  Real Madrid   4–3       Stade de Reims          France   \n",
 135 |        "1  1956–57  Spain  Real Madrid   2–0           Fiorentina           Italy   \n",
 136 |        "2  1957–58  Spain  Real Madrid   3–2                Milan           Italy   \n",
 137 |        "3  1958–59  Spain  Real Madrid   2–0       Stade de Reims          France   \n",
 138 |        "4  1959–60  Spain  Real Madrid   7–3  Eintracht Frankfurt         Germany   \n",
 139 |        "\n",
 140 |        "                               Venue  Attendance  \n",
 141 |        "0             Parc des Princes,Paris       38239  \n",
 142 |        "1  Santiago Bernabéu Stadium, Madrid      124000  \n",
 143 |        "2            Heysel Stadium,Brussels       67000  \n",
 144 |        "3            Neckarstadion,Stuttgart       72000  \n",
 145 |        "4               Hampden Park,Glasgow      127621  "
 146 |       ]
 147 |      },
 148 |      "execution_count": 6,
 149 |      "metadata": {},
 150 |      "output_type": "execute_result"
 151 |     }
 152 |    ],
 153 |    "source": [
 154 |     "uefaDF.head()"
 155 |    ]
 156 |   },
 157 |   {
 158 |    "cell_type": "markdown",
 159 |    "metadata": {},
 160 |    "source": [
 161 |     "<b>Rank nations by number of club championships</b>"
 162 |    ]
 163 |   },
 164 |   {
 165 |    "cell_type": "code",
 166 |    "execution_count": 8,
 167 |    "metadata": {
 168 |     "collapsed": false
 169 |    },
 170 |    "outputs": [
 171 |     {
 172 |      "data": {
 173 |       "text/plain": [
 174 |        "pandas.core.groupby.DataFrameGroupBy"
 175 |       ]
 176 |      },
 177 |      "execution_count": 8,
 178 |      "metadata": {},
 179 |      "output_type": "execute_result"
 180 |     }
 181 |    ],
 182 |    "source": [
 183 |     "nationsGrp =uefaDF.groupby('Nation')\n",
 184 |     "type(nationsGrp)"
 185 |    ]
 186 |   },
 187 |   {
 188 |    "cell_type": "code",
 189 |    "execution_count": 9,
 190 |    "metadata": {
 191 |     "collapsed": false
 192 |    },
 193 |    "outputs": [
 194 |     {
 195 |      "data": {
 196 |       "text/plain": [
 197 |        "{'England': [12, 21, 22, 23, 24, 25, 26, 28, 43, 49, 52, 56],\n",
 198 |        " 'France': [37],\n",
 199 |        " 'Germany': [18, 19, 20, 27, 41, 45, 57],\n",
 200 |        " 'Italy': [7, 8, 9, 13, 29, 33, 34, 38, 40, 47, 51, 54],\n",
 201 |        " 'Netherlands': [14, 15, 16, 17, 32, 39],\n",
 202 |        " 'Portugal': [5, 6, 31, 48],\n",
 203 |        " 'Romania': [30],\n",
 204 |        " 'Scotland': [11],\n",
 205 |        " 'Spain': [0, 1, 2, 3, 4, 10, 36, 42, 44, 46, 50, 53, 55],\n",
 206 |        " 'Yugoslavia': [35]}"
 207 |       ]
 208 |      },
 209 |      "execution_count": 9,
 210 |      "metadata": {},
 211 |      "output_type": "execute_result"
 212 |     }
 213 |    ],
 214 |    "source": [
 215 |     "nationsGrp.groups"
 216 |    ]
 217 |   },
 218 |   {
 219 |    "cell_type": "markdown",
 220 |    "metadata": {},
 221 |    "source": [
 222 |     "<b>Obtain number of groups</b>"
 223 |    ]
 224 |   },
 225 |   {
 226 |    "cell_type": "code",
 227 |    "execution_count": 10,
 228 |    "metadata": {
 229 |     "collapsed": false
 230 |    },
 231 |    "outputs": [
 232 |     {
 233 |      "data": {
 234 |       "text/plain": [
 235 |        "10"
 236 |       ]
 237 |      },
 238 |      "execution_count": 10,
 239 |      "metadata": {},
 240 |      "output_type": "execute_result"
 241 |     }
 242 |    ],
 243 |    "source": [
 244 |     "len(nationsGrp.groups)"
 245 |    ]
 246 |   },
 247 |   {
 248 |    "cell_type": "markdown",
 249 |    "metadata": {},
 250 |    "source": [
 251 |     "<b>Display the number of wins by each nation in descending order:</b>"
 252 |    ]
 253 |   },
 254 |   {
 255 |    "cell_type": "code",
 256 |    "execution_count": 11,
 257 |    "metadata": {
 258 |     "collapsed": true
 259 |    },
 260 |    "outputs": [],
 261 |    "source": [
 262 |     "nationWins=nationsGrp.size()"
 263 |    ]
 264 |   },
 265 |   {
 266 |    "cell_type": "code",
 267 |    "execution_count": 12,
 268 |    "metadata": {
 269 |     "collapsed": false
 270 |    },
 271 |    "outputs": [
 272 |     {
 273 |      "data": {
 274 |       "text/plain": [
 275 |        "Nation\n",
 276 |        "Spain          13\n",
 277 |        "Italy          12\n",
 278 |        "England        12\n",
 279 |        "Germany         7\n",
 280 |        "Netherlands     6\n",
 281 |        "Portugal        4\n",
 282 |        "Yugoslavia      1\n",
 283 |        "Scotland        1\n",
 284 |        "Romania         1\n",
 285 |        "France          1\n",
 286 |        "dtype: int64"
 287 |       ]
 288 |      },
 289 |      "execution_count": 12,
 290 |      "metadata": {},
 291 |      "output_type": "execute_result"
 292 |     }
 293 |    ],
 294 |    "source": [
 295 |     "nationWins.sort(ascending=False);nationWins"
 296 |    ]
 297 |   },
 298 |   {
 299 |    "cell_type": "markdown",
 300 |    "metadata": {},
 301 |    "source": []
 302 |   },
 303 |   {
 304 |    "cell_type": "code",
 305 |    "execution_count": 14,
 306 |    "metadata": {
 307 |     "collapsed": false
 308 |    },
 309 |    "outputs": [
 310 |     {
 311 |      "data": {
 312 |       "text/plain": [
 313 |        "Nation       Winners          \n",
 314 |        "Spain        Real Madrid          9\n",
 315 |        "Italy        Milan                7\n",
 316 |        "Germany      Bayern Munich        5\n",
 317 |        "England      Liverpool            5\n",
 318 |        "Spain        Barcelona            4\n",
 319 |        "Netherlands  Ajax                 4\n",
 320 |        "England      Manchester United    3\n",
 321 |        "Italy        Internazionale       3\n",
 322 |        "             Juventus             2\n",
 323 |        "Portugal     Porto                2\n",
 324 |        "             Benfica              2\n",
 325 |        "England      Nottingham Forest    2\n",
 326 |        "             Chelsea              1\n",
 327 |        "France       Marseille            1\n",
 328 |        "Yugoslavia   Red Star Belgrade    1\n",
 329 |        "Germany      Borussia Dortmund    1\n",
 330 |        "             Hamburg              1\n",
 331 |        "Netherlands  Feyenoord            1\n",
 332 |        "             PSV Eindhoven        1\n",
 333 |        "Romania      Steaua Bucure?ti     1\n",
 334 |        "Scotland     Celtic               1\n",
 335 |        "England      Aston Villa          1\n",
 336 |        "dtype: int64"
 337 |       ]
 338 |      },
 339 |      "execution_count": 14,
 340 |      "metadata": {},
 341 |      "output_type": "execute_result"
 342 |     }
 343 |    ],
 344 |    "source": [
 345 |     "winnersGrp=uefaDF.groupby(['Nation','Winners'])\n",
 346 |     "clubWins=winnersGrp.size()\n",
 347 |     "clubWins.sort(ascending=False)\n",
 348 |     "clubWins\n"
 349 |    ]
 350 |   },
 351 |   {
 352 |    "cell_type": "markdown",
 353 |    "metadata": {},
 354 |    "source": [
 355 |     "<b>Read in goal stats dataset</b>"
 356 |    ]
 357 |   },
 358 |   {
 359 |    "cell_type": "code",
 360 |    "execution_count": 17,
 361 |    "metadata": {
 362 |     "collapsed": true
 363 |    },
 364 |    "outputs": [],
 365 |    "source": [
 366 |     "goalStatsDF=pd.read_csv('./data/goal_stats_euro_leagues_2012-13.csv')\n",
 367 |     "goalStatsDF=goalStatsDF.set_index('Month')\n"
 368 |    ]
 369 |   },
 370 |   {
 371 |    "cell_type": "markdown",
 372 |    "metadata": {},
 373 |    "source": [
 374 |     "<b>Snapshot of dataset:</b>"
 375 |    ]
 376 |   },
 377 |   {
 378 |    "cell_type": "code",
 379 |    "execution_count": 18,
 380 |    "metadata": {
 381 |     "collapsed": false
 382 |    },
 383 |    "outputs": [
 384 |     {
 385 |      "data": {
 386 |       "text/html": [
 387 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 388 |        "<table border=\"1\" class=\"dataframe\">\n",
 389 |        "  <thead>\n",
 390 |        "    <tr style=\"text-align: right;\">\n",
 391 |        "      <th></th>\n",
 392 |        "      <th>Stat</th>\n",
 393 |        "      <th>EPL</th>\n",
 394 |        "      <th>La Liga</th>\n",
 395 |        "      <th>Serie A</th>\n",
 396 |        "      <th>Bundesliga</th>\n",
 397 |        "    </tr>\n",
 398 |        "    <tr>\n",
 399 |        "      <th>Month</th>\n",
 400 |        "      <th></th>\n",
 401 |        "      <th></th>\n",
 402 |        "      <th></th>\n",
 403 |        "      <th></th>\n",
 404 |        "      <th></th>\n",
 405 |        "    </tr>\n",
 406 |        "  </thead>\n",
 407 |        "  <tbody>\n",
 408 |        "    <tr>\n",
 409 |        "      <th>08/01/2012</th>\n",
 410 |        "      <td> MatchesPlayed</td>\n",
 411 |        "      <td> 20</td>\n",
 412 |        "      <td> 20</td>\n",
 413 |        "      <td> 10</td>\n",
 414 |        "      <td> 10</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>09/01/2012</th>\n",
 418 |        "      <td> MatchesPlayed</td>\n",
 419 |        "      <td> 38</td>\n",
 420 |        "      <td> 39</td>\n",
 421 |        "      <td> 50</td>\n",
 422 |        "      <td> 44</td>\n",
 423 |        "    </tr>\n",
 424 |        "    <tr>\n",
 425 |        "      <th>10/01/2012</th>\n",
 426 |        "      <td> MatchesPlayed</td>\n",
 427 |        "      <td> 31</td>\n",
 428 |        "      <td> 31</td>\n",
 429 |        "      <td> 39</td>\n",
 430 |        "      <td> 27</td>\n",
 431 |        "    </tr>\n",
 432 |        "  </tbody>\n",
 433 |        "</table>\n",
 434 |        "</div>"
 435 |       ],
 436 |       "text/plain": [
 437 |        "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 438 |        "Month                                                       \n",
 439 |        "08/01/2012  MatchesPlayed   20       20       10          10\n",
 440 |        "09/01/2012  MatchesPlayed   38       39       50          44\n",
 441 |        "10/01/2012  MatchesPlayed   31       31       39          27"
 442 |       ]
 443 |      },
 444 |      "execution_count": 18,
 445 |      "metadata": {},
 446 |      "output_type": "execute_result"
 447 |     }
 448 |    ],
 449 |    "source": [
 450 |     "goalStatsDF.head(3)\n"
 451 |    ]
 452 |   },
 453 |   {
 454 |    "cell_type": "code",
 455 |    "execution_count": 19,
 456 |    "metadata": {
 457 |     "collapsed": false
 458 |    },
 459 |    "outputs": [
 460 |     {
 461 |      "data": {
 462 |       "text/html": [
 463 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 464 |        "<table border=\"1\" class=\"dataframe\">\n",
 465 |        "  <thead>\n",
 466 |        "    <tr style=\"text-align: right;\">\n",
 467 |        "      <th></th>\n",
 468 |        "      <th>Stat</th>\n",
 469 |        "      <th>EPL</th>\n",
 470 |        "      <th>La Liga</th>\n",
 471 |        "      <th>Serie A</th>\n",
 472 |        "      <th>Bundesliga</th>\n",
 473 |        "    </tr>\n",
 474 |        "    <tr>\n",
 475 |        "      <th>Month</th>\n",
 476 |        "      <th></th>\n",
 477 |        "      <th></th>\n",
 478 |        "      <th></th>\n",
 479 |        "      <th></th>\n",
 480 |        "      <th></th>\n",
 481 |        "    </tr>\n",
 482 |        "  </thead>\n",
 483 |        "  <tbody>\n",
 484 |        "    <tr>\n",
 485 |        "      <th>04/01/2013</th>\n",
 486 |        "      <td> GoalsScored</td>\n",
 487 |        "      <td> 105</td>\n",
 488 |        "      <td> 127</td>\n",
 489 |        "      <td> 102</td>\n",
 490 |        "      <td> 104</td>\n",
 491 |        "    </tr>\n",
 492 |        "    <tr>\n",
 493 |        "      <th>05/01/2013</th>\n",
 494 |        "      <td> GoalsScored</td>\n",
 495 |        "      <td>  96</td>\n",
 496 |        "      <td> 109</td>\n",
 497 |        "      <td> 102</td>\n",
 498 |        "      <td>  92</td>\n",
 499 |        "    </tr>\n",
 500 |        "    <tr>\n",
 501 |        "      <th>06/01/2013</th>\n",
 502 |        "      <td> GoalsScored</td>\n",
 503 |        "      <td> NaN</td>\n",
 504 |        "      <td>  80</td>\n",
 505 |        "      <td> NaN</td>\n",
 506 |        "      <td> NaN</td>\n",
 507 |        "    </tr>\n",
 508 |        "  </tbody>\n",
 509 |        "</table>\n",
 510 |        "</div>"
 511 |       ],
 512 |       "text/plain": [
 513 |        "                   Stat  EPL  La Liga  Serie A  Bundesliga\n",
 514 |        "Month                                                     \n",
 515 |        "04/01/2013  GoalsScored  105      127      102         104\n",
 516 |        "05/01/2013  GoalsScored   96      109      102          92\n",
 517 |        "06/01/2013  GoalsScored  NaN       80      NaN         NaN"
 518 |       ]
 519 |      },
 520 |      "execution_count": 19,
 521 |      "metadata": {},
 522 |      "output_type": "execute_result"
 523 |     }
 524 |    ],
 525 |    "source": [
 526 |     "goalStatsDF.tail(3)"
 527 |    ]
 528 |   },
 529 |   {
 530 |    "cell_type": "markdown",
 531 |    "metadata": {},
 532 |    "source": [
 533 |     "<b>Display the stats, but by grouped year instead. </b>"
 534 |    ]
 535 |   },
 536 |   {
 537 |    "cell_type": "code",
 538 |    "execution_count": null,
 539 |    "metadata": {
 540 |     "collapsed": true
 541 |    },
 542 |    "outputs": [],
 543 |    "source": [
 544 |     "goalStatsGroupedByYear = goalStatsDF.groupby(lambda Month: Month.split('/')[2])\n"
 545 |    ]
 546 |   },
 547 |   {
 548 |    "cell_type": "code",
 549 |    "execution_count": 23,
 550 |    "metadata": {
 551 |     "collapsed": false
 552 |    },
 553 |    "outputs": [
 554 |     {
 555 |      "name": "stdout",
 556 |      "output_type": "stream",
 557 |      "text": [
 558 |       "2012\n",
 559 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 560 |       "Month                                                       \n",
 561 |       "08/01/2012  MatchesPlayed   20       20       10          10\n",
 562 |       "09/01/2012  MatchesPlayed   38       39       50          44\n",
 563 |       "10/01/2012  MatchesPlayed   31       31       39          27\n",
 564 |       "11/01/2012  MatchesPlayed   50       41       42          46\n",
 565 |       "12/01/2012  MatchesPlayed   59       39       39          26\n",
 566 |       "08/01/2012    GoalsScored   57       60       21          23\n",
 567 |       "09/01/2012    GoalsScored  111      112      133         135\n",
 568 |       "10/01/2012    GoalsScored   95       88       97          77\n",
 569 |       "11/01/2012    GoalsScored  121      116      120         137\n",
 570 |       "12/01/2012    GoalsScored  183      109      125          72\n",
 571 |       "2013\n",
 572 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 573 |       "Month                                                       \n",
 574 |       "01/01/2013  MatchesPlayed   42       40       40          18\n",
 575 |       "02/01/2013  MatchesPlayed   30       40       40          36\n",
 576 |       "03/01/2013  MatchesPlayed   35       38       39          36\n",
 577 |       "04/01/2013  MatchesPlayed   42       42       41          36\n",
 578 |       "05/01/2013  MatchesPlayed   33       40       40          27\n",
 579 |       "06/02/2013  MatchesPlayed  NaN       10      NaN         NaN\n",
 580 |       "01/01/2013    GoalsScored  117      121      104          51\n",
 581 |       "02/01/2013    GoalsScored   87      110      100         101\n",
 582 |       "03/01/2013    GoalsScored   91      101       99         106\n",
 583 |       "04/01/2013    GoalsScored  105      127      102         104\n",
 584 |       "05/01/2013    GoalsScored   96      109      102          92\n",
 585 |       "06/01/2013    GoalsScored  NaN       80      NaN         NaN\n"
 586 |      ]
 587 |     }
 588 |    ],
 589 |    "source": [
 590 |     "for name, group in goalStatsGroupedByYear:\n",
 591 |     "    \t      print name\n",
 592 |     "    \t      print group\n"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "markdown",
 597 |    "metadata": {},
 598 |    "source": [
 599 |     "<b>Group by individual month using level arg:</b>"
 600 |    ]
 601 |   },
 602 |   {
 603 |    "cell_type": "code",
 604 |    "execution_count": 24,
 605 |    "metadata": {
 606 |     "collapsed": true
 607 |    },
 608 |    "outputs": [],
 609 |    "source": [
 610 |     "goalStatsGroupedByMonth = goalStatsDF.groupby(level=0)"
 611 |    ]
 612 |   },
 613 |   {
 614 |    "cell_type": "code",
 615 |    "execution_count": 28,
 616 |    "metadata": {
 617 |     "collapsed": false
 618 |    },
 619 |    "outputs": [
 620 |     {
 621 |      "name": "stdout",
 622 |      "output_type": "stream",
 623 |      "text": [
 624 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 625 |       "Month                                                       \n",
 626 |       "01/01/2013  MatchesPlayed   42       40       40          18\n",
 627 |       "01/01/2013    GoalsScored  117      121      104          51\n",
 628 |       "\n",
 629 |       "\n",
 630 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 631 |       "Month                                                       \n",
 632 |       "02/01/2013  MatchesPlayed   30       40       40          36\n",
 633 |       "02/01/2013    GoalsScored   87      110      100         101\n",
 634 |       "\n",
 635 |       "\n",
 636 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 637 |       "Month                                                       \n",
 638 |       "03/01/2013  MatchesPlayed   35       38       39          36\n",
 639 |       "03/01/2013    GoalsScored   91      101       99         106\n",
 640 |       "\n",
 641 |       "\n",
 642 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 643 |       "Month                                                       \n",
 644 |       "04/01/2013  MatchesPlayed   42       42       41          36\n",
 645 |       "04/01/2013    GoalsScored  105      127      102         104\n",
 646 |       "\n",
 647 |       "\n",
 648 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 649 |       "Month                                                       \n",
 650 |       "05/01/2013  MatchesPlayed   33       40       40          27\n",
 651 |       "05/01/2013    GoalsScored   96      109      102          92\n",
 652 |       "\n",
 653 |       "\n",
 654 |       "                   Stat  EPL  La Liga  Serie A  Bundesliga\n",
 655 |       "Month                                                     \n",
 656 |       "06/01/2013  GoalsScored  NaN       80      NaN         NaN\n",
 657 |       "\n",
 658 |       "\n",
 659 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 660 |       "Month                                                       \n",
 661 |       "06/02/2013  MatchesPlayed  NaN       10      NaN         NaN\n",
 662 |       "\n",
 663 |       "\n",
 664 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 665 |       "Month                                                       \n",
 666 |       "08/01/2012  MatchesPlayed   20       20       10          10\n",
 667 |       "08/01/2012    GoalsScored   57       60       21          23\n",
 668 |       "\n",
 669 |       "\n",
 670 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 671 |       "Month                                                       \n",
 672 |       "09/01/2012  MatchesPlayed   38       39       50          44\n",
 673 |       "09/01/2012    GoalsScored  111      112      133         135\n",
 674 |       "\n",
 675 |       "\n",
 676 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 677 |       "Month                                                       \n",
 678 |       "10/01/2012  MatchesPlayed   31       31       39          27\n",
 679 |       "10/01/2012    GoalsScored   95       88       97          77\n",
 680 |       "\n",
 681 |       "\n",
 682 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 683 |       "Month                                                       \n",
 684 |       "11/01/2012  MatchesPlayed   50       41       42          46\n",
 685 |       "11/01/2012    GoalsScored  121      116      120         137\n",
 686 |       "\n",
 687 |       "\n",
 688 |       "                     Stat  EPL  La Liga  Serie A  Bundesliga\n",
 689 |       "Month                                                       \n",
 690 |       "12/01/2012  MatchesPlayed   59       39       39          26\n",
 691 |       "12/01/2012    GoalsScored  183      109      125          72\n",
 692 |       "\n",
 693 |       "\n"
 694 |      ]
 695 |     }
 696 |    ],
 697 |    "source": [
 698 |     "for name, group in goalStatsGroupedByMonth:\n",
 699 |     "    #print name \n",
 700 |     "    print group\n",
 701 |     "    print \"\\n\"\n"
 702 |    ]
 703 |   },
 704 |   {
 705 |    "cell_type": "markdown",
 706 |    "metadata": {},
 707 |    "source": [
 708 |     "<b>group by multiple keys:</b>"
 709 |    ]
 710 |   },
 711 |   {
 712 |    "cell_type": "code",
 713 |    "execution_count": 39,
 714 |    "metadata": {
 715 |     "collapsed": false
 716 |    },
 717 |    "outputs": [],
 718 |    "source": [
 719 |     "goalStatsDF=goalStatsDF.reset_index()\n",
 720 |     "goalStatsDF=goalStatsDF.set_index(['Month','Stat'])\n",
 721 |     "\n",
 722 |     "\n"
 723 |    ]
 724 |   },
 725 |   {
 726 |    "cell_type": "code",
 727 |    "execution_count": 41,
 728 |    "metadata": {
 729 |     "collapsed": false
 730 |    },
 731 |    "outputs": [
 732 |     {
 733 |      "name": "stdout",
 734 |      "output_type": "stream",
 735 |      "text": [
 736 |       "('01/01/2013', 'GoalsScored')\n",
 737 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 738 |       "Month      Stat                                                          \n",
 739 |       "01/01/2013 GoalsScored       16     16  117      121      104          51\n",
 740 |       "('01/01/2013', 'MatchesPlayed')\n",
 741 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 742 |       "Month      Stat                                                            \n",
 743 |       "01/01/2013 MatchesPlayed        5      5   42       40       40          18\n",
 744 |       "('02/01/2013', 'GoalsScored')\n",
 745 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 746 |       "Month      Stat                                                          \n",
 747 |       "02/01/2013 GoalsScored       17     17   87      110      100         101\n",
 748 |       "('02/01/2013', 'MatchesPlayed')\n",
 749 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 750 |       "Month      Stat                                                            \n",
 751 |       "02/01/2013 MatchesPlayed        6      6   30       40       40          36\n",
 752 |       "('03/01/2013', 'GoalsScored')\n",
 753 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 754 |       "Month      Stat                                                          \n",
 755 |       "03/01/2013 GoalsScored       18     18   91      101       99         106\n",
 756 |       "('03/01/2013', 'MatchesPlayed')\n",
 757 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 758 |       "Month      Stat                                                            \n",
 759 |       "03/01/2013 MatchesPlayed        7      7   35       38       39          36\n",
 760 |       "('04/01/2013', 'GoalsScored')\n",
 761 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 762 |       "Month      Stat                                                          \n",
 763 |       "04/01/2013 GoalsScored       19     19  105      127      102         104\n",
 764 |       "('04/01/2013', 'MatchesPlayed')\n",
 765 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 766 |       "Month      Stat                                                            \n",
 767 |       "04/01/2013 MatchesPlayed        8      8   42       42       41          36\n",
 768 |       "('05/01/2013', 'GoalsScored')\n",
 769 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 770 |       "Month      Stat                                                          \n",
 771 |       "05/01/2013 GoalsScored       20     20   96      109      102          92\n",
 772 |       "('05/01/2013', 'MatchesPlayed')\n",
 773 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 774 |       "Month      Stat                                                            \n",
 775 |       "05/01/2013 MatchesPlayed        9      9   33       40       40          27\n",
 776 |       "('06/01/2013', 'GoalsScored')\n",
 777 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 778 |       "Month      Stat                                                          \n",
 779 |       "06/01/2013 GoalsScored       21     21  NaN       80      NaN         NaN\n",
 780 |       "('06/02/2013', 'MatchesPlayed')\n",
 781 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 782 |       "Month      Stat                                                            \n",
 783 |       "06/02/2013 MatchesPlayed       10     10  NaN       10      NaN         NaN\n",
 784 |       "('08/01/2012', 'GoalsScored')\n",
 785 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 786 |       "Month      Stat                                                          \n",
 787 |       "08/01/2012 GoalsScored       11     11   57       60       21          23\n",
 788 |       "('08/01/2012', 'MatchesPlayed')\n",
 789 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 790 |       "Month      Stat                                                            \n",
 791 |       "08/01/2012 MatchesPlayed        0      0   20       20       10          10\n",
 792 |       "('09/01/2012', 'GoalsScored')\n",
 793 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 794 |       "Month      Stat                                                          \n",
 795 |       "09/01/2012 GoalsScored       12     12  111      112      133         135\n",
 796 |       "('09/01/2012', 'MatchesPlayed')\n",
 797 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 798 |       "Month      Stat                                                            \n",
 799 |       "09/01/2012 MatchesPlayed        1      1   38       39       50          44\n",
 800 |       "('10/01/2012', 'GoalsScored')\n",
 801 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 802 |       "Month      Stat                                                          \n",
 803 |       "10/01/2012 GoalsScored       13     13   95       88       97          77\n",
 804 |       "('10/01/2012', 'MatchesPlayed')\n",
 805 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 806 |       "Month      Stat                                                            \n",
 807 |       "10/01/2012 MatchesPlayed        2      2   31       31       39          27\n",
 808 |       "('11/01/2012', 'GoalsScored')\n",
 809 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 810 |       "Month      Stat                                                          \n",
 811 |       "11/01/2012 GoalsScored       14     14  121      116      120         137\n",
 812 |       "('11/01/2012', 'MatchesPlayed')\n",
 813 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 814 |       "Month      Stat                                                            \n",
 815 |       "11/01/2012 MatchesPlayed        3      3   50       41       42          46\n",
 816 |       "('12/01/2012', 'GoalsScored')\n",
 817 |       "                        level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 818 |       "Month      Stat                                                          \n",
 819 |       "12/01/2012 GoalsScored       15     15  183      109      125          72\n",
 820 |       "('12/01/2012', 'MatchesPlayed')\n",
 821 |       "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
 822 |       "Month      Stat                                                            \n",
 823 |       "12/01/2012 MatchesPlayed        4      4   59       39       39          26\n"
 824 |      ]
 825 |     }
 826 |    ],
 827 |    "source": [
 828 |     "monthStatGroup=goalStatsDF.groupby(level=['Month','Stat'])\n",
 829 |     "for name, group in monthStatGroup:\n",
 830 |     "          print name\n",
 831 |     "          print group\n"
 832 |    ]
 833 |   },
 834 |   {
 835 |    "cell_type": "markdown",
 836 |    "metadata": {},
 837 |    "source": [
 838 |     "<h3>Using groupby with a MultiIndex</h3>"
 839 |    ]
 840 |   },
 841 |   {
 842 |    "cell_type": "markdown",
 843 |    "metadata": {},
 844 |    "source": [
 845 |     "<b>Read in the goal stats DataFrame again:</b>"
 846 |    ]
 847 |   },
 848 |   {
 849 |    "cell_type": "code",
 850 |    "execution_count": 43,
 851 |    "metadata": {
 852 |     "collapsed": false
 853 |    },
 854 |    "outputs": [],
 855 |    "source": [
 856 |     "goalStatsDF3=pd.read_csv('./data/goal_stats_euro_leagues_2012-13.csv')\n",
 857 |     "goalStatsDF3=goalStatsDF3.set_index(['Month','Stat'])\n"
 858 |    ]
 859 |   },
 860 |   {
 861 |    "cell_type": "code",
 862 |    "execution_count": 53,
 863 |    "metadata": {
 864 |     "collapsed": false
 865 |    },
 866 |    "outputs": [
 867 |     {
 868 |      "name": "stdout",
 869 |      "output_type": "stream",
 870 |      "text": [
 871 |       "                          EPL  La Liga  Serie A  Bundesliga\n",
 872 |       "Month      Stat                                            \n",
 873 |       "08/01/2012 MatchesPlayed   20       20       10          10\n",
 874 |       "09/01/2012 MatchesPlayed   38       39       50          44\n",
 875 |       "10/01/2012 MatchesPlayed   31       31       39          27\n",
 876 |       "                        EPL  La Liga  Serie A  Bundesliga\n",
 877 |       "Month      Stat                                          \n",
 878 |       "04/01/2013 GoalsScored  105      127      102         104\n",
 879 |       "05/01/2013 GoalsScored   96      109      102          92\n",
 880 |       "06/01/2013 GoalsScored  NaN       80      NaN         NaN\n"
 881 |      ]
 882 |     }
 883 |    ],
 884 |    "source": [
 885 |     "print goalStatsDF3.head(3)\n",
 886 |     "print goalStatsDF3.tail(3)"
 887 |    ]
 888 |   },
 889 |   {
 890 |    "cell_type": "markdown",
 891 |    "metadata": {},
 892 |    "source": [
 893 |     "<b>Compute the total number of goals scored and total matches played for the entire season for each league.</b>"
 894 |    ]
 895 |   },
 896 |   {
 897 |    "cell_type": "code",
 898 |    "execution_count": 47,
 899 |    "metadata": {
 900 |     "collapsed": false
 901 |    },
 902 |    "outputs": [
 903 |     {
 904 |      "data": {
 905 |       "text/html": [
 906 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 907 |        "<table border=\"1\" class=\"dataframe\">\n",
 908 |        "  <thead>\n",
 909 |        "    <tr style=\"text-align: right;\">\n",
 910 |        "      <th></th>\n",
 911 |        "      <th>EPL</th>\n",
 912 |        "      <th>La Liga</th>\n",
 913 |        "      <th>Serie A</th>\n",
 914 |        "      <th>Bundesliga</th>\n",
 915 |        "    </tr>\n",
 916 |        "    <tr>\n",
 917 |        "      <th>Stat</th>\n",
 918 |        "      <th></th>\n",
 919 |        "      <th></th>\n",
 920 |        "      <th></th>\n",
 921 |        "      <th></th>\n",
 922 |        "    </tr>\n",
 923 |        "  </thead>\n",
 924 |        "  <tbody>\n",
 925 |        "    <tr>\n",
 926 |        "      <th>GoalsScored</th>\n",
 927 |        "      <td> 1063</td>\n",
 928 |        "      <td> 1133</td>\n",
 929 |        "      <td> 1003</td>\n",
 930 |        "      <td> 898</td>\n",
 931 |        "    </tr>\n",
 932 |        "    <tr>\n",
 933 |        "      <th>MatchesPlayed</th>\n",
 934 |        "      <td>  380</td>\n",
 935 |        "      <td>  380</td>\n",
 936 |        "      <td>  380</td>\n",
 937 |        "      <td> 306</td>\n",
 938 |        "    </tr>\n",
 939 |        "  </tbody>\n",
 940 |        "</table>\n",
 941 |        "</div>"
 942 |       ],
 943 |       "text/plain": [
 944 |        "                EPL  La Liga  Serie A  Bundesliga\n",
 945 |        "Stat                                             \n",
 946 |        "GoalsScored    1063     1133     1003         898\n",
 947 |        "MatchesPlayed   380      380      380         306"
 948 |       ]
 949 |      },
 950 |      "execution_count": 47,
 951 |      "metadata": {},
 952 |      "output_type": "execute_result"
 953 |     }
 954 |    ],
 955 |    "source": [
 956 |     "grouped3=goalStatsDF3.groupby(level='Stat')\n",
 957 |     "grouped3.sum()"
 958 |    ]
 959 |   },
 960 |   {
 961 |    "cell_type": "markdown",
 962 |    "metadata": {},
 963 |    "source": [
 964 |     "<b>Use sum directly but pass level as parameter to achieve same result.</b>"
 965 |    ]
 966 |   },
 967 |   {
 968 |    "cell_type": "code",
 969 |    "execution_count": 45,
 970 |    "metadata": {
 971 |     "collapsed": false
 972 |    },
 973 |    "outputs": [
 974 |     {
 975 |      "data": {
 976 |       "text/html": [
 977 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 978 |        "<table border=\"1\" class=\"dataframe\">\n",
 979 |        "  <thead>\n",
 980 |        "    <tr style=\"text-align: right;\">\n",
 981 |        "      <th></th>\n",
 982 |        "      <th>EPL</th>\n",
 983 |        "      <th>La Liga</th>\n",
 984 |        "      <th>Serie A</th>\n",
 985 |        "      <th>Bundesliga</th>\n",
 986 |        "    </tr>\n",
 987 |        "    <tr>\n",
 988 |        "      <th>Stat</th>\n",
 989 |        "      <th></th>\n",
 990 |        "      <th></th>\n",
 991 |        "      <th></th>\n",
 992 |        "      <th></th>\n",
 993 |        "    </tr>\n",
 994 |        "  </thead>\n",
 995 |        "  <tbody>\n",
 996 |        "    <tr>\n",
 997 |        "      <th>GoalsScored</th>\n",
 998 |        "      <td> 1063</td>\n",
 999 |        "      <td> 1133</td>\n",
1000 |        "      <td> 1003</td>\n",
1001 |        "      <td> 898</td>\n",
1002 |        "    </tr>\n",
1003 |        "    <tr>\n",
1004 |        "      <th>MatchesPlayed</th>\n",
1005 |        "      <td>  380</td>\n",
1006 |        "      <td>  380</td>\n",
1007 |        "      <td>  380</td>\n",
1008 |        "      <td> 306</td>\n",
1009 |        "    </tr>\n",
1010 |        "  </tbody>\n",
1011 |        "</table>\n",
1012 |        "</div>"
1013 |       ],
1014 |       "text/plain": [
1015 |        "                EPL  La Liga  Serie A  Bundesliga\n",
1016 |        "Stat                                             \n",
1017 |        "GoalsScored    1063     1133     1003         898\n",
1018 |        "MatchesPlayed   380      380      380         306"
1019 |       ]
1020 |      },
1021 |      "execution_count": 45,
1022 |      "metadata": {},
1023 |      "output_type": "execute_result"
1024 |     }
1025 |    ],
1026 |    "source": [
1027 |     "goalStatsDF3.sum(level='Stat')"
1028 |    ]
1029 |   },
1030 |   {
1031 |    "cell_type": "markdown",
1032 |    "metadata": {},
1033 |    "source": [
1034 |     "<b>Calculate goals per game ratio</b>:"
1035 |    ]
1036 |   },
1037 |   {
1038 |    "cell_type": "code",
1039 |    "execution_count": 54,
1040 |    "metadata": {
1041 |     "collapsed": false
1042 |    },
1043 |    "outputs": [
1044 |     {
1045 |      "data": {
1046 |       "text/plain": [
1047 |        "EPL           2.797368\n",
1048 |        "La Liga       2.981579\n",
1049 |        "Serie A       2.639474\n",
1050 |        "Bundesliga    2.934641\n",
1051 |        "dtype: float64"
1052 |       ]
1053 |      },
1054 |      "execution_count": 54,
1055 |      "metadata": {},
1056 |      "output_type": "execute_result"
1057 |     }
1058 |    ],
1059 |    "source": [
1060 |     "totalsDF=grouped3.sum()\n",
1061 |     "totalsDF.ix['GoalsScored']/totalsDF.ix['MatchesPlayed']"
1062 |    ]
1063 |   },
1064 |   {
1065 |    "cell_type": "code",
1066 |    "execution_count": 55,
1067 |    "metadata": {
1068 |     "collapsed": false
1069 |    },
1070 |    "outputs": [
1071 |     {
1072 |      "data": {
1073 |       "text/html": [
1074 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1075 |        "<table border=\"1\" class=\"dataframe\">\n",
1076 |        "  <thead>\n",
1077 |        "    <tr style=\"text-align: right;\">\n",
1078 |        "      <th></th>\n",
1079 |        "      <th>EPL</th>\n",
1080 |        "      <th>La Liga</th>\n",
1081 |        "      <th>Serie A</th>\n",
1082 |        "      <th>Bundesliga</th>\n",
1083 |        "    </tr>\n",
1084 |        "  </thead>\n",
1085 |        "  <tbody>\n",
1086 |        "    <tr>\n",
1087 |        "      <th>0</th>\n",
1088 |        "      <td> 2.797368</td>\n",
1089 |        "      <td> 2.981579</td>\n",
1090 |        "      <td> 2.639474</td>\n",
1091 |        "      <td> 2.934641</td>\n",
1092 |        "    </tr>\n",
1093 |        "  </tbody>\n",
1094 |        "</table>\n",
1095 |        "</div>"
1096 |       ],
1097 |       "text/plain": [
1098 |        "        EPL   La Liga   Serie A  Bundesliga\n",
1099 |        "0  2.797368  2.981579  2.639474    2.934641"
1100 |       ]
1101 |      },
1102 |      "execution_count": 55,
1103 |      "metadata": {},
1104 |      "output_type": "execute_result"
1105 |     }
1106 |    ],
1107 |    "source": [
1108 |     "gpg=totalsDF.ix['GoalsScored']/totalsDF.ix['MatchesPlayed']\n",
1109 |     "goalsPerGameDF=pd.DataFrame(gpg).T\n",
1110 |     "goalsPerGameDF"
1111 |    ]
1112 |   },
1113 |   {
1114 |    "cell_type": "code",
1115 |    "execution_count": 56,
1116 |    "metadata": {
1117 |     "collapsed": false
1118 |    },
1119 |    "outputs": [
1120 |     {
1121 |      "data": {
1122 |       "text/html": [
1123 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1124 |        "<table border=\"1\" class=\"dataframe\">\n",
1125 |        "  <thead>\n",
1126 |        "    <tr style=\"text-align: right;\">\n",
1127 |        "      <th></th>\n",
1128 |        "      <th>EPL</th>\n",
1129 |        "      <th>La Liga</th>\n",
1130 |        "      <th>Serie A</th>\n",
1131 |        "      <th>Bundesliga</th>\n",
1132 |        "    </tr>\n",
1133 |        "  </thead>\n",
1134 |        "  <tbody>\n",
1135 |        "    <tr>\n",
1136 |        "      <th>GoalsPerGame</th>\n",
1137 |        "      <td> 2.797368</td>\n",
1138 |        "      <td> 2.981579</td>\n",
1139 |        "      <td> 2.639474</td>\n",
1140 |        "      <td> 2.934641</td>\n",
1141 |        "    </tr>\n",
1142 |        "  </tbody>\n",
1143 |        "</table>\n",
1144 |        "</div>"
1145 |       ],
1146 |       "text/plain": [
1147 |        "                   EPL   La Liga   Serie A  Bundesliga\n",
1148 |        "GoalsPerGame  2.797368  2.981579  2.639474    2.934641"
1149 |       ]
1150 |      },
1151 |      "execution_count": 56,
1152 |      "metadata": {},
1153 |      "output_type": "execute_result"
1154 |     }
1155 |    ],
1156 |    "source": [
1157 |     "goalsPerGameDF=goalsPerGameDF.rename(index={0:'GoalsPerGame'}) \n",
1158 |     "goalsPerGameDF"
1159 |    ]
1160 |   },
1161 |   {
1162 |    "cell_type": "markdown",
1163 |    "metadata": {},
1164 |    "source": [
1165 |     "<b>Format and append goalsPerGameDF to original DataFrame:</b>"
1166 |    ]
1167 |   },
1168 |   {
1169 |    "cell_type": "code",
1170 |    "execution_count": 57,
1171 |    "metadata": {
1172 |     "collapsed": false
1173 |    },
1174 |    "outputs": [
1175 |     {
1176 |      "data": {
1177 |       "text/html": [
1178 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1179 |        "<table border=\"1\" class=\"dataframe\">\n",
1180 |        "  <thead>\n",
1181 |        "    <tr style=\"text-align: right;\">\n",
1182 |        "      <th></th>\n",
1183 |        "      <th>EPL</th>\n",
1184 |        "      <th>La Liga</th>\n",
1185 |        "      <th>Serie A</th>\n",
1186 |        "      <th>Bundesliga</th>\n",
1187 |        "    </tr>\n",
1188 |        "  </thead>\n",
1189 |        "  <tbody>\n",
1190 |        "    <tr>\n",
1191 |        "      <th>GoalsScored</th>\n",
1192 |        "      <td>1063.00</td>\n",
1193 |        "      <td>1133.00</td>\n",
1194 |        "      <td>1003.00</td>\n",
1195 |        "      <td>898.00</td>\n",
1196 |        "    </tr>\n",
1197 |        "    <tr>\n",
1198 |        "      <th>MatchesPlayed</th>\n",
1199 |        "      <td> 380.00</td>\n",
1200 |        "      <td> 380.00</td>\n",
1201 |        "      <td> 380.00</td>\n",
1202 |        "      <td>306.00</td>\n",
1203 |        "    </tr>\n",
1204 |        "    <tr>\n",
1205 |        "      <th>GoalsPerGame</th>\n",
1206 |        "      <td>   2.80</td>\n",
1207 |        "      <td>   2.98</td>\n",
1208 |        "      <td>   2.64</td>\n",
1209 |        "      <td>  2.93</td>\n",
1210 |        "    </tr>\n",
1211 |        "  </tbody>\n",
1212 |        "</table>\n",
1213 |        "</div>"
1214 |       ],
1215 |       "text/plain": [
1216 |        "                  EPL  La Liga  Serie A  Bundesliga\n",
1217 |        "GoalsScored   1063.00  1133.00  1003.00      898.00\n",
1218 |        "MatchesPlayed  380.00   380.00   380.00      306.00\n",
1219 |        "GoalsPerGame     2.80     2.98     2.64        2.93"
1220 |       ]
1221 |      },
1222 |      "execution_count": 57,
1223 |      "metadata": {},
1224 |      "output_type": "execute_result"
1225 |     }
1226 |    ],
1227 |    "source": [
1228 |     "pd.options.display.float_format='{:.2f}'.format \n",
1229 |     "totalsDF.append(goalsPerGameDF)\n"
1230 |    ]
1231 |   },
1232 |   {
1233 |    "cell_type": "markdown",
1234 |    "metadata": {},
1235 |    "source": [
1236 |     "<h3>Using the aggregate method</h3>"
1237 |    ]
1238 |   },
1239 |   {
1240 |    "cell_type": "code",
1241 |    "execution_count": 58,
1242 |    "metadata": {
1243 |     "collapsed": false
1244 |    },
1245 |    "outputs": [
1246 |     {
1247 |      "data": {
1248 |       "text/html": [
1249 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1250 |        "<table border=\"1\" class=\"dataframe\">\n",
1251 |        "  <thead>\n",
1252 |        "    <tr style=\"text-align: right;\">\n",
1253 |        "      <th></th>\n",
1254 |        "      <th>EPL</th>\n",
1255 |        "      <th>La Liga</th>\n",
1256 |        "      <th>Serie A</th>\n",
1257 |        "      <th>Bundesliga</th>\n",
1258 |        "    </tr>\n",
1259 |        "    <tr>\n",
1260 |        "      <th>Stat</th>\n",
1261 |        "      <th></th>\n",
1262 |        "      <th></th>\n",
1263 |        "      <th></th>\n",
1264 |        "      <th></th>\n",
1265 |        "    </tr>\n",
1266 |        "  </thead>\n",
1267 |        "  <tbody>\n",
1268 |        "    <tr>\n",
1269 |        "      <th>GoalsScored</th>\n",
1270 |        "      <td> 1063</td>\n",
1271 |        "      <td> 1133</td>\n",
1272 |        "      <td> 1003</td>\n",
1273 |        "      <td> 898</td>\n",
1274 |        "    </tr>\n",
1275 |        "    <tr>\n",
1276 |        "      <th>MatchesPlayed</th>\n",
1277 |        "      <td>  380</td>\n",
1278 |        "      <td>  380</td>\n",
1279 |        "      <td>  380</td>\n",
1280 |        "      <td> 306</td>\n",
1281 |        "    </tr>\n",
1282 |        "  </tbody>\n",
1283 |        "</table>\n",
1284 |        "</div>"
1285 |       ],
1286 |       "text/plain": [
1287 |        "                EPL  La Liga  Serie A  Bundesliga\n",
1288 |        "Stat                                             \n",
1289 |        "GoalsScored    1063     1133     1003         898\n",
1290 |        "MatchesPlayed   380      380      380         306"
1291 |       ]
1292 |      },
1293 |      "execution_count": 58,
1294 |      "metadata": {},
1295 |      "output_type": "execute_result"
1296 |     }
1297 |    ],
1298 |    "source": [
1299 |     "pd.options.display.float_format=None\n",
1300 |     "grouped3.aggregate(np.sum)"
1301 |    ]
1302 |   },
1303 |   {
1304 |    "cell_type": "markdown",
1305 |    "metadata": {},
1306 |    "source": [
1307 |     "<h3>Applying multiple functions</h3>"
1308 |    ]
1309 |   },
1310 |   {
1311 |    "cell_type": "markdown",
1312 |    "metadata": {},
1313 |    "source": [
1314 |     "<b>Specify list of functions to be applied to each column:</b>"
1315 |    ]
1316 |   },
1317 |   {
1318 |    "cell_type": "code",
1319 |    "execution_count": 60,
1320 |    "metadata": {
1321 |     "collapsed": false
1322 |    },
1323 |    "outputs": [
1324 |     {
1325 |      "data": {
1326 |       "text/html": [
1327 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1328 |        "<table border=\"1\" class=\"dataframe\">\n",
1329 |        "  <thead>\n",
1330 |        "    <tr>\n",
1331 |        "      <th></th>\n",
1332 |        "      <th colspan=\"3\" halign=\"left\">EPL</th>\n",
1333 |        "      <th colspan=\"3\" halign=\"left\">La Liga</th>\n",
1334 |        "      <th colspan=\"3\" halign=\"left\">Serie A</th>\n",
1335 |        "      <th colspan=\"3\" halign=\"left\">Bundesliga</th>\n",
1336 |        "    </tr>\n",
1337 |        "    <tr>\n",
1338 |        "      <th></th>\n",
1339 |        "      <th>sum</th>\n",
1340 |        "      <th>mean</th>\n",
1341 |        "      <th>size</th>\n",
1342 |        "      <th>sum</th>\n",
1343 |        "      <th>mean</th>\n",
1344 |        "      <th>size</th>\n",
1345 |        "      <th>sum</th>\n",
1346 |        "      <th>mean</th>\n",
1347 |        "      <th>size</th>\n",
1348 |        "      <th>sum</th>\n",
1349 |        "      <th>mean</th>\n",
1350 |        "      <th>size</th>\n",
1351 |        "    </tr>\n",
1352 |        "    <tr>\n",
1353 |        "      <th>Stat</th>\n",
1354 |        "      <th></th>\n",
1355 |        "      <th></th>\n",
1356 |        "      <th></th>\n",
1357 |        "      <th></th>\n",
1358 |        "      <th></th>\n",
1359 |        "      <th></th>\n",
1360 |        "      <th></th>\n",
1361 |        "      <th></th>\n",
1362 |        "      <th></th>\n",
1363 |        "      <th></th>\n",
1364 |        "      <th></th>\n",
1365 |        "      <th></th>\n",
1366 |        "    </tr>\n",
1367 |        "  </thead>\n",
1368 |        "  <tbody>\n",
1369 |        "    <tr>\n",
1370 |        "      <th>GoalsScored</th>\n",
1371 |        "      <td> 1063</td>\n",
1372 |        "      <td> 106.3</td>\n",
1373 |        "      <td> 11</td>\n",
1374 |        "      <td> 1133</td>\n",
1375 |        "      <td> 103.000000</td>\n",
1376 |        "      <td> 11</td>\n",
1377 |        "      <td> 1003</td>\n",
1378 |        "      <td> 100.3</td>\n",
1379 |        "      <td> 11</td>\n",
1380 |        "      <td> 898</td>\n",
1381 |        "      <td> 89.8</td>\n",
1382 |        "      <td> 11</td>\n",
1383 |        "    </tr>\n",
1384 |        "    <tr>\n",
1385 |        "      <th>MatchesPlayed</th>\n",
1386 |        "      <td>  380</td>\n",
1387 |        "      <td>  38.0</td>\n",
1388 |        "      <td> 11</td>\n",
1389 |        "      <td>  380</td>\n",
1390 |        "      <td>  34.545455</td>\n",
1391 |        "      <td> 11</td>\n",
1392 |        "      <td>  380</td>\n",
1393 |        "      <td>  38.0</td>\n",
1394 |        "      <td> 11</td>\n",
1395 |        "      <td> 306</td>\n",
1396 |        "      <td> 30.6</td>\n",
1397 |        "      <td> 11</td>\n",
1398 |        "    </tr>\n",
1399 |        "  </tbody>\n",
1400 |        "</table>\n",
1401 |        "</div>"
1402 |       ],
1403 |       "text/plain": [
1404 |        "                EPL               La Liga                    Serie A         \\\n",
1405 |        "                sum   mean  size      sum        mean  size      sum   mean   \n",
1406 |        "Stat                                                                          \n",
1407 |        "GoalsScored    1063  106.3    11     1133  103.000000    11     1003  100.3   \n",
1408 |        "MatchesPlayed   380   38.0    11      380   34.545455    11      380   38.0   \n",
1409 |        "\n",
1410 |        "                     Bundesliga              \n",
1411 |        "               size         sum  mean  size  \n",
1412 |        "Stat                                         \n",
1413 |        "GoalsScored      11         898  89.8    11  \n",
1414 |        "MatchesPlayed    11         306  30.6    11  "
1415 |       ]
1416 |      },
1417 |      "execution_count": 60,
1418 |      "metadata": {},
1419 |      "output_type": "execute_result"
1420 |     }
1421 |    ],
1422 |    "source": [
1423 |     "grouped3.aggregate([np.sum, np.mean,np.size])"
1424 |    ]
1425 |   },
1426 |   {
1427 |    "cell_type": "markdown",
1428 |    "metadata": {},
1429 |    "source": [
1430 |     "<b>Grouped Series</b>"
1431 |    ]
1432 |   },
1433 |   {
1434 |    "cell_type": "code",
1435 |    "execution_count": 61,
1436 |    "metadata": {
1437 |     "collapsed": false
1438 |    },
1439 |    "outputs": [
1440 |     {
1441 |      "data": {
1442 |       "text/html": [
1443 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1444 |        "<table border=\"1\" class=\"dataframe\">\n",
1445 |        "  <thead>\n",
1446 |        "    <tr style=\"text-align: right;\">\n",
1447 |        "      <th></th>\n",
1448 |        "      <th>Deviation</th>\n",
1449 |        "      <th>Average</th>\n",
1450 |        "      <th>Total</th>\n",
1451 |        "    </tr>\n",
1452 |        "    <tr>\n",
1453 |        "      <th>Nation</th>\n",
1454 |        "      <th></th>\n",
1455 |        "      <th></th>\n",
1456 |        "      <th></th>\n",
1457 |        "    </tr>\n",
1458 |        "  </thead>\n",
1459 |        "  <tbody>\n",
1460 |        "    <tr>\n",
1461 |        "      <th>England</th>\n",
1462 |        "      <td> 17091.309877</td>\n",
1463 |        "      <td> 66534.250000</td>\n",
1464 |        "      <td> 798411</td>\n",
1465 |        "    </tr>\n",
1466 |        "    <tr>\n",
1467 |        "      <th>France</th>\n",
1468 |        "      <td>          NaN</td>\n",
1469 |        "      <td> 64400.000000</td>\n",
1470 |        "      <td>  64400</td>\n",
1471 |        "    </tr>\n",
1472 |        "    <tr>\n",
1473 |        "      <th>Germany</th>\n",
1474 |        "      <td> 13783.830076</td>\n",
1475 |        "      <td> 67583.285714</td>\n",
1476 |        "      <td> 473083</td>\n",
1477 |        "    </tr>\n",
1478 |        "    <tr>\n",
1479 |        "      <th>Italy</th>\n",
1480 |        "      <td> 17443.516494</td>\n",
1481 |        "      <td> 65761.250000</td>\n",
1482 |        "      <td> 789135</td>\n",
1483 |        "    </tr>\n",
1484 |        "    <tr>\n",
1485 |        "      <th>Netherlands</th>\n",
1486 |        "      <td> 16048.580972</td>\n",
1487 |        "      <td> 67489.000000</td>\n",
1488 |        "      <td> 404934</td>\n",
1489 |        "    </tr>\n",
1490 |        "    <tr>\n",
1491 |        "      <th>Portugal</th>\n",
1492 |        "      <td> 15632.863259</td>\n",
1493 |        "      <td> 49635.500000</td>\n",
1494 |        "      <td> 198542</td>\n",
1495 |        "    </tr>\n",
1496 |        "    <tr>\n",
1497 |        "      <th>Romania</th>\n",
1498 |        "      <td>          NaN</td>\n",
1499 |        "      <td> 70000.000000</td>\n",
1500 |        "      <td>  70000</td>\n",
1501 |        "    </tr>\n",
1502 |        "    <tr>\n",
1503 |        "      <th>Scotland</th>\n",
1504 |        "      <td>          NaN</td>\n",
1505 |        "      <td> 45000.000000</td>\n",
1506 |        "      <td>  45000</td>\n",
1507 |        "    </tr>\n",
1508 |        "    <tr>\n",
1509 |        "      <th>Spain</th>\n",
1510 |        "      <td> 27457.531064</td>\n",
1511 |        "      <td> 73477.153846</td>\n",
1512 |        "      <td> 955203</td>\n",
1513 |        "    </tr>\n",
1514 |        "    <tr>\n",
1515 |        "      <th>Yugoslavia</th>\n",
1516 |        "      <td>          NaN</td>\n",
1517 |        "      <td> 56000.000000</td>\n",
1518 |        "      <td>  56000</td>\n",
1519 |        "    </tr>\n",
1520 |        "  </tbody>\n",
1521 |        "</table>\n",
1522 |        "</div>"
1523 |       ],
1524 |       "text/plain": [
1525 |        "                Deviation       Average   Total\n",
1526 |        "Nation                                         \n",
1527 |        "England      17091.309877  66534.250000  798411\n",
1528 |        "France                NaN  64400.000000   64400\n",
1529 |        "Germany      13783.830076  67583.285714  473083\n",
1530 |        "Italy        17443.516494  65761.250000  789135\n",
1531 |        "Netherlands  16048.580972  67489.000000  404934\n",
1532 |        "Portugal     15632.863259  49635.500000  198542\n",
1533 |        "Romania               NaN  70000.000000   70000\n",
1534 |        "Scotland              NaN  45000.000000   45000\n",
1535 |        "Spain        27457.531064  73477.153846  955203\n",
1536 |        "Yugoslavia            NaN  56000.000000   56000"
1537 |       ]
1538 |      },
1539 |      "execution_count": 61,
1540 |      "metadata": {},
1541 |      "output_type": "execute_result"
1542 |     }
1543 |    ],
1544 |    "source": [
1545 |     "nationsGrp['Attendance'].agg({'Total':np.sum, 'Average':np.mean, 'Deviation':np.std})"
1546 |    ]
1547 |   },
1548 |   {
1549 |    "cell_type": "markdown",
1550 |    "metadata": {},
1551 |    "source": [
1552 |     "<h3>The transform( ) method</h3>"
1553 |    ]
1554 |   },
1555 |   {
1556 |    "cell_type": "code",
1557 |    "execution_count": 72,
1558 |    "metadata": {
1559 |     "collapsed": false
1560 |    },
1561 |    "outputs": [
1562 |     {
1563 |      "data": {
1564 |       "text/html": [
1565 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1566 |        "<table border=\"1\" class=\"dataframe\">\n",
1567 |        "  <thead>\n",
1568 |        "    <tr style=\"text-align: right;\">\n",
1569 |        "      <th></th>\n",
1570 |        "      <th></th>\n",
1571 |        "      <th>level_0</th>\n",
1572 |        "      <th>index</th>\n",
1573 |        "      <th>EPL</th>\n",
1574 |        "      <th>La Liga</th>\n",
1575 |        "      <th>Serie A</th>\n",
1576 |        "      <th>Bundesliga</th>\n",
1577 |        "    </tr>\n",
1578 |        "    <tr>\n",
1579 |        "      <th>Month</th>\n",
1580 |        "      <th>Stat</th>\n",
1581 |        "      <th></th>\n",
1582 |        "      <th></th>\n",
1583 |        "      <th></th>\n",
1584 |        "      <th></th>\n",
1585 |        "      <th></th>\n",
1586 |        "      <th></th>\n",
1587 |        "    </tr>\n",
1588 |        "  </thead>\n",
1589 |        "  <tbody>\n",
1590 |        "    <tr>\n",
1591 |        "      <th>08/01/2012</th>\n",
1592 |        "      <th>MatchesPlayed</th>\n",
1593 |        "      <td>  0</td>\n",
1594 |        "      <td>  0</td>\n",
1595 |        "      <td>  20</td>\n",
1596 |        "      <td>  20</td>\n",
1597 |        "      <td>  10</td>\n",
1598 |        "      <td>  10</td>\n",
1599 |        "    </tr>\n",
1600 |        "    <tr>\n",
1601 |        "      <th>09/01/2012</th>\n",
1602 |        "      <th>MatchesPlayed</th>\n",
1603 |        "      <td>  1</td>\n",
1604 |        "      <td>  1</td>\n",
1605 |        "      <td>  38</td>\n",
1606 |        "      <td>  39</td>\n",
1607 |        "      <td>  50</td>\n",
1608 |        "      <td>  44</td>\n",
1609 |        "    </tr>\n",
1610 |        "    <tr>\n",
1611 |        "      <th>10/01/2012</th>\n",
1612 |        "      <th>MatchesPlayed</th>\n",
1613 |        "      <td>  2</td>\n",
1614 |        "      <td>  2</td>\n",
1615 |        "      <td>  31</td>\n",
1616 |        "      <td>  31</td>\n",
1617 |        "      <td>  39</td>\n",
1618 |        "      <td>  27</td>\n",
1619 |        "    </tr>\n",
1620 |        "    <tr>\n",
1621 |        "      <th>11/01/2012</th>\n",
1622 |        "      <th>MatchesPlayed</th>\n",
1623 |        "      <td>  3</td>\n",
1624 |        "      <td>  3</td>\n",
1625 |        "      <td>  50</td>\n",
1626 |        "      <td>  41</td>\n",
1627 |        "      <td>  42</td>\n",
1628 |        "      <td>  46</td>\n",
1629 |        "    </tr>\n",
1630 |        "    <tr>\n",
1631 |        "      <th>12/01/2012</th>\n",
1632 |        "      <th>MatchesPlayed</th>\n",
1633 |        "      <td>  4</td>\n",
1634 |        "      <td>  4</td>\n",
1635 |        "      <td>  59</td>\n",
1636 |        "      <td>  39</td>\n",
1637 |        "      <td>  39</td>\n",
1638 |        "      <td>  26</td>\n",
1639 |        "    </tr>\n",
1640 |        "    <tr>\n",
1641 |        "      <th>01/01/2013</th>\n",
1642 |        "      <th>MatchesPlayed</th>\n",
1643 |        "      <td>  5</td>\n",
1644 |        "      <td>  5</td>\n",
1645 |        "      <td>  42</td>\n",
1646 |        "      <td>  40</td>\n",
1647 |        "      <td>  40</td>\n",
1648 |        "      <td>  18</td>\n",
1649 |        "    </tr>\n",
1650 |        "    <tr>\n",
1651 |        "      <th>02/01/2013</th>\n",
1652 |        "      <th>MatchesPlayed</th>\n",
1653 |        "      <td>  6</td>\n",
1654 |        "      <td>  6</td>\n",
1655 |        "      <td>  30</td>\n",
1656 |        "      <td>  40</td>\n",
1657 |        "      <td>  40</td>\n",
1658 |        "      <td>  36</td>\n",
1659 |        "    </tr>\n",
1660 |        "    <tr>\n",
1661 |        "      <th>03/01/2013</th>\n",
1662 |        "      <th>MatchesPlayed</th>\n",
1663 |        "      <td>  7</td>\n",
1664 |        "      <td>  7</td>\n",
1665 |        "      <td>  35</td>\n",
1666 |        "      <td>  38</td>\n",
1667 |        "      <td>  39</td>\n",
1668 |        "      <td>  36</td>\n",
1669 |        "    </tr>\n",
1670 |        "    <tr>\n",
1671 |        "      <th>04/01/2013</th>\n",
1672 |        "      <th>MatchesPlayed</th>\n",
1673 |        "      <td>  8</td>\n",
1674 |        "      <td>  8</td>\n",
1675 |        "      <td>  42</td>\n",
1676 |        "      <td>  42</td>\n",
1677 |        "      <td>  41</td>\n",
1678 |        "      <td>  36</td>\n",
1679 |        "    </tr>\n",
1680 |        "    <tr>\n",
1681 |        "      <th>05/01/2013</th>\n",
1682 |        "      <th>MatchesPlayed</th>\n",
1683 |        "      <td>  9</td>\n",
1684 |        "      <td>  9</td>\n",
1685 |        "      <td>  33</td>\n",
1686 |        "      <td>  40</td>\n",
1687 |        "      <td>  40</td>\n",
1688 |        "      <td>  27</td>\n",
1689 |        "    </tr>\n",
1690 |        "    <tr>\n",
1691 |        "      <th>06/02/2013</th>\n",
1692 |        "      <th>MatchesPlayed</th>\n",
1693 |        "      <td> 10</td>\n",
1694 |        "      <td> 10</td>\n",
1695 |        "      <td> NaN</td>\n",
1696 |        "      <td>  10</td>\n",
1697 |        "      <td> NaN</td>\n",
1698 |        "      <td> NaN</td>\n",
1699 |        "    </tr>\n",
1700 |        "    <tr>\n",
1701 |        "      <th>08/01/2012</th>\n",
1702 |        "      <th>GoalsScored</th>\n",
1703 |        "      <td> 11</td>\n",
1704 |        "      <td> 11</td>\n",
1705 |        "      <td>  57</td>\n",
1706 |        "      <td>  60</td>\n",
1707 |        "      <td>  21</td>\n",
1708 |        "      <td>  23</td>\n",
1709 |        "    </tr>\n",
1710 |        "    <tr>\n",
1711 |        "      <th>09/01/2012</th>\n",
1712 |        "      <th>GoalsScored</th>\n",
1713 |        "      <td> 12</td>\n",
1714 |        "      <td> 12</td>\n",
1715 |        "      <td> 111</td>\n",
1716 |        "      <td> 112</td>\n",
1717 |        "      <td> 133</td>\n",
1718 |        "      <td> 135</td>\n",
1719 |        "    </tr>\n",
1720 |        "    <tr>\n",
1721 |        "      <th>10/01/2012</th>\n",
1722 |        "      <th>GoalsScored</th>\n",
1723 |        "      <td> 13</td>\n",
1724 |        "      <td> 13</td>\n",
1725 |        "      <td>  95</td>\n",
1726 |        "      <td>  88</td>\n",
1727 |        "      <td>  97</td>\n",
1728 |        "      <td>  77</td>\n",
1729 |        "    </tr>\n",
1730 |        "    <tr>\n",
1731 |        "      <th>11/01/2012</th>\n",
1732 |        "      <th>GoalsScored</th>\n",
1733 |        "      <td> 14</td>\n",
1734 |        "      <td> 14</td>\n",
1735 |        "      <td> 121</td>\n",
1736 |        "      <td> 116</td>\n",
1737 |        "      <td> 120</td>\n",
1738 |        "      <td> 137</td>\n",
1739 |        "    </tr>\n",
1740 |        "    <tr>\n",
1741 |        "      <th>12/01/2012</th>\n",
1742 |        "      <th>GoalsScored</th>\n",
1743 |        "      <td> 15</td>\n",
1744 |        "      <td> 15</td>\n",
1745 |        "      <td> 183</td>\n",
1746 |        "      <td> 109</td>\n",
1747 |        "      <td> 125</td>\n",
1748 |        "      <td>  72</td>\n",
1749 |        "    </tr>\n",
1750 |        "    <tr>\n",
1751 |        "      <th>01/01/2013</th>\n",
1752 |        "      <th>GoalsScored</th>\n",
1753 |        "      <td> 16</td>\n",
1754 |        "      <td> 16</td>\n",
1755 |        "      <td> 117</td>\n",
1756 |        "      <td> 121</td>\n",
1757 |        "      <td> 104</td>\n",
1758 |        "      <td>  51</td>\n",
1759 |        "    </tr>\n",
1760 |        "    <tr>\n",
1761 |        "      <th>02/01/2013</th>\n",
1762 |        "      <th>GoalsScored</th>\n",
1763 |        "      <td> 17</td>\n",
1764 |        "      <td> 17</td>\n",
1765 |        "      <td>  87</td>\n",
1766 |        "      <td> 110</td>\n",
1767 |        "      <td> 100</td>\n",
1768 |        "      <td> 101</td>\n",
1769 |        "    </tr>\n",
1770 |        "    <tr>\n",
1771 |        "      <th>03/01/2013</th>\n",
1772 |        "      <th>GoalsScored</th>\n",
1773 |        "      <td> 18</td>\n",
1774 |        "      <td> 18</td>\n",
1775 |        "      <td>  91</td>\n",
1776 |        "      <td> 101</td>\n",
1777 |        "      <td>  99</td>\n",
1778 |        "      <td> 106</td>\n",
1779 |        "    </tr>\n",
1780 |        "    <tr>\n",
1781 |        "      <th>04/01/2013</th>\n",
1782 |        "      <th>GoalsScored</th>\n",
1783 |        "      <td> 19</td>\n",
1784 |        "      <td> 19</td>\n",
1785 |        "      <td> 105</td>\n",
1786 |        "      <td> 127</td>\n",
1787 |        "      <td> 102</td>\n",
1788 |        "      <td> 104</td>\n",
1789 |        "    </tr>\n",
1790 |        "    <tr>\n",
1791 |        "      <th>05/01/2013</th>\n",
1792 |        "      <th>GoalsScored</th>\n",
1793 |        "      <td> 20</td>\n",
1794 |        "      <td> 20</td>\n",
1795 |        "      <td>  96</td>\n",
1796 |        "      <td> 109</td>\n",
1797 |        "      <td> 102</td>\n",
1798 |        "      <td>  92</td>\n",
1799 |        "    </tr>\n",
1800 |        "    <tr>\n",
1801 |        "      <th>06/01/2013</th>\n",
1802 |        "      <th>GoalsScored</th>\n",
1803 |        "      <td> 21</td>\n",
1804 |        "      <td> 21</td>\n",
1805 |        "      <td> NaN</td>\n",
1806 |        "      <td>  80</td>\n",
1807 |        "      <td> NaN</td>\n",
1808 |        "      <td> NaN</td>\n",
1809 |        "    </tr>\n",
1810 |        "  </tbody>\n",
1811 |        "</table>\n",
1812 |        "</div>"
1813 |       ],
1814 |       "text/plain": [
1815 |        "                          level_0  index  EPL  La Liga  Serie A  Bundesliga\n",
1816 |        "Month      Stat                                                            \n",
1817 |        "08/01/2012 MatchesPlayed        0      0   20       20       10          10\n",
1818 |        "09/01/2012 MatchesPlayed        1      1   38       39       50          44\n",
1819 |        "10/01/2012 MatchesPlayed        2      2   31       31       39          27\n",
1820 |        "11/01/2012 MatchesPlayed        3      3   50       41       42          46\n",
1821 |        "12/01/2012 MatchesPlayed        4      4   59       39       39          26\n",
1822 |        "01/01/2013 MatchesPlayed        5      5   42       40       40          18\n",
1823 |        "02/01/2013 MatchesPlayed        6      6   30       40       40          36\n",
1824 |        "03/01/2013 MatchesPlayed        7      7   35       38       39          36\n",
1825 |        "04/01/2013 MatchesPlayed        8      8   42       42       41          36\n",
1826 |        "05/01/2013 MatchesPlayed        9      9   33       40       40          27\n",
1827 |        "06/02/2013 MatchesPlayed       10     10  NaN       10      NaN         NaN\n",
1828 |        "08/01/2012 GoalsScored         11     11   57       60       21          23\n",
1829 |        "09/01/2012 GoalsScored         12     12  111      112      133         135\n",
1830 |        "10/01/2012 GoalsScored         13     13   95       88       97          77\n",
1831 |        "11/01/2012 GoalsScored         14     14  121      116      120         137\n",
1832 |        "12/01/2012 GoalsScored         15     15  183      109      125          72\n",
1833 |        "01/01/2013 GoalsScored         16     16  117      121      104          51\n",
1834 |        "02/01/2013 GoalsScored         17     17   87      110      100         101\n",
1835 |        "03/01/2013 GoalsScored         18     18   91      101       99         106\n",
1836 |        "04/01/2013 GoalsScored         19     19  105      127      102         104\n",
1837 |        "05/01/2013 GoalsScored         20     20   96      109      102          92\n",
1838 |        "06/01/2013 GoalsScored         21     21  NaN       80      NaN         NaN"
1839 |       ]
1840 |      },
1841 |      "execution_count": 72,
1842 |      "metadata": {},
1843 |      "output_type": "execute_result"
1844 |     }
1845 |    ],
1846 |    "source": [
1847 |     "goalStatsDF"
1848 |    ]
1849 |   },
1850 |   {
1851 |    "cell_type": "markdown",
1852 |    "metadata": {},
1853 |    "source": [
1854 |     "<b>DataFrame of goals scored across 4 Euro leagues 2012-2013</b>"
1855 |    ]
1856 |   },
1857 |   {
1858 |    "cell_type": "code",
1859 |    "execution_count": 139,
1860 |    "metadata": {
1861 |     "collapsed": false
1862 |    },
1863 |    "outputs": [
1864 |     {
1865 |      "data": {
1866 |       "text/html": [
1867 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
1868 |        "<table border=\"1\" class=\"dataframe\">\n",
1869 |        "  <thead>\n",
1870 |        "    <tr style=\"text-align: right;\">\n",
1871 |        "      <th></th>\n",
1872 |        "      <th>EPL</th>\n",
1873 |        "      <th>La Liga</th>\n",
1874 |        "      <th>Serie A</th>\n",
1875 |        "      <th>Bundesliga</th>\n",
1876 |        "    </tr>\n",
1877 |        "    <tr>\n",
1878 |        "      <th>Month</th>\n",
1879 |        "      <th></th>\n",
1880 |        "      <th></th>\n",
1881 |        "      <th></th>\n",
1882 |        "      <th></th>\n",
1883 |        "    </tr>\n",
1884 |        "  </thead>\n",
1885 |        "  <tbody>\n",
1886 |        "    <tr>\n",
1887 |        "      <th>08/01/2012</th>\n",
1888 |        "      <td>  57</td>\n",
1889 |        "      <td>  60</td>\n",
1890 |        "      <td>  21</td>\n",
1891 |        "      <td>  23</td>\n",
1892 |        "    </tr>\n",
1893 |        "    <tr>\n",
1894 |        "      <th>09/01/2012</th>\n",
1895 |        "      <td> 111</td>\n",
1896 |        "      <td> 112</td>\n",
1897 |        "      <td> 133</td>\n",
1898 |        "      <td> 135</td>\n",
1899 |        "    </tr>\n",
1900 |        "    <tr>\n",
1901 |        "      <th>10/01/2012</th>\n",
1902 |        "      <td>  95</td>\n",
1903 |        "      <td>  88</td>\n",
1904 |        "      <td>  97</td>\n",
1905 |        "      <td>  77</td>\n",
1906 |        "    </tr>\n",
1907 |        "    <tr>\n",
1908 |        "      <th>11/01/2012</th>\n",
1909 |        "      <td> 121</td>\n",
1910 |        "      <td> 116</td>\n",
1911 |        "      <td> 120</td>\n",
1912 |        "      <td> 137</td>\n",
1913 |        "    </tr>\n",
1914 |        "    <tr>\n",
1915 |        "      <th>12/01/2012</th>\n",
1916 |        "      <td> 183</td>\n",
1917 |        "      <td> 109</td>\n",
1918 |        "      <td> 125</td>\n",
1919 |        "      <td>  72</td>\n",
1920 |        "    </tr>\n",
1921 |        "    <tr>\n",
1922 |        "      <th>01/01/2013</th>\n",
1923 |        "      <td> 117</td>\n",
1924 |        "      <td> 121</td>\n",
1925 |        "      <td> 104</td>\n",
1926 |        "      <td>  51</td>\n",
1927 |        "    </tr>\n",
1928 |        "    <tr>\n",
1929 |        "      <th>02/01/2013</th>\n",
1930 |        "      <td>  87</td>\n",
1931 |        "      <td> 110</td>\n",
1932 |        "      <td> 100</td>\n",
1933 |        "      <td> 101</td>\n",
1934 |        "    </tr>\n",
1935 |        "    <tr>\n",
1936 |        "      <th>03/01/2013</th>\n",
1937 |        "      <td>  91</td>\n",
1938 |        "      <td> 101</td>\n",
1939 |        "      <td>  99</td>\n",
1940 |        "      <td> 106</td>\n",
1941 |        "    </tr>\n",
1942 |        "    <tr>\n",
1943 |        "      <th>04/01/2013</th>\n",
1944 |        "      <td> 105</td>\n",
1945 |        "      <td> 127</td>\n",
1946 |        "      <td> 102</td>\n",
1947 |        "      <td> 104</td>\n",
1948 |        "    </tr>\n",
1949 |        "    <tr>\n",
1950 |        "      <th>05/01/2013</th>\n",
1951 |        "      <td>  96</td>\n",
1952 |        "      <td> 109</td>\n",
1953 |        "      <td> 102</td>\n",
1954 |        "      <td>  92</td>\n",
1955 |        "    </tr>\n",
1956 |        "    <tr>\n",
1957 |        "      <th>06/01/2013</th>\n",
1958 |        "      <td> NaN</td>\n",
1959 |        "      <td>  80</td>\n",
1960 |        "      <td> NaN</td>\n",
1961 |        "      <td> NaN</td>\n",
1962 |        "    </tr>\n",
1963 |        "  </tbody>\n",
1964 |        "</table>\n",
1965 |        "</div>"
1966 |       ],
1967 |       "text/plain": [
1968 |        "            EPL  La Liga  Serie A  Bundesliga\n",
1969 |        "Month                                        \n",
1970 |        "08/01/2012   57       60       21          23\n",
1971 |        "09/01/2012  111      112      133         135\n",
1972 |        "10/01/2012   95       88       97          77\n",
1973 |        "11/01/2012  121      116      120         137\n",
1974 |        "12/01/2012  183      109      125          72\n",
1975 |        "01/01/2013  117      121      104          51\n",
1976 |        "02/01/2013   87      110      100         101\n",
1977 |        "03/01/2013   91      101       99         106\n",
1978 |        "04/01/2013  105      127      102         104\n",
1979 |        "05/01/2013   96      109      102          92\n",
1980 |        "06/01/2013  NaN       80      NaN         NaN"
1981 |       ]
1982 |      },
1983 |      "execution_count": 139,
1984 |      "metadata": {},
1985 |      "output_type": "execute_result"
1986 |     }
1987 |    ],
1988 |    "source": [
1989 |     "goalStatsDF2=pd.read_csv('./data/goal_stats_euro_leagues_2012-13.csv')\n",
1990 |     "goalStatsDF2=goalStatsDF2.set_index(['Month'])\n",
1991 |     "goalsScoredDF=goalStatsDF2.ix[goalStatsDF2['Stat']=='GoalsScored']\n",
1992 |     "\n",
1993 |     "goalsScoredDF.iloc[:,1:]"
1994 |    ]
1995 |   },
1996 |   {
1997 |    "cell_type": "markdown",
1998 |    "metadata": {},
1999 |    "source": [
2000 |     "<b>Find mean of goals scored for each league, grouped by year:</b>"
2001 |    ]
2002 |   },
2003 |   {
2004 |    "cell_type": "code",
2005 |    "execution_count": 117,
2006 |    "metadata": {
2007 |     "collapsed": false
2008 |    },
2009 |    "outputs": [
2010 |     {
2011 |      "data": {
2012 |       "text/html": [
2013 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2014 |        "<table border=\"1\" class=\"dataframe\">\n",
2015 |        "  <thead>\n",
2016 |        "    <tr style=\"text-align: right;\">\n",
2017 |        "      <th></th>\n",
2018 |        "      <th>EPL</th>\n",
2019 |        "      <th>La Liga</th>\n",
2020 |        "      <th>Serie A</th>\n",
2021 |        "      <th>Bundesliga</th>\n",
2022 |        "    </tr>\n",
2023 |        "  </thead>\n",
2024 |        "  <tbody>\n",
2025 |        "    <tr>\n",
2026 |        "      <th>2012</th>\n",
2027 |        "      <td> 113.4</td>\n",
2028 |        "      <td>  97</td>\n",
2029 |        "      <td>  99.2</td>\n",
2030 |        "      <td> 88.8</td>\n",
2031 |        "    </tr>\n",
2032 |        "    <tr>\n",
2033 |        "      <th>2013</th>\n",
2034 |        "      <td>  99.2</td>\n",
2035 |        "      <td> 108</td>\n",
2036 |        "      <td> 101.4</td>\n",
2037 |        "      <td> 90.8</td>\n",
2038 |        "    </tr>\n",
2039 |        "  </tbody>\n",
2040 |        "</table>\n",
2041 |        "</div>"
2042 |       ],
2043 |       "text/plain": [
2044 |        "        EPL  La Liga  Serie A  Bundesliga\n",
2045 |        "2012  113.4       97     99.2        88.8\n",
2046 |        "2013   99.2      108    101.4        90.8"
2047 |       ]
2048 |      },
2049 |      "execution_count": 117,
2050 |      "metadata": {},
2051 |      "output_type": "execute_result"
2052 |     }
2053 |    ],
2054 |    "source": [
2055 |     "goalsScoredPerYearGrp=goalsScoredDF.groupby(lambda Month: Month.split('/')[2])\n",
2056 |     "goalsScoredPerYearGrp.mean()"
2057 |    ]
2058 |   },
2059 |   {
2060 |    "cell_type": "markdown",
2061 |    "metadata": {},
2062 |    "source": [
2063 |     "<b>Count of months of football for each year</b>"
2064 |    ]
2065 |   },
2066 |   {
2067 |    "cell_type": "code",
2068 |    "execution_count": 118,
2069 |    "metadata": {
2070 |     "collapsed": false
2071 |    },
2072 |    "outputs": [
2073 |     {
2074 |      "data": {
2075 |       "text/html": [
2076 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2077 |        "<table border=\"1\" class=\"dataframe\">\n",
2078 |        "  <thead>\n",
2079 |        "    <tr style=\"text-align: right;\">\n",
2080 |        "      <th></th>\n",
2081 |        "      <th>EPL</th>\n",
2082 |        "      <th>La Liga</th>\n",
2083 |        "      <th>Serie A</th>\n",
2084 |        "      <th>Bundesliga</th>\n",
2085 |        "    </tr>\n",
2086 |        "  </thead>\n",
2087 |        "  <tbody>\n",
2088 |        "    <tr>\n",
2089 |        "      <th>2012</th>\n",
2090 |        "      <td> 5</td>\n",
2091 |        "      <td> 5</td>\n",
2092 |        "      <td> 5</td>\n",
2093 |        "      <td> 5</td>\n",
2094 |        "    </tr>\n",
2095 |        "    <tr>\n",
2096 |        "      <th>2013</th>\n",
2097 |        "      <td> 5</td>\n",
2098 |        "      <td> 6</td>\n",
2099 |        "      <td> 5</td>\n",
2100 |        "      <td> 5</td>\n",
2101 |        "    </tr>\n",
2102 |        "  </tbody>\n",
2103 |        "</table>\n",
2104 |        "</div>"
2105 |       ],
2106 |       "text/plain": [
2107 |        "      EPL  La Liga  Serie A  Bundesliga\n",
2108 |        "2012    5        5        5           5\n",
2109 |        "2013    5        6        5           5"
2110 |       ]
2111 |      },
2112 |      "execution_count": 118,
2113 |      "metadata": {},
2114 |      "output_type": "execute_result"
2115 |     }
2116 |    ],
2117 |    "source": [
2118 |     "goalsScoredPerYearGrp.count()\n"
2119 |    ]
2120 |   },
2121 |   {
2122 |    "cell_type": "markdown",
2123 |    "metadata": {},
2124 |    "source": [
2125 |     "<b>Define fill function and fill in missing values</b>"
2126 |    ]
2127 |   },
2128 |   {
2129 |    "cell_type": "code",
2130 |    "execution_count": 119,
2131 |    "metadata": {
2132 |     "collapsed": false
2133 |    },
2134 |    "outputs": [
2135 |     {
2136 |      "data": {
2137 |       "text/html": [
2138 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2139 |        "<table border=\"1\" class=\"dataframe\">\n",
2140 |        "  <thead>\n",
2141 |        "    <tr style=\"text-align: right;\">\n",
2142 |        "      <th></th>\n",
2143 |        "      <th>EPL</th>\n",
2144 |        "      <th>La Liga</th>\n",
2145 |        "      <th>Serie A</th>\n",
2146 |        "      <th>Bundesliga</th>\n",
2147 |        "    </tr>\n",
2148 |        "  </thead>\n",
2149 |        "  <tbody>\n",
2150 |        "    <tr>\n",
2151 |        "      <th>2012</th>\n",
2152 |        "      <td> 113.4</td>\n",
2153 |        "      <td>  97</td>\n",
2154 |        "      <td>  99.2</td>\n",
2155 |        "      <td> 88.8</td>\n",
2156 |        "    </tr>\n",
2157 |        "    <tr>\n",
2158 |        "      <th>2013</th>\n",
2159 |        "      <td>  99.2</td>\n",
2160 |        "      <td> 108</td>\n",
2161 |        "      <td> 101.4</td>\n",
2162 |        "      <td> 90.8</td>\n",
2163 |        "    </tr>\n",
2164 |        "  </tbody>\n",
2165 |        "</table>\n",
2166 |        "</div>"
2167 |       ],
2168 |       "text/plain": [
2169 |        "        EPL  La Liga  Serie A  Bundesliga\n",
2170 |        "2012  113.4       97     99.2        88.8\n",
2171 |        "2013   99.2      108    101.4        90.8"
2172 |       ]
2173 |      },
2174 |      "execution_count": 119,
2175 |      "metadata": {},
2176 |      "output_type": "execute_result"
2177 |     }
2178 |    ],
2179 |    "source": [
2180 |     "fill_fcn = lambda x: x.fillna(x.mean())\n",
2181 |     "trans = goalsScoredPerYearGrp.transform(fill_fcn)\n",
2182 |     "tGroupedStats = trans.groupby(lambda Month:   Month.split('/')[2])\n",
2183 |     "tGroupedStats.mean() \n"
2184 |    ]
2185 |   },
2186 |   {
2187 |    "cell_type": "markdown",
2188 |    "metadata": {},
2189 |    "source": [
2190 |     "<b>Display count on filled in data:</b>"
2191 |    ]
2192 |   },
2193 |   {
2194 |    "cell_type": "code",
2195 |    "execution_count": 120,
2196 |    "metadata": {
2197 |     "collapsed": false
2198 |    },
2199 |    "outputs": [
2200 |     {
2201 |      "data": {
2202 |       "text/html": [
2203 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2204 |        "<table border=\"1\" class=\"dataframe\">\n",
2205 |        "  <thead>\n",
2206 |        "    <tr style=\"text-align: right;\">\n",
2207 |        "      <th></th>\n",
2208 |        "      <th>EPL</th>\n",
2209 |        "      <th>La Liga</th>\n",
2210 |        "      <th>Serie A</th>\n",
2211 |        "      <th>Bundesliga</th>\n",
2212 |        "    </tr>\n",
2213 |        "  </thead>\n",
2214 |        "  <tbody>\n",
2215 |        "    <tr>\n",
2216 |        "      <th>2012</th>\n",
2217 |        "      <td> 5</td>\n",
2218 |        "      <td> 5</td>\n",
2219 |        "      <td> 5</td>\n",
2220 |        "      <td> 5</td>\n",
2221 |        "    </tr>\n",
2222 |        "    <tr>\n",
2223 |        "      <th>2013</th>\n",
2224 |        "      <td> 6</td>\n",
2225 |        "      <td> 6</td>\n",
2226 |        "      <td> 6</td>\n",
2227 |        "      <td> 6</td>\n",
2228 |        "    </tr>\n",
2229 |        "  </tbody>\n",
2230 |        "</table>\n",
2231 |        "</div>"
2232 |       ],
2233 |       "text/plain": [
2234 |        "      EPL  La Liga  Serie A  Bundesliga\n",
2235 |        "2012    5        5        5           5\n",
2236 |        "2013    6        6        6           6"
2237 |       ]
2238 |      },
2239 |      "execution_count": 120,
2240 |      "metadata": {},
2241 |      "output_type": "execute_result"
2242 |     }
2243 |    ],
2244 |    "source": [
2245 |     "tGroupedStats.count()"
2246 |    ]
2247 |   },
2248 |   {
2249 |    "cell_type": "markdown",
2250 |    "metadata": {},
2251 |    "source": [
2252 |     "<h3>Filtering</h3>"
2253 |    ]
2254 |   },
2255 |   {
2256 |    "cell_type": "markdown",
2257 |    "metadata": {},
2258 |    "source": [
2259 |     "<b>Display which months of the season had more than 100 goals scored in each of the 4 leagues </b>"
2260 |    ]
2261 |   },
2262 |   {
2263 |    "cell_type": "code",
2264 |    "execution_count": 143,
2265 |    "metadata": {
2266 |     "collapsed": false
2267 |    },
2268 |    "outputs": [
2269 |     {
2270 |      "data": {
2271 |       "text/html": [
2272 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2273 |        "<table border=\"1\" class=\"dataframe\">\n",
2274 |        "  <thead>\n",
2275 |        "    <tr style=\"text-align: right;\">\n",
2276 |        "      <th></th>\n",
2277 |        "      <th>Stat</th>\n",
2278 |        "      <th>EPL</th>\n",
2279 |        "      <th>La Liga</th>\n",
2280 |        "      <th>Serie A</th>\n",
2281 |        "      <th>Bundesliga</th>\n",
2282 |        "    </tr>\n",
2283 |        "    <tr>\n",
2284 |        "      <th>Month</th>\n",
2285 |        "      <th></th>\n",
2286 |        "      <th></th>\n",
2287 |        "      <th></th>\n",
2288 |        "      <th></th>\n",
2289 |        "      <th></th>\n",
2290 |        "    </tr>\n",
2291 |        "  </thead>\n",
2292 |        "  <tbody>\n",
2293 |        "    <tr>\n",
2294 |        "      <th>08/01/2012</th>\n",
2295 |        "      <td> GoalsScored</td>\n",
2296 |        "      <td>  57</td>\n",
2297 |        "      <td>  60</td>\n",
2298 |        "      <td>  21</td>\n",
2299 |        "      <td>  23</td>\n",
2300 |        "    </tr>\n",
2301 |        "    <tr>\n",
2302 |        "      <th>09/01/2012</th>\n",
2303 |        "      <td> GoalsScored</td>\n",
2304 |        "      <td> 111</td>\n",
2305 |        "      <td> 112</td>\n",
2306 |        "      <td> 133</td>\n",
2307 |        "      <td> 135</td>\n",
2308 |        "    </tr>\n",
2309 |        "    <tr>\n",
2310 |        "      <th>10/01/2012</th>\n",
2311 |        "      <td> GoalsScored</td>\n",
2312 |        "      <td>  95</td>\n",
2313 |        "      <td>  88</td>\n",
2314 |        "      <td>  97</td>\n",
2315 |        "      <td>  77</td>\n",
2316 |        "    </tr>\n",
2317 |        "    <tr>\n",
2318 |        "      <th>11/01/2012</th>\n",
2319 |        "      <td> GoalsScored</td>\n",
2320 |        "      <td> 121</td>\n",
2321 |        "      <td> 116</td>\n",
2322 |        "      <td> 120</td>\n",
2323 |        "      <td> 137</td>\n",
2324 |        "    </tr>\n",
2325 |        "    <tr>\n",
2326 |        "      <th>12/01/2012</th>\n",
2327 |        "      <td> GoalsScored</td>\n",
2328 |        "      <td> 183</td>\n",
2329 |        "      <td> 109</td>\n",
2330 |        "      <td> 125</td>\n",
2331 |        "      <td>  72</td>\n",
2332 |        "    </tr>\n",
2333 |        "    <tr>\n",
2334 |        "      <th>01/01/2013</th>\n",
2335 |        "      <td> GoalsScored</td>\n",
2336 |        "      <td> 117</td>\n",
2337 |        "      <td> 121</td>\n",
2338 |        "      <td> 104</td>\n",
2339 |        "      <td>  51</td>\n",
2340 |        "    </tr>\n",
2341 |        "    <tr>\n",
2342 |        "      <th>02/01/2013</th>\n",
2343 |        "      <td> GoalsScored</td>\n",
2344 |        "      <td>  87</td>\n",
2345 |        "      <td> 110</td>\n",
2346 |        "      <td> 100</td>\n",
2347 |        "      <td> 101</td>\n",
2348 |        "    </tr>\n",
2349 |        "    <tr>\n",
2350 |        "      <th>03/01/2013</th>\n",
2351 |        "      <td> GoalsScored</td>\n",
2352 |        "      <td>  91</td>\n",
2353 |        "      <td> 101</td>\n",
2354 |        "      <td>  99</td>\n",
2355 |        "      <td> 106</td>\n",
2356 |        "    </tr>\n",
2357 |        "    <tr>\n",
2358 |        "      <th>04/01/2013</th>\n",
2359 |        "      <td> GoalsScored</td>\n",
2360 |        "      <td> 105</td>\n",
2361 |        "      <td> 127</td>\n",
2362 |        "      <td> 102</td>\n",
2363 |        "      <td> 104</td>\n",
2364 |        "    </tr>\n",
2365 |        "    <tr>\n",
2366 |        "      <th>05/01/2013</th>\n",
2367 |        "      <td> GoalsScored</td>\n",
2368 |        "      <td>  96</td>\n",
2369 |        "      <td> 109</td>\n",
2370 |        "      <td> 102</td>\n",
2371 |        "      <td>  92</td>\n",
2372 |        "    </tr>\n",
2373 |        "    <tr>\n",
2374 |        "      <th>06/01/2013</th>\n",
2375 |        "      <td> GoalsScored</td>\n",
2376 |        "      <td> NaN</td>\n",
2377 |        "      <td>  80</td>\n",
2378 |        "      <td> NaN</td>\n",
2379 |        "      <td> NaN</td>\n",
2380 |        "    </tr>\n",
2381 |        "  </tbody>\n",
2382 |        "</table>\n",
2383 |        "</div>"
2384 |       ],
2385 |       "text/plain": [
2386 |        "                   Stat  EPL  La Liga  Serie A  Bundesliga\n",
2387 |        "Month                                                     \n",
2388 |        "08/01/2012  GoalsScored   57       60       21          23\n",
2389 |        "09/01/2012  GoalsScored  111      112      133         135\n",
2390 |        "10/01/2012  GoalsScored   95       88       97          77\n",
2391 |        "11/01/2012  GoalsScored  121      116      120         137\n",
2392 |        "12/01/2012  GoalsScored  183      109      125          72\n",
2393 |        "01/01/2013  GoalsScored  117      121      104          51\n",
2394 |        "02/01/2013  GoalsScored   87      110      100         101\n",
2395 |        "03/01/2013  GoalsScored   91      101       99         106\n",
2396 |        "04/01/2013  GoalsScored  105      127      102         104\n",
2397 |        "05/01/2013  GoalsScored   96      109      102          92\n",
2398 |        "06/01/2013  GoalsScored  NaN       80      NaN         NaN"
2399 |       ]
2400 |      },
2401 |      "execution_count": 143,
2402 |      "metadata": {},
2403 |      "output_type": "execute_result"
2404 |     }
2405 |    ],
2406 |    "source": [
2407 |     "#goalsScoredDF=goalsScoredDF.iloc[:,1:]\n",
2408 |     "goalsScoredDF"
2409 |    ]
2410 |   },
2411 |   {
2412 |    "cell_type": "code",
2413 |    "execution_count": 144,
2414 |    "metadata": {
2415 |     "collapsed": false
2416 |    },
2417 |    "outputs": [
2418 |     {
2419 |      "data": {
2420 |       "text/html": [
2421 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2422 |        "<table border=\"1\" class=\"dataframe\">\n",
2423 |        "  <thead>\n",
2424 |        "    <tr style=\"text-align: right;\">\n",
2425 |        "      <th></th>\n",
2426 |        "      <th>Stat</th>\n",
2427 |        "      <th>EPL</th>\n",
2428 |        "      <th>La Liga</th>\n",
2429 |        "      <th>Serie A</th>\n",
2430 |        "      <th>Bundesliga</th>\n",
2431 |        "    </tr>\n",
2432 |        "    <tr>\n",
2433 |        "      <th>Month</th>\n",
2434 |        "      <th></th>\n",
2435 |        "      <th></th>\n",
2436 |        "      <th></th>\n",
2437 |        "      <th></th>\n",
2438 |        "      <th></th>\n",
2439 |        "    </tr>\n",
2440 |        "  </thead>\n",
2441 |        "  <tbody>\n",
2442 |        "    <tr>\n",
2443 |        "      <th>09/01/2012</th>\n",
2444 |        "      <td> GoalsScored</td>\n",
2445 |        "      <td> 111</td>\n",
2446 |        "      <td> 112</td>\n",
2447 |        "      <td> 133</td>\n",
2448 |        "      <td> 135</td>\n",
2449 |        "    </tr>\n",
2450 |        "    <tr>\n",
2451 |        "      <th>11/01/2012</th>\n",
2452 |        "      <td> GoalsScored</td>\n",
2453 |        "      <td> 121</td>\n",
2454 |        "      <td> 116</td>\n",
2455 |        "      <td> 120</td>\n",
2456 |        "      <td> 137</td>\n",
2457 |        "    </tr>\n",
2458 |        "    <tr>\n",
2459 |        "      <th>04/01/2013</th>\n",
2460 |        "      <td> GoalsScored</td>\n",
2461 |        "      <td> 105</td>\n",
2462 |        "      <td> 127</td>\n",
2463 |        "      <td> 102</td>\n",
2464 |        "      <td> 104</td>\n",
2465 |        "    </tr>\n",
2466 |        "  </tbody>\n",
2467 |        "</table>\n",
2468 |        "</div>"
2469 |       ],
2470 |       "text/plain": [
2471 |        "                   Stat  EPL  La Liga  Serie A  Bundesliga\n",
2472 |        "Month                                                     \n",
2473 |        "09/01/2012  GoalsScored  111      112      133         135\n",
2474 |        "11/01/2012  GoalsScored  121      116      120         137\n",
2475 |        "04/01/2013  GoalsScored  105      127      102         104"
2476 |       ]
2477 |      },
2478 |      "execution_count": 144,
2479 |      "metadata": {},
2480 |      "output_type": "execute_result"
2481 |     }
2482 |    ],
2483 |    "source": [
2484 |     "goalsScoredDF.groupby(level='Month').filter(lambda x: \n",
2485 |     "                                            np.all([x[col] > 100 for col in goalsScoredDF.columns]))"
2486 |    ]
2487 |   },
2488 |   {
2489 |    "cell_type": "markdown",
2490 |    "metadata": {},
2491 |    "source": [
2492 |     "<h1>Merging and Joining</h1>\n"
2493 |    ]
2494 |   },
2495 |   {
2496 |    "cell_type": "code",
2497 |    "execution_count": 145,
2498 |    "metadata": {
2499 |     "collapsed": false
2500 |    },
2501 |    "outputs": [
2502 |     {
2503 |      "data": {
2504 |       "text/html": [
2505 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2506 |        "<table border=\"1\" class=\"dataframe\">\n",
2507 |        "  <thead>\n",
2508 |        "    <tr style=\"text-align: right;\">\n",
2509 |        "      <th></th>\n",
2510 |        "      <th>Closing price</th>\n",
2511 |        "      <th>EPS</th>\n",
2512 |        "      <th>Shares Outstanding(M)</th>\n",
2513 |        "      <th>Beta</th>\n",
2514 |        "      <th>P/E</th>\n",
2515 |        "      <th>Market Cap(B)</th>\n",
2516 |        "    </tr>\n",
2517 |        "    <tr>\n",
2518 |        "      <th>Symbol</th>\n",
2519 |        "      <th></th>\n",
2520 |        "      <th></th>\n",
2521 |        "      <th></th>\n",
2522 |        "      <th></th>\n",
2523 |        "      <th></th>\n",
2524 |        "      <th></th>\n",
2525 |        "    </tr>\n",
2526 |        "  </thead>\n",
2527 |        "  <tbody>\n",
2528 |        "    <tr>\n",
2529 |        "      <th>AMZN</th>\n",
2530 |        "      <td>  346.15</td>\n",
2531 |        "      <td>  0.59</td>\n",
2532 |        "      <td>  459.00</td>\n",
2533 |        "      <td>  0.52</td>\n",
2534 |        "      <td> 589.80</td>\n",
2535 |        "      <td> 158.88</td>\n",
2536 |        "    </tr>\n",
2537 |        "    <tr>\n",
2538 |        "      <th>GOOG</th>\n",
2539 |        "      <td> 1133.43</td>\n",
2540 |        "      <td> 36.05</td>\n",
2541 |        "      <td>  335.83</td>\n",
2542 |        "      <td>  0.87</td>\n",
2543 |        "      <td>  31.44</td>\n",
2544 |        "      <td> 380.64</td>\n",
2545 |        "    </tr>\n",
2546 |        "    <tr>\n",
2547 |        "      <th>FB</th>\n",
2548 |        "      <td>   61.48</td>\n",
2549 |        "      <td>  0.59</td>\n",
2550 |        "      <td> 2450.00</td>\n",
2551 |        "      <td>   NaN</td>\n",
2552 |        "      <td> 104.93</td>\n",
2553 |        "      <td> 150.92</td>\n",
2554 |        "    </tr>\n",
2555 |        "    <tr>\n",
2556 |        "      <th>YHOO</th>\n",
2557 |        "      <td>   34.90</td>\n",
2558 |        "      <td>  1.27</td>\n",
2559 |        "      <td> 1010.00</td>\n",
2560 |        "      <td> 27.48</td>\n",
2561 |        "      <td>   0.66</td>\n",
2562 |        "      <td>  35.36</td>\n",
2563 |        "    </tr>\n",
2564 |        "    <tr>\n",
2565 |        "      <th>TWTR</th>\n",
2566 |        "      <td>   65.25</td>\n",
2567 |        "      <td> -0.30</td>\n",
2568 |        "      <td>  555.20</td>\n",
2569 |        "      <td>   NaN</td>\n",
2570 |        "      <td>    NaN</td>\n",
2571 |        "      <td>  36.23</td>\n",
2572 |        "    </tr>\n",
2573 |        "    <tr>\n",
2574 |        "      <th>AAPL</th>\n",
2575 |        "      <td>  501.53</td>\n",
2576 |        "      <td> 40.32</td>\n",
2577 |        "      <td>  892.45</td>\n",
2578 |        "      <td> 12.44</td>\n",
2579 |        "      <td> 447.59</td>\n",
2580 |        "      <td>   0.84</td>\n",
2581 |        "    </tr>\n",
2582 |        "  </tbody>\n",
2583 |        "</table>\n",
2584 |        "</div>"
2585 |       ],
2586 |       "text/plain": [
2587 |        "        Closing price    EPS  Shares Outstanding(M)   Beta     P/E  \\\n",
2588 |        "Symbol                                                               \n",
2589 |        "AMZN           346.15   0.59                 459.00   0.52  589.80   \n",
2590 |        "GOOG          1133.43  36.05                 335.83   0.87   31.44   \n",
2591 |        "FB              61.48   0.59                2450.00    NaN  104.93   \n",
2592 |        "YHOO            34.90   1.27                1010.00  27.48    0.66   \n",
2593 |        "TWTR            65.25  -0.30                 555.20    NaN     NaN   \n",
2594 |        "AAPL           501.53  40.32                 892.45  12.44  447.59   \n",
2595 |        "\n",
2596 |        "        Market Cap(B)  \n",
2597 |        "Symbol                 \n",
2598 |        "AMZN           158.88  \n",
2599 |        "GOOG           380.64  \n",
2600 |        "FB             150.92  \n",
2601 |        "YHOO            35.36  \n",
2602 |        "TWTR            36.23  \n",
2603 |        "AAPL             0.84  "
2604 |       ]
2605 |      },
2606 |      "execution_count": 145,
2607 |      "metadata": {},
2608 |      "output_type": "execute_result"
2609 |     }
2610 |    ],
2611 |    "source": [
2612 |     "stockDataDF=pd.read_csv('./data/tech_stockprices.csv').set_index(['Symbol']);stockDataDF\n"
2613 |    ]
2614 |   },
2615 |   {
2616 |    "cell_type": "markdown",
2617 |    "metadata": {},
2618 |    "source": [
2619 |     "<h3>The concat function</h3>"
2620 |    ]
2621 |   },
2622 |   {
2623 |    "cell_type": "markdown",
2624 |    "metadata": {},
2625 |    "source": [
2626 |     "<b>Obtain various slices of the data</b>"
2627 |    ]
2628 |   },
2629 |   {
2630 |    "cell_type": "code",
2631 |    "execution_count": 146,
2632 |    "metadata": {
2633 |     "collapsed": false
2634 |    },
2635 |    "outputs": [
2636 |     {
2637 |      "data": {
2638 |       "text/html": [
2639 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2640 |        "<table border=\"1\" class=\"dataframe\">\n",
2641 |        "  <thead>\n",
2642 |        "    <tr style=\"text-align: right;\">\n",
2643 |        "      <th></th>\n",
2644 |        "      <th>Closing price</th>\n",
2645 |        "      <th>EPS</th>\n",
2646 |        "    </tr>\n",
2647 |        "    <tr>\n",
2648 |        "      <th>Symbol</th>\n",
2649 |        "      <th></th>\n",
2650 |        "      <th></th>\n",
2651 |        "    </tr>\n",
2652 |        "  </thead>\n",
2653 |        "  <tbody>\n",
2654 |        "    <tr>\n",
2655 |        "      <th>AMZN</th>\n",
2656 |        "      <td>  346.15</td>\n",
2657 |        "      <td>  0.59</td>\n",
2658 |        "    </tr>\n",
2659 |        "    <tr>\n",
2660 |        "      <th>GOOG</th>\n",
2661 |        "      <td> 1133.43</td>\n",
2662 |        "      <td> 36.05</td>\n",
2663 |        "    </tr>\n",
2664 |        "    <tr>\n",
2665 |        "      <th>FB</th>\n",
2666 |        "      <td>   61.48</td>\n",
2667 |        "      <td>  0.59</td>\n",
2668 |        "    </tr>\n",
2669 |        "    <tr>\n",
2670 |        "      <th>YHOO</th>\n",
2671 |        "      <td>   34.90</td>\n",
2672 |        "      <td>  1.27</td>\n",
2673 |        "    </tr>\n",
2674 |        "  </tbody>\n",
2675 |        "</table>\n",
2676 |        "</div>"
2677 |       ],
2678 |       "text/plain": [
2679 |        "        Closing price    EPS\n",
2680 |        "Symbol                      \n",
2681 |        "AMZN           346.15   0.59\n",
2682 |        "GOOG          1133.43  36.05\n",
2683 |        "FB              61.48   0.59\n",
2684 |        "YHOO            34.90   1.27"
2685 |       ]
2686 |      },
2687 |      "execution_count": 146,
2688 |      "metadata": {},
2689 |      "output_type": "execute_result"
2690 |     }
2691 |    ],
2692 |    "source": [
2693 |     "A=stockDataDF.ix[:4, ['Closing price', 'EPS']]; A"
2694 |    ]
2695 |   },
2696 |   {
2697 |    "cell_type": "code",
2698 |    "execution_count": 147,
2699 |    "metadata": {
2700 |     "collapsed": false
2701 |    },
2702 |    "outputs": [
2703 |     {
2704 |      "data": {
2705 |       "text/html": [
2706 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2707 |        "<table border=\"1\" class=\"dataframe\">\n",
2708 |        "  <thead>\n",
2709 |        "    <tr style=\"text-align: right;\">\n",
2710 |        "      <th></th>\n",
2711 |        "      <th>P/E</th>\n",
2712 |        "    </tr>\n",
2713 |        "    <tr>\n",
2714 |        "      <th>Symbol</th>\n",
2715 |        "      <th></th>\n",
2716 |        "    </tr>\n",
2717 |        "  </thead>\n",
2718 |        "  <tbody>\n",
2719 |        "    <tr>\n",
2720 |        "      <th>FB</th>\n",
2721 |        "      <td> 104.93</td>\n",
2722 |        "    </tr>\n",
2723 |        "    <tr>\n",
2724 |        "      <th>YHOO</th>\n",
2725 |        "      <td>   0.66</td>\n",
2726 |        "    </tr>\n",
2727 |        "  </tbody>\n",
2728 |        "</table>\n",
2729 |        "</div>"
2730 |       ],
2731 |       "text/plain": [
2732 |        "           P/E\n",
2733 |        "Symbol        \n",
2734 |        "FB      104.93\n",
2735 |        "YHOO      0.66"
2736 |       ]
2737 |      },
2738 |      "execution_count": 147,
2739 |      "metadata": {},
2740 |      "output_type": "execute_result"
2741 |     }
2742 |    ],
2743 |    "source": [
2744 |     "B=stockDataDF.ix[2:-2, ['P/E']];B"
2745 |    ]
2746 |   },
2747 |   {
2748 |    "cell_type": "code",
2749 |    "execution_count": 148,
2750 |    "metadata": {
2751 |     "collapsed": false
2752 |    },
2753 |    "outputs": [
2754 |     {
2755 |      "data": {
2756 |       "text/html": [
2757 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2758 |        "<table border=\"1\" class=\"dataframe\">\n",
2759 |        "  <thead>\n",
2760 |        "    <tr style=\"text-align: right;\">\n",
2761 |        "      <th></th>\n",
2762 |        "      <th>Market Cap(B)</th>\n",
2763 |        "    </tr>\n",
2764 |        "    <tr>\n",
2765 |        "      <th>Symbol</th>\n",
2766 |        "      <th></th>\n",
2767 |        "    </tr>\n",
2768 |        "  </thead>\n",
2769 |        "  <tbody>\n",
2770 |        "    <tr>\n",
2771 |        "      <th>GOOG</th>\n",
2772 |        "      <td> 380.64</td>\n",
2773 |        "    </tr>\n",
2774 |        "    <tr>\n",
2775 |        "      <th>FB</th>\n",
2776 |        "      <td> 150.92</td>\n",
2777 |        "    </tr>\n",
2778 |        "    <tr>\n",
2779 |        "      <th>YHOO</th>\n",
2780 |        "      <td>  35.36</td>\n",
2781 |        "    </tr>\n",
2782 |        "    <tr>\n",
2783 |        "      <th>TWTR</th>\n",
2784 |        "      <td>  36.23</td>\n",
2785 |        "    </tr>\n",
2786 |        "  </tbody>\n",
2787 |        "</table>\n",
2788 |        "</div>"
2789 |       ],
2790 |       "text/plain": [
2791 |        "        Market Cap(B)\n",
2792 |        "Symbol               \n",
2793 |        "GOOG           380.64\n",
2794 |        "FB             150.92\n",
2795 |        "YHOO            35.36\n",
2796 |        "TWTR            36.23"
2797 |       ]
2798 |      },
2799 |      "execution_count": 148,
2800 |      "metadata": {},
2801 |      "output_type": "execute_result"
2802 |     }
2803 |    ],
2804 |    "source": [
2805 |     "C=stockDataDF.ix[1:5, ['Market Cap(B)']];C"
2806 |    ]
2807 |   },
2808 |   {
2809 |    "cell_type": "markdown",
2810 |    "metadata": {},
2811 |    "source": [
2812 |     "<b>Perform concatenation, specifying outer join:</b>"
2813 |    ]
2814 |   },
2815 |   {
2816 |    "cell_type": "code",
2817 |    "execution_count": 150,
2818 |    "metadata": {
2819 |     "collapsed": false
2820 |    },
2821 |    "outputs": [
2822 |     {
2823 |      "data": {
2824 |       "text/html": [
2825 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2826 |        "<table border=\"1\" class=\"dataframe\">\n",
2827 |        "  <thead>\n",
2828 |        "    <tr style=\"text-align: right;\">\n",
2829 |        "      <th></th>\n",
2830 |        "      <th>Closing price</th>\n",
2831 |        "      <th>EPS</th>\n",
2832 |        "      <th>P/E</th>\n",
2833 |        "      <th>Market Cap(B)</th>\n",
2834 |        "    </tr>\n",
2835 |        "  </thead>\n",
2836 |        "  <tbody>\n",
2837 |        "    <tr>\n",
2838 |        "      <th>AMZN</th>\n",
2839 |        "      <td>  346.15</td>\n",
2840 |        "      <td>  0.59</td>\n",
2841 |        "      <td>    NaN</td>\n",
2842 |        "      <td>    NaN</td>\n",
2843 |        "    </tr>\n",
2844 |        "    <tr>\n",
2845 |        "      <th>FB</th>\n",
2846 |        "      <td>   61.48</td>\n",
2847 |        "      <td>  0.59</td>\n",
2848 |        "      <td> 104.93</td>\n",
2849 |        "      <td> 150.92</td>\n",
2850 |        "    </tr>\n",
2851 |        "    <tr>\n",
2852 |        "      <th>GOOG</th>\n",
2853 |        "      <td> 1133.43</td>\n",
2854 |        "      <td> 36.05</td>\n",
2855 |        "      <td>    NaN</td>\n",
2856 |        "      <td> 380.64</td>\n",
2857 |        "    </tr>\n",
2858 |        "    <tr>\n",
2859 |        "      <th>TWTR</th>\n",
2860 |        "      <td>     NaN</td>\n",
2861 |        "      <td>   NaN</td>\n",
2862 |        "      <td>    NaN</td>\n",
2863 |        "      <td>  36.23</td>\n",
2864 |        "    </tr>\n",
2865 |        "    <tr>\n",
2866 |        "      <th>YHOO</th>\n",
2867 |        "      <td>   34.90</td>\n",
2868 |        "      <td>  1.27</td>\n",
2869 |        "      <td>   0.66</td>\n",
2870 |        "      <td>  35.36</td>\n",
2871 |        "    </tr>\n",
2872 |        "  </tbody>\n",
2873 |        "</table>\n",
2874 |        "</div>"
2875 |       ],
2876 |       "text/plain": [
2877 |        "      Closing price    EPS     P/E  Market Cap(B)\n",
2878 |        "AMZN         346.15   0.59     NaN            NaN\n",
2879 |        "FB            61.48   0.59  104.93         150.92\n",
2880 |        "GOOG        1133.43  36.05     NaN         380.64\n",
2881 |        "TWTR            NaN    NaN     NaN          36.23\n",
2882 |        "YHOO          34.90   1.27    0.66          35.36"
2883 |       ]
2884 |      },
2885 |      "execution_count": 150,
2886 |      "metadata": {},
2887 |      "output_type": "execute_result"
2888 |     }
2889 |    ],
2890 |    "source": [
2891 |     "pd.concat([A,B,C],axis=1) # outer join"
2892 |    ]
2893 |   },
2894 |   {
2895 |    "cell_type": "markdown",
2896 |    "metadata": {},
2897 |    "source": [
2898 |     "<b>Perform concatenation, specifying inner join:</b>"
2899 |    ]
2900 |   },
2901 |   {
2902 |    "cell_type": "code",
2903 |    "execution_count": 151,
2904 |    "metadata": {
2905 |     "collapsed": false
2906 |    },
2907 |    "outputs": [
2908 |     {
2909 |      "data": {
2910 |       "text/html": [
2911 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2912 |        "<table border=\"1\" class=\"dataframe\">\n",
2913 |        "  <thead>\n",
2914 |        "    <tr style=\"text-align: right;\">\n",
2915 |        "      <th></th>\n",
2916 |        "      <th>Closing price</th>\n",
2917 |        "      <th>EPS</th>\n",
2918 |        "      <th>P/E</th>\n",
2919 |        "      <th>Market Cap(B)</th>\n",
2920 |        "    </tr>\n",
2921 |        "    <tr>\n",
2922 |        "      <th>Symbol</th>\n",
2923 |        "      <th></th>\n",
2924 |        "      <th></th>\n",
2925 |        "      <th></th>\n",
2926 |        "      <th></th>\n",
2927 |        "    </tr>\n",
2928 |        "  </thead>\n",
2929 |        "  <tbody>\n",
2930 |        "    <tr>\n",
2931 |        "      <th>FB</th>\n",
2932 |        "      <td> 61.48</td>\n",
2933 |        "      <td> 0.59</td>\n",
2934 |        "      <td> 104.93</td>\n",
2935 |        "      <td> 150.92</td>\n",
2936 |        "    </tr>\n",
2937 |        "    <tr>\n",
2938 |        "      <th>YHOO</th>\n",
2939 |        "      <td> 34.90</td>\n",
2940 |        "      <td> 1.27</td>\n",
2941 |        "      <td>   0.66</td>\n",
2942 |        "      <td>  35.36</td>\n",
2943 |        "    </tr>\n",
2944 |        "  </tbody>\n",
2945 |        "</table>\n",
2946 |        "</div>"
2947 |       ],
2948 |       "text/plain": [
2949 |        "        Closing price   EPS     P/E  Market Cap(B)\n",
2950 |        "Symbol                                            \n",
2951 |        "FB              61.48  0.59  104.93         150.92\n",
2952 |        "YHOO            34.90  1.27    0.66          35.36"
2953 |       ]
2954 |      },
2955 |      "execution_count": 151,
2956 |      "metadata": {},
2957 |      "output_type": "execute_result"
2958 |     }
2959 |    ],
2960 |    "source": [
2961 |     "pd.concat([A,B,C],axis=1, join='inner') # Inner join"
2962 |    ]
2963 |   },
2964 |   {
2965 |    "cell_type": "markdown",
2966 |    "metadata": {},
2967 |    "source": [
2968 |     "<b>Join on specific index from original DataFrame:</b>"
2969 |    ]
2970 |   },
2971 |   {
2972 |    "cell_type": "code",
2973 |    "execution_count": 152,
2974 |    "metadata": {
2975 |     "collapsed": false
2976 |    },
2977 |    "outputs": [
2978 |     {
2979 |      "data": {
2980 |       "text/html": [
2981 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
2982 |        "<table border=\"1\" class=\"dataframe\">\n",
2983 |        "  <thead>\n",
2984 |        "    <tr style=\"text-align: right;\">\n",
2985 |        "      <th></th>\n",
2986 |        "      <th>Closing price</th>\n",
2987 |        "      <th>EPS</th>\n",
2988 |        "      <th>P/E</th>\n",
2989 |        "      <th>Market Cap(B)</th>\n",
2990 |        "    </tr>\n",
2991 |        "    <tr>\n",
2992 |        "      <th>Symbol</th>\n",
2993 |        "      <th></th>\n",
2994 |        "      <th></th>\n",
2995 |        "      <th></th>\n",
2996 |        "      <th></th>\n",
2997 |        "    </tr>\n",
2998 |        "  </thead>\n",
2999 |        "  <tbody>\n",
3000 |        "    <tr>\n",
3001 |        "      <th>AMZN</th>\n",
3002 |        "      <td>  346.15</td>\n",
3003 |        "      <td>  0.59</td>\n",
3004 |        "      <td>    NaN</td>\n",
3005 |        "      <td>    NaN</td>\n",
3006 |        "    </tr>\n",
3007 |        "    <tr>\n",
3008 |        "      <th>GOOG</th>\n",
3009 |        "      <td> 1133.43</td>\n",
3010 |        "      <td> 36.05</td>\n",
3011 |        "      <td>    NaN</td>\n",
3012 |        "      <td> 380.64</td>\n",
3013 |        "    </tr>\n",
3014 |        "    <tr>\n",
3015 |        "      <th>FB</th>\n",
3016 |        "      <td>   61.48</td>\n",
3017 |        "      <td>  0.59</td>\n",
3018 |        "      <td> 104.93</td>\n",
3019 |        "      <td> 150.92</td>\n",
3020 |        "    </tr>\n",
3021 |        "    <tr>\n",
3022 |        "      <th>YHOO</th>\n",
3023 |        "      <td>   34.90</td>\n",
3024 |        "      <td>  1.27</td>\n",
3025 |        "      <td>   0.66</td>\n",
3026 |        "      <td>  35.36</td>\n",
3027 |        "    </tr>\n",
3028 |        "    <tr>\n",
3029 |        "      <th>TWTR</th>\n",
3030 |        "      <td>     NaN</td>\n",
3031 |        "      <td>   NaN</td>\n",
3032 |        "      <td>    NaN</td>\n",
3033 |        "      <td>  36.23</td>\n",
3034 |        "    </tr>\n",
3035 |        "    <tr>\n",
3036 |        "      <th>AAPL</th>\n",
3037 |        "      <td>     NaN</td>\n",
3038 |        "      <td>   NaN</td>\n",
3039 |        "      <td>    NaN</td>\n",
3040 |        "      <td>    NaN</td>\n",
3041 |        "    </tr>\n",
3042 |        "  </tbody>\n",
3043 |        "</table>\n",
3044 |        "</div>"
3045 |       ],
3046 |       "text/plain": [
3047 |        "        Closing price    EPS     P/E  Market Cap(B)\n",
3048 |        "Symbol                                             \n",
3049 |        "AMZN           346.15   0.59     NaN            NaN\n",
3050 |        "GOOG          1133.43  36.05     NaN         380.64\n",
3051 |        "FB              61.48   0.59  104.93         150.92\n",
3052 |        "YHOO            34.90   1.27    0.66          35.36\n",
3053 |        "TWTR              NaN    NaN     NaN          36.23\n",
3054 |        "AAPL              NaN    NaN     NaN            NaN"
3055 |       ]
3056 |      },
3057 |      "execution_count": 152,
3058 |      "metadata": {},
3059 |      "output_type": "execute_result"
3060 |     }
3061 |    ],
3062 |    "source": [
3063 |     "pd.concat([A,B,C], axis=1, join_axes=[stockDataDF.index])"
3064 |    ]
3065 |   },
3066 |   {
3067 |    "cell_type": "markdown",
3068 |    "metadata": {},
3069 |    "source": [
3070 |     "<b>Another case illustrating concat</b>"
3071 |    ]
3072 |   },
3073 |   {
3074 |    "cell_type": "code",
3075 |    "execution_count": 153,
3076 |    "metadata": {
3077 |     "collapsed": false
3078 |    },
3079 |    "outputs": [
3080 |     {
3081 |      "data": {
3082 |       "text/html": [
3083 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3084 |        "<table border=\"1\" class=\"dataframe\">\n",
3085 |        "  <thead>\n",
3086 |        "    <tr style=\"text-align: right;\">\n",
3087 |        "      <th></th>\n",
3088 |        "      <th>0</th>\n",
3089 |        "      <th>1</th>\n",
3090 |        "      <th>2</th>\n",
3091 |        "      <th>3</th>\n",
3092 |        "    </tr>\n",
3093 |        "  </thead>\n",
3094 |        "  <tbody>\n",
3095 |        "    <tr>\n",
3096 |        "      <th>0</th>\n",
3097 |        "      <td>-1.749765</td>\n",
3098 |        "      <td> 0.342680</td>\n",
3099 |        "      <td> 1.153036</td>\n",
3100 |        "      <td>-0.252436</td>\n",
3101 |        "    </tr>\n",
3102 |        "    <tr>\n",
3103 |        "      <th>1</th>\n",
3104 |        "      <td> 0.981321</td>\n",
3105 |        "      <td> 0.514219</td>\n",
3106 |        "      <td> 0.221180</td>\n",
3107 |        "      <td>-1.070043</td>\n",
3108 |        "    </tr>\n",
3109 |        "    <tr>\n",
3110 |        "      <th>2</th>\n",
3111 |        "      <td>-0.189496</td>\n",
3112 |        "      <td> 0.255001</td>\n",
3113 |        "      <td>-0.458027</td>\n",
3114 |        "      <td> 0.435163</td>\n",
3115 |        "    </tr>\n",
3116 |        "  </tbody>\n",
3117 |        "</table>\n",
3118 |        "</div>"
3119 |       ],
3120 |       "text/plain": [
3121 |        "          0         1         2         3\n",
3122 |        "0 -1.749765  0.342680  1.153036 -0.252436\n",
3123 |        "1  0.981321  0.514219  0.221180 -1.070043\n",
3124 |        "2 -0.189496  0.255001 -0.458027  0.435163"
3125 |       ]
3126 |      },
3127 |      "execution_count": 153,
3128 |      "metadata": {},
3129 |      "output_type": "execute_result"
3130 |     }
3131 |    ],
3132 |    "source": [
3133 |     "np.random.seed(100)\n",
3134 |     "normDF=pd.DataFrame(np.random.randn(3,4));normDF\n"
3135 |    ]
3136 |   },
3137 |   {
3138 |    "cell_type": "code",
3139 |    "execution_count": 154,
3140 |    "metadata": {
3141 |     "collapsed": false
3142 |    },
3143 |    "outputs": [
3144 |     {
3145 |      "data": {
3146 |       "text/html": [
3147 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3148 |        "<table border=\"1\" class=\"dataframe\">\n",
3149 |        "  <thead>\n",
3150 |        "    <tr style=\"text-align: right;\">\n",
3151 |        "      <th></th>\n",
3152 |        "      <th>0</th>\n",
3153 |        "      <th>1</th>\n",
3154 |        "      <th>2</th>\n",
3155 |        "      <th>3</th>\n",
3156 |        "    </tr>\n",
3157 |        "  </thead>\n",
3158 |        "  <tbody>\n",
3159 |        "    <tr>\n",
3160 |        "      <th>0</th>\n",
3161 |        "      <td> 57</td>\n",
3162 |        "      <td> 50</td>\n",
3163 |        "      <td> 57</td>\n",
3164 |        "      <td> 50</td>\n",
3165 |        "    </tr>\n",
3166 |        "    <tr>\n",
3167 |        "      <th>1</th>\n",
3168 |        "      <td> 48</td>\n",
3169 |        "      <td> 56</td>\n",
3170 |        "      <td> 49</td>\n",
3171 |        "      <td> 43</td>\n",
3172 |        "    </tr>\n",
3173 |        "    <tr>\n",
3174 |        "      <th>2</th>\n",
3175 |        "      <td> 40</td>\n",
3176 |        "      <td> 47</td>\n",
3177 |        "      <td> 49</td>\n",
3178 |        "      <td> 55</td>\n",
3179 |        "    </tr>\n",
3180 |        "  </tbody>\n",
3181 |        "</table>\n",
3182 |        "</div>"
3183 |       ],
3184 |       "text/plain": [
3185 |        "    0   1   2   3\n",
3186 |        "0  57  50  57  50\n",
3187 |        "1  48  56  49  43\n",
3188 |        "2  40  47  49  55"
3189 |       ]
3190 |      },
3191 |      "execution_count": 154,
3192 |      "metadata": {},
3193 |      "output_type": "execute_result"
3194 |     }
3195 |    ],
3196 |    "source": [
3197 |     "binomDF=pd.DataFrame(np.random.binomial(100,0.5,(3,4)));binomDF"
3198 |    ]
3199 |   },
3200 |   {
3201 |    "cell_type": "code",
3202 |    "execution_count": 155,
3203 |    "metadata": {
3204 |     "collapsed": false
3205 |    },
3206 |    "outputs": [
3207 |     {
3208 |      "data": {
3209 |       "text/html": [
3210 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3211 |        "<table border=\"1\" class=\"dataframe\">\n",
3212 |        "  <thead>\n",
3213 |        "    <tr style=\"text-align: right;\">\n",
3214 |        "      <th></th>\n",
3215 |        "      <th>0</th>\n",
3216 |        "      <th>1</th>\n",
3217 |        "      <th>2</th>\n",
3218 |        "      <th>3</th>\n",
3219 |        "    </tr>\n",
3220 |        "  </thead>\n",
3221 |        "  <tbody>\n",
3222 |        "    <tr>\n",
3223 |        "      <th>0</th>\n",
3224 |        "      <td> 93</td>\n",
3225 |        "      <td> 96</td>\n",
3226 |        "      <td>  96</td>\n",
3227 |        "      <td>  89</td>\n",
3228 |        "    </tr>\n",
3229 |        "    <tr>\n",
3230 |        "      <th>1</th>\n",
3231 |        "      <td> 76</td>\n",
3232 |        "      <td> 96</td>\n",
3233 |        "      <td> 104</td>\n",
3234 |        "      <td> 103</td>\n",
3235 |        "    </tr>\n",
3236 |        "    <tr>\n",
3237 |        "      <th>2</th>\n",
3238 |        "      <td> 96</td>\n",
3239 |        "      <td> 93</td>\n",
3240 |        "      <td> 107</td>\n",
3241 |        "      <td>  84</td>\n",
3242 |        "    </tr>\n",
3243 |        "  </tbody>\n",
3244 |        "</table>\n",
3245 |        "</div>"
3246 |       ],
3247 |       "text/plain": [
3248 |        "    0   1    2    3\n",
3249 |        "0  93  96   96   89\n",
3250 |        "1  76  96  104  103\n",
3251 |        "2  96  93  107   84"
3252 |       ]
3253 |      },
3254 |      "execution_count": 155,
3255 |      "metadata": {},
3256 |      "output_type": "execute_result"
3257 |     }
3258 |    ],
3259 |    "source": [
3260 |     "poissonDF=pd.DataFrame(np.random.poisson(100,(3,4)));poissonDF"
3261 |    ]
3262 |   },
3263 |   {
3264 |    "cell_type": "code",
3265 |    "execution_count": 156,
3266 |    "metadata": {
3267 |     "collapsed": true
3268 |    },
3269 |    "outputs": [],
3270 |    "source": [
3271 |     "rand_distribs=[normDF,binomDF,poissonDF]"
3272 |    ]
3273 |   },
3274 |   {
3275 |    "cell_type": "code",
3276 |    "execution_count": 157,
3277 |    "metadata": {
3278 |     "collapsed": false
3279 |    },
3280 |    "outputs": [
3281 |     {
3282 |      "data": {
3283 |       "text/html": [
3284 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3285 |        "<table border=\"1\" class=\"dataframe\">\n",
3286 |        "  <thead>\n",
3287 |        "    <tr style=\"text-align: right;\">\n",
3288 |        "      <th></th>\n",
3289 |        "      <th></th>\n",
3290 |        "      <th>0</th>\n",
3291 |        "      <th>1</th>\n",
3292 |        "      <th>2</th>\n",
3293 |        "      <th>3</th>\n",
3294 |        "    </tr>\n",
3295 |        "  </thead>\n",
3296 |        "  <tbody>\n",
3297 |        "    <tr>\n",
3298 |        "      <th rowspan=\"3\" valign=\"top\">Normal</th>\n",
3299 |        "      <th>0</th>\n",
3300 |        "      <td> -1.749765</td>\n",
3301 |        "      <td>  0.342680</td>\n",
3302 |        "      <td>   1.153036</td>\n",
3303 |        "      <td>  -0.252436</td>\n",
3304 |        "    </tr>\n",
3305 |        "    <tr>\n",
3306 |        "      <th>1</th>\n",
3307 |        "      <td>  0.981321</td>\n",
3308 |        "      <td>  0.514219</td>\n",
3309 |        "      <td>   0.221180</td>\n",
3310 |        "      <td>  -1.070043</td>\n",
3311 |        "    </tr>\n",
3312 |        "    <tr>\n",
3313 |        "      <th>2</th>\n",
3314 |        "      <td> -0.189496</td>\n",
3315 |        "      <td>  0.255001</td>\n",
3316 |        "      <td>  -0.458027</td>\n",
3317 |        "      <td>   0.435163</td>\n",
3318 |        "    </tr>\n",
3319 |        "    <tr>\n",
3320 |        "      <th rowspan=\"3\" valign=\"top\">Binomial</th>\n",
3321 |        "      <th>0</th>\n",
3322 |        "      <td> 57.000000</td>\n",
3323 |        "      <td> 50.000000</td>\n",
3324 |        "      <td>  57.000000</td>\n",
3325 |        "      <td>  50.000000</td>\n",
3326 |        "    </tr>\n",
3327 |        "    <tr>\n",
3328 |        "      <th>1</th>\n",
3329 |        "      <td> 48.000000</td>\n",
3330 |        "      <td> 56.000000</td>\n",
3331 |        "      <td>  49.000000</td>\n",
3332 |        "      <td>  43.000000</td>\n",
3333 |        "    </tr>\n",
3334 |        "    <tr>\n",
3335 |        "      <th>2</th>\n",
3336 |        "      <td> 40.000000</td>\n",
3337 |        "      <td> 47.000000</td>\n",
3338 |        "      <td>  49.000000</td>\n",
3339 |        "      <td>  55.000000</td>\n",
3340 |        "    </tr>\n",
3341 |        "    <tr>\n",
3342 |        "      <th rowspan=\"3\" valign=\"top\">Poisson</th>\n",
3343 |        "      <th>0</th>\n",
3344 |        "      <td> 93.000000</td>\n",
3345 |        "      <td> 96.000000</td>\n",
3346 |        "      <td>  96.000000</td>\n",
3347 |        "      <td>  89.000000</td>\n",
3348 |        "    </tr>\n",
3349 |        "    <tr>\n",
3350 |        "      <th>1</th>\n",
3351 |        "      <td> 76.000000</td>\n",
3352 |        "      <td> 96.000000</td>\n",
3353 |        "      <td> 104.000000</td>\n",
3354 |        "      <td> 103.000000</td>\n",
3355 |        "    </tr>\n",
3356 |        "    <tr>\n",
3357 |        "      <th>2</th>\n",
3358 |        "      <td> 96.000000</td>\n",
3359 |        "      <td> 93.000000</td>\n",
3360 |        "      <td> 107.000000</td>\n",
3361 |        "      <td>  84.000000</td>\n",
3362 |        "    </tr>\n",
3363 |        "  </tbody>\n",
3364 |        "</table>\n",
3365 |        "</div>"
3366 |       ],
3367 |       "text/plain": [
3368 |        "                    0          1           2           3\n",
3369 |        "Normal   0  -1.749765   0.342680    1.153036   -0.252436\n",
3370 |        "         1   0.981321   0.514219    0.221180   -1.070043\n",
3371 |        "         2  -0.189496   0.255001   -0.458027    0.435163\n",
3372 |        "Binomial 0  57.000000  50.000000   57.000000   50.000000\n",
3373 |        "         1  48.000000  56.000000   49.000000   43.000000\n",
3374 |        "         2  40.000000  47.000000   49.000000   55.000000\n",
3375 |        "Poisson  0  93.000000  96.000000   96.000000   89.000000\n",
3376 |        "         1  76.000000  96.000000  104.000000  103.000000\n",
3377 |        "         2  96.000000  93.000000  107.000000   84.000000"
3378 |       ]
3379 |      },
3380 |      "execution_count": 157,
3381 |      "metadata": {},
3382 |      "output_type": "execute_result"
3383 |     }
3384 |    ],
3385 |    "source": [
3386 |     "rand_distribsDF=pd.concat(rand_distribs,keys=['Normal', 'Binomial', 'Poisson']);rand_distribsDF"
3387 |    ]
3388 |   },
3389 |   {
3390 |    "cell_type": "markdown",
3391 |    "metadata": {},
3392 |    "source": [
3393 |     "<h3>Using append</h3>"
3394 |    ]
3395 |   },
3396 |   {
3397 |    "cell_type": "code",
3398 |    "execution_count": 158,
3399 |    "metadata": {
3400 |     "collapsed": false
3401 |    },
3402 |    "outputs": [
3403 |     {
3404 |      "data": {
3405 |       "text/html": [
3406 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3407 |        "<table border=\"1\" class=\"dataframe\">\n",
3408 |        "  <thead>\n",
3409 |        "    <tr style=\"text-align: right;\">\n",
3410 |        "      <th></th>\n",
3411 |        "      <th>Closing price</th>\n",
3412 |        "      <th>EPS</th>\n",
3413 |        "      <th>Shares Outstanding(M)</th>\n",
3414 |        "    </tr>\n",
3415 |        "    <tr>\n",
3416 |        "      <th>Symbol</th>\n",
3417 |        "      <th></th>\n",
3418 |        "      <th></th>\n",
3419 |        "      <th></th>\n",
3420 |        "    </tr>\n",
3421 |        "  </thead>\n",
3422 |        "  <tbody>\n",
3423 |        "    <tr>\n",
3424 |        "      <th>AMZN</th>\n",
3425 |        "      <td>  346.15</td>\n",
3426 |        "      <td>  0.59</td>\n",
3427 |        "      <td> 459.00</td>\n",
3428 |        "    </tr>\n",
3429 |        "    <tr>\n",
3430 |        "      <th>GOOG</th>\n",
3431 |        "      <td> 1133.43</td>\n",
3432 |        "      <td> 36.05</td>\n",
3433 |        "      <td> 335.83</td>\n",
3434 |        "    </tr>\n",
3435 |        "  </tbody>\n",
3436 |        "</table>\n",
3437 |        "</div>"
3438 |       ],
3439 |       "text/plain": [
3440 |        "        Closing price    EPS  Shares Outstanding(M)\n",
3441 |        "Symbol                                             \n",
3442 |        "AMZN           346.15   0.59                 459.00\n",
3443 |        "GOOG          1133.43  36.05                 335.83"
3444 |       ]
3445 |      },
3446 |      "execution_count": 158,
3447 |      "metadata": {},
3448 |      "output_type": "execute_result"
3449 |     }
3450 |    ],
3451 |    "source": [
3452 |     "stockDataA=stockDataDF.ix[:2,:3]\n",
3453 |     "stockDataA\n"
3454 |    ]
3455 |   },
3456 |   {
3457 |    "cell_type": "code",
3458 |    "execution_count": 159,
3459 |    "metadata": {
3460 |     "collapsed": false
3461 |    },
3462 |    "outputs": [
3463 |     {
3464 |      "data": {
3465 |       "text/html": [
3466 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3467 |        "<table border=\"1\" class=\"dataframe\">\n",
3468 |        "  <thead>\n",
3469 |        "    <tr style=\"text-align: right;\">\n",
3470 |        "      <th></th>\n",
3471 |        "      <th>Closing price</th>\n",
3472 |        "      <th>EPS</th>\n",
3473 |        "      <th>Shares Outstanding(M)</th>\n",
3474 |        "      <th>Beta</th>\n",
3475 |        "      <th>P/E</th>\n",
3476 |        "      <th>Market Cap(B)</th>\n",
3477 |        "    </tr>\n",
3478 |        "    <tr>\n",
3479 |        "      <th>Symbol</th>\n",
3480 |        "      <th></th>\n",
3481 |        "      <th></th>\n",
3482 |        "      <th></th>\n",
3483 |        "      <th></th>\n",
3484 |        "      <th></th>\n",
3485 |        "      <th></th>\n",
3486 |        "    </tr>\n",
3487 |        "  </thead>\n",
3488 |        "  <tbody>\n",
3489 |        "    <tr>\n",
3490 |        "      <th>FB</th>\n",
3491 |        "      <td>  61.48</td>\n",
3492 |        "      <td>  0.59</td>\n",
3493 |        "      <td> 2450.00</td>\n",
3494 |        "      <td>   NaN</td>\n",
3495 |        "      <td> 104.93</td>\n",
3496 |        "      <td> 150.92</td>\n",
3497 |        "    </tr>\n",
3498 |        "    <tr>\n",
3499 |        "      <th>YHOO</th>\n",
3500 |        "      <td>  34.90</td>\n",
3501 |        "      <td>  1.27</td>\n",
3502 |        "      <td> 1010.00</td>\n",
3503 |        "      <td> 27.48</td>\n",
3504 |        "      <td>   0.66</td>\n",
3505 |        "      <td>  35.36</td>\n",
3506 |        "    </tr>\n",
3507 |        "    <tr>\n",
3508 |        "      <th>TWTR</th>\n",
3509 |        "      <td>  65.25</td>\n",
3510 |        "      <td> -0.30</td>\n",
3511 |        "      <td>  555.20</td>\n",
3512 |        "      <td>   NaN</td>\n",
3513 |        "      <td>    NaN</td>\n",
3514 |        "      <td>  36.23</td>\n",
3515 |        "    </tr>\n",
3516 |        "    <tr>\n",
3517 |        "      <th>AAPL</th>\n",
3518 |        "      <td> 501.53</td>\n",
3519 |        "      <td> 40.32</td>\n",
3520 |        "      <td>  892.45</td>\n",
3521 |        "      <td> 12.44</td>\n",
3522 |        "      <td> 447.59</td>\n",
3523 |        "      <td>   0.84</td>\n",
3524 |        "    </tr>\n",
3525 |        "  </tbody>\n",
3526 |        "</table>\n",
3527 |        "</div>"
3528 |       ],
3529 |       "text/plain": [
3530 |        "        Closing price    EPS  Shares Outstanding(M)   Beta     P/E  \\\n",
3531 |        "Symbol                                                               \n",
3532 |        "FB              61.48   0.59                2450.00    NaN  104.93   \n",
3533 |        "YHOO            34.90   1.27                1010.00  27.48    0.66   \n",
3534 |        "TWTR            65.25  -0.30                 555.20    NaN     NaN   \n",
3535 |        "AAPL           501.53  40.32                 892.45  12.44  447.59   \n",
3536 |        "\n",
3537 |        "        Market Cap(B)  \n",
3538 |        "Symbol                 \n",
3539 |        "FB             150.92  \n",
3540 |        "YHOO            35.36  \n",
3541 |        "TWTR            36.23  \n",
3542 |        "AAPL             0.84  "
3543 |       ]
3544 |      },
3545 |      "execution_count": 159,
3546 |      "metadata": {},
3547 |      "output_type": "execute_result"
3548 |     }
3549 |    ],
3550 |    "source": [
3551 |     "stockDataB=stockDataDF[2:]\n",
3552 |     "stockDataB\n"
3553 |    ]
3554 |   },
3555 |   {
3556 |    "cell_type": "code",
3557 |    "execution_count": 161,
3558 |    "metadata": {
3559 |     "collapsed": false
3560 |    },
3561 |    "outputs": [
3562 |     {
3563 |      "data": {
3564 |       "text/html": [
3565 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3566 |        "<table border=\"1\" class=\"dataframe\">\n",
3567 |        "  <thead>\n",
3568 |        "    <tr style=\"text-align: right;\">\n",
3569 |        "      <th></th>\n",
3570 |        "      <th>Beta</th>\n",
3571 |        "      <th>Closing price</th>\n",
3572 |        "      <th>EPS</th>\n",
3573 |        "      <th>Market Cap(B)</th>\n",
3574 |        "      <th>P/E</th>\n",
3575 |        "      <th>Shares Outstanding(M)</th>\n",
3576 |        "    </tr>\n",
3577 |        "    <tr>\n",
3578 |        "      <th>Symbol</th>\n",
3579 |        "      <th></th>\n",
3580 |        "      <th></th>\n",
3581 |        "      <th></th>\n",
3582 |        "      <th></th>\n",
3583 |        "      <th></th>\n",
3584 |        "      <th></th>\n",
3585 |        "    </tr>\n",
3586 |        "  </thead>\n",
3587 |        "  <tbody>\n",
3588 |        "    <tr>\n",
3589 |        "      <th>AMZN</th>\n",
3590 |        "      <td>   NaN</td>\n",
3591 |        "      <td>  346.15</td>\n",
3592 |        "      <td>  0.59</td>\n",
3593 |        "      <td>    NaN</td>\n",
3594 |        "      <td>    NaN</td>\n",
3595 |        "      <td>  459.00</td>\n",
3596 |        "    </tr>\n",
3597 |        "    <tr>\n",
3598 |        "      <th>GOOG</th>\n",
3599 |        "      <td>   NaN</td>\n",
3600 |        "      <td> 1133.43</td>\n",
3601 |        "      <td> 36.05</td>\n",
3602 |        "      <td>    NaN</td>\n",
3603 |        "      <td>    NaN</td>\n",
3604 |        "      <td>  335.83</td>\n",
3605 |        "    </tr>\n",
3606 |        "    <tr>\n",
3607 |        "      <th>FB</th>\n",
3608 |        "      <td>   NaN</td>\n",
3609 |        "      <td>   61.48</td>\n",
3610 |        "      <td>  0.59</td>\n",
3611 |        "      <td> 150.92</td>\n",
3612 |        "      <td> 104.93</td>\n",
3613 |        "      <td> 2450.00</td>\n",
3614 |        "    </tr>\n",
3615 |        "    <tr>\n",
3616 |        "      <th>YHOO</th>\n",
3617 |        "      <td> 27.48</td>\n",
3618 |        "      <td>   34.90</td>\n",
3619 |        "      <td>  1.27</td>\n",
3620 |        "      <td>  35.36</td>\n",
3621 |        "      <td>   0.66</td>\n",
3622 |        "      <td> 1010.00</td>\n",
3623 |        "    </tr>\n",
3624 |        "    <tr>\n",
3625 |        "      <th>TWTR</th>\n",
3626 |        "      <td>   NaN</td>\n",
3627 |        "      <td>   65.25</td>\n",
3628 |        "      <td> -0.30</td>\n",
3629 |        "      <td>  36.23</td>\n",
3630 |        "      <td>    NaN</td>\n",
3631 |        "      <td>  555.20</td>\n",
3632 |        "    </tr>\n",
3633 |        "    <tr>\n",
3634 |        "      <th>AAPL</th>\n",
3635 |        "      <td> 12.44</td>\n",
3636 |        "      <td>  501.53</td>\n",
3637 |        "      <td> 40.32</td>\n",
3638 |        "      <td>   0.84</td>\n",
3639 |        "      <td> 447.59</td>\n",
3640 |        "      <td>  892.45</td>\n",
3641 |        "    </tr>\n",
3642 |        "  </tbody>\n",
3643 |        "</table>\n",
3644 |        "</div>"
3645 |       ],
3646 |       "text/plain": [
3647 |        "         Beta  Closing price    EPS  Market Cap(B)     P/E  \\\n",
3648 |        "Symbol                                                       \n",
3649 |        "AMZN      NaN         346.15   0.59            NaN     NaN   \n",
3650 |        "GOOG      NaN        1133.43  36.05            NaN     NaN   \n",
3651 |        "FB        NaN          61.48   0.59         150.92  104.93   \n",
3652 |        "YHOO    27.48          34.90   1.27          35.36    0.66   \n",
3653 |        "TWTR      NaN          65.25  -0.30          36.23     NaN   \n",
3654 |        "AAPL    12.44         501.53  40.32           0.84  447.59   \n",
3655 |        "\n",
3656 |        "        Shares Outstanding(M)  \n",
3657 |        "Symbol                         \n",
3658 |        "AMZN                   459.00  \n",
3659 |        "GOOG                   335.83  \n",
3660 |        "FB                    2450.00  \n",
3661 |        "YHOO                  1010.00  \n",
3662 |        "TWTR                   555.20  \n",
3663 |        "AAPL                   892.45  "
3664 |       ]
3665 |      },
3666 |      "execution_count": 161,
3667 |      "metadata": {},
3668 |      "output_type": "execute_result"
3669 |     }
3670 |    ],
3671 |    "source": [
3672 |     "stockDataA.append(stockDataB)"
3673 |    ]
3674 |   },
3675 |   {
3676 |    "cell_type": "code",
3677 |    "execution_count": 160,
3678 |    "metadata": {
3679 |     "collapsed": false
3680 |    },
3681 |    "outputs": [
3682 |     {
3683 |      "data": {
3684 |       "text/html": [
3685 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3686 |        "<table border=\"1\" class=\"dataframe\">\n",
3687 |        "  <thead>\n",
3688 |        "    <tr style=\"text-align: right;\">\n",
3689 |        "      <th></th>\n",
3690 |        "      <th>Closing price</th>\n",
3691 |        "      <th>EPS</th>\n",
3692 |        "      <th>Shares Outstanding(M)</th>\n",
3693 |        "      <th>Beta</th>\n",
3694 |        "      <th>P/E</th>\n",
3695 |        "      <th>Market Cap(B)</th>\n",
3696 |        "    </tr>\n",
3697 |        "    <tr>\n",
3698 |        "      <th>Symbol</th>\n",
3699 |        "      <th></th>\n",
3700 |        "      <th></th>\n",
3701 |        "      <th></th>\n",
3702 |        "      <th></th>\n",
3703 |        "      <th></th>\n",
3704 |        "      <th></th>\n",
3705 |        "    </tr>\n",
3706 |        "  </thead>\n",
3707 |        "  <tbody>\n",
3708 |        "    <tr>\n",
3709 |        "      <th>AMZN</th>\n",
3710 |        "      <td>  346.15</td>\n",
3711 |        "      <td>  0.59</td>\n",
3712 |        "      <td>  459.00</td>\n",
3713 |        "      <td>   NaN</td>\n",
3714 |        "      <td>    NaN</td>\n",
3715 |        "      <td>    NaN</td>\n",
3716 |        "    </tr>\n",
3717 |        "    <tr>\n",
3718 |        "      <th>GOOG</th>\n",
3719 |        "      <td> 1133.43</td>\n",
3720 |        "      <td> 36.05</td>\n",
3721 |        "      <td>  335.83</td>\n",
3722 |        "      <td>   NaN</td>\n",
3723 |        "      <td>    NaN</td>\n",
3724 |        "      <td>    NaN</td>\n",
3725 |        "    </tr>\n",
3726 |        "    <tr>\n",
3727 |        "      <th>FB</th>\n",
3728 |        "      <td>   61.48</td>\n",
3729 |        "      <td>  0.59</td>\n",
3730 |        "      <td> 2450.00</td>\n",
3731 |        "      <td>   NaN</td>\n",
3732 |        "      <td> 104.93</td>\n",
3733 |        "      <td> 150.92</td>\n",
3734 |        "    </tr>\n",
3735 |        "    <tr>\n",
3736 |        "      <th>YHOO</th>\n",
3737 |        "      <td>   34.90</td>\n",
3738 |        "      <td>  1.27</td>\n",
3739 |        "      <td> 1010.00</td>\n",
3740 |        "      <td> 27.48</td>\n",
3741 |        "      <td>   0.66</td>\n",
3742 |        "      <td>  35.36</td>\n",
3743 |        "    </tr>\n",
3744 |        "    <tr>\n",
3745 |        "      <th>TWTR</th>\n",
3746 |        "      <td>   65.25</td>\n",
3747 |        "      <td> -0.30</td>\n",
3748 |        "      <td>  555.20</td>\n",
3749 |        "      <td>   NaN</td>\n",
3750 |        "      <td>    NaN</td>\n",
3751 |        "      <td>  36.23</td>\n",
3752 |        "    </tr>\n",
3753 |        "    <tr>\n",
3754 |        "      <th>AAPL</th>\n",
3755 |        "      <td>  501.53</td>\n",
3756 |        "      <td> 40.32</td>\n",
3757 |        "      <td>  892.45</td>\n",
3758 |        "      <td> 12.44</td>\n",
3759 |        "      <td> 447.59</td>\n",
3760 |        "      <td>   0.84</td>\n",
3761 |        "    </tr>\n",
3762 |        "  </tbody>\n",
3763 |        "</table>\n",
3764 |        "</div>"
3765 |       ],
3766 |       "text/plain": [
3767 |        "        Closing price    EPS  Shares Outstanding(M)   Beta     P/E  \\\n",
3768 |        "Symbol                                                               \n",
3769 |        "AMZN           346.15   0.59                 459.00    NaN     NaN   \n",
3770 |        "GOOG          1133.43  36.05                 335.83    NaN     NaN   \n",
3771 |        "FB              61.48   0.59                2450.00    NaN  104.93   \n",
3772 |        "YHOO            34.90   1.27                1010.00  27.48    0.66   \n",
3773 |        "TWTR            65.25  -0.30                 555.20    NaN     NaN   \n",
3774 |        "AAPL           501.53  40.32                 892.45  12.44  447.59   \n",
3775 |        "\n",
3776 |        "        Market Cap(B)  \n",
3777 |        "Symbol                 \n",
3778 |        "AMZN              NaN  \n",
3779 |        "GOOG              NaN  \n",
3780 |        "FB             150.92  \n",
3781 |        "YHOO            35.36  \n",
3782 |        "TWTR            36.23  \n",
3783 |        "AAPL             0.84  "
3784 |       ]
3785 |      },
3786 |      "execution_count": 160,
3787 |      "metadata": {},
3788 |      "output_type": "execute_result"
3789 |     }
3790 |    ],
3791 |    "source": [
3792 |     "stockDataA.append(stockDataB).reindex_axis(stockDataDF.columns, axis=1)"
3793 |    ]
3794 |   },
3795 |   {
3796 |    "cell_type": "markdown",
3797 |    "metadata": {},
3798 |    "source": [
3799 |     "<h3>Appending a single row to a DataFrame</h3>"
3800 |    ]
3801 |   },
3802 |   {
3803 |    "cell_type": "code",
3804 |    "execution_count": 162,
3805 |    "metadata": {
3806 |     "collapsed": false
3807 |    },
3808 |    "outputs": [
3809 |     {
3810 |      "data": {
3811 |       "text/html": [
3812 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3813 |        "<table border=\"1\" class=\"dataframe\">\n",
3814 |        "  <thead>\n",
3815 |        "    <tr style=\"text-align: right;\">\n",
3816 |        "      <th></th>\n",
3817 |        "      <th>machine learning</th>\n",
3818 |        "      <th>search</th>\n",
3819 |        "      <th>sorting</th>\n",
3820 |        "    </tr>\n",
3821 |        "  </thead>\n",
3822 |        "  <tbody>\n",
3823 |        "    <tr>\n",
3824 |        "      <th>0</th>\n",
3825 |        "      <td>        RandomForest</td>\n",
3826 |        "      <td>           DFS</td>\n",
3827 |        "      <td>   Quicksort</td>\n",
3828 |        "    </tr>\n",
3829 |        "    <tr>\n",
3830 |        "      <th>1</th>\n",
3831 |        "      <td>  K Nearest Neighbor</td>\n",
3832 |        "      <td>           BFS</td>\n",
3833 |        "      <td>   Mergesort</td>\n",
3834 |        "    </tr>\n",
3835 |        "    <tr>\n",
3836 |        "      <th>2</th>\n",
3837 |        "      <td> Logistic Regression</td>\n",
3838 |        "      <td> Binary Search</td>\n",
3839 |        "      <td>    Heapsort</td>\n",
3840 |        "    </tr>\n",
3841 |        "    <tr>\n",
3842 |        "      <th>3</th>\n",
3843 |        "      <td>  K-Means Clustering</td>\n",
3844 |        "      <td>        Linear</td>\n",
3845 |        "      <td> Bubble Sort</td>\n",
3846 |        "    </tr>\n",
3847 |        "  </tbody>\n",
3848 |        "</table>\n",
3849 |        "</div>"
3850 |       ],
3851 |       "text/plain": [
3852 |        "      machine learning         search      sorting\n",
3853 |        "0         RandomForest            DFS    Quicksort\n",
3854 |        "1   K Nearest Neighbor            BFS    Mergesort\n",
3855 |        "2  Logistic Regression  Binary Search     Heapsort\n",
3856 |        "3   K-Means Clustering         Linear  Bubble Sort"
3857 |       ]
3858 |      },
3859 |      "execution_count": 162,
3860 |      "metadata": {},
3861 |      "output_type": "execute_result"
3862 |     }
3863 |    ],
3864 |    "source": [
3865 |     "algos={'search':['DFS','BFS','Binary Search','Linear'],\n",
3866 |     "        'sorting': ['Quicksort','Mergesort','Heapsort','Bubble Sort'],\n",
3867 |     "       'machine learning':['RandomForest','K Nearest Neighbor','Logistic Regression','K-Means Clustering']}\n",
3868 |     "algoDF=pd.DataFrame(algos);algoDF\n",
3869 |     "\n"
3870 |    ]
3871 |   },
3872 |   {
3873 |    "cell_type": "code",
3874 |    "execution_count": 163,
3875 |    "metadata": {
3876 |     "collapsed": false
3877 |    },
3878 |    "outputs": [
3879 |     {
3880 |      "data": {
3881 |       "text/html": [
3882 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3883 |        "<table border=\"1\" class=\"dataframe\">\n",
3884 |        "  <thead>\n",
3885 |        "    <tr style=\"text-align: right;\">\n",
3886 |        "      <th></th>\n",
3887 |        "      <th>machine learning</th>\n",
3888 |        "      <th>search</th>\n",
3889 |        "      <th>sorting</th>\n",
3890 |        "    </tr>\n",
3891 |        "  </thead>\n",
3892 |        "  <tbody>\n",
3893 |        "    <tr>\n",
3894 |        "      <th>0</th>\n",
3895 |        "      <td>        RandomForest</td>\n",
3896 |        "      <td>           DFS</td>\n",
3897 |        "      <td>      Quicksort</td>\n",
3898 |        "    </tr>\n",
3899 |        "    <tr>\n",
3900 |        "      <th>1</th>\n",
3901 |        "      <td>  K Nearest Neighbor</td>\n",
3902 |        "      <td>           BFS</td>\n",
3903 |        "      <td>      Mergesort</td>\n",
3904 |        "    </tr>\n",
3905 |        "    <tr>\n",
3906 |        "      <th>2</th>\n",
3907 |        "      <td> Logistic Regression</td>\n",
3908 |        "      <td> Binary Search</td>\n",
3909 |        "      <td>       Heapsort</td>\n",
3910 |        "    </tr>\n",
3911 |        "    <tr>\n",
3912 |        "      <th>3</th>\n",
3913 |        "      <td>  K-Means Clustering</td>\n",
3914 |        "      <td>        Linear</td>\n",
3915 |        "      <td>    Bubble Sort</td>\n",
3916 |        "    </tr>\n",
3917 |        "    <tr>\n",
3918 |        "      <th>4</th>\n",
3919 |        "      <td>   Linear Regression</td>\n",
3920 |        "      <td>  ShortestPath</td>\n",
3921 |        "      <td> Insertion Sort</td>\n",
3922 |        "    </tr>\n",
3923 |        "  </tbody>\n",
3924 |        "</table>\n",
3925 |        "</div>"
3926 |       ],
3927 |       "text/plain": [
3928 |        "      machine learning         search         sorting\n",
3929 |        "0         RandomForest            DFS       Quicksort\n",
3930 |        "1   K Nearest Neighbor            BFS       Mergesort\n",
3931 |        "2  Logistic Regression  Binary Search        Heapsort\n",
3932 |        "3   K-Means Clustering         Linear     Bubble Sort\n",
3933 |        "4    Linear Regression   ShortestPath  Insertion Sort"
3934 |       ]
3935 |      },
3936 |      "execution_count": 163,
3937 |      "metadata": {},
3938 |      "output_type": "execute_result"
3939 |     }
3940 |    ],
3941 |    "source": [
3942 |     "moreAlgos={'search': 'ShortestPath'  , 'sorting': 'Insertion Sort',\n",
3943 |     "            'machine learning': 'Linear Regression'}\n",
3944 |     "algoDF.append(moreAlgos,ignore_index=True)\n"
3945 |    ]
3946 |   },
3947 |   {
3948 |    "cell_type": "markdown",
3949 |    "metadata": {},
3950 |    "source": [
3951 |     "<h2>SQL-Like Merging/Joining of DataFrame objects</h2>"
3952 |    ]
3953 |   },
3954 |   {
3955 |    "cell_type": "markdown",
3956 |    "metadata": {},
3957 |    "source": [
3958 |     "<b>Read stock data into DataFrame</b>"
3959 |    ]
3960 |   },
3961 |   {
3962 |    "cell_type": "code",
3963 |    "execution_count": 164,
3964 |    "metadata": {
3965 |     "collapsed": false
3966 |    },
3967 |    "outputs": [
3968 |     {
3969 |      "data": {
3970 |       "text/html": [
3971 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
3972 |        "<table border=\"1\" class=\"dataframe\">\n",
3973 |        "  <thead>\n",
3974 |        "    <tr style=\"text-align: right;\">\n",
3975 |        "      <th></th>\n",
3976 |        "      <th>TradingDate</th>\n",
3977 |        "      <th>Nasdaq</th>\n",
3978 |        "      <th>S&amp;P 500</th>\n",
3979 |        "      <th>Russell 2000</th>\n",
3980 |        "      <th>DJIA</th>\n",
3981 |        "    </tr>\n",
3982 |        "  </thead>\n",
3983 |        "  <tbody>\n",
3984 |        "    <tr>\n",
3985 |        "      <th>0</th>\n",
3986 |        "      <td> 2014/01/30</td>\n",
3987 |        "      <td> 4123.13</td>\n",
3988 |        "      <td> 1794.19</td>\n",
3989 |        "      <td> 1139.36</td>\n",
3990 |        "      <td> 15848.61</td>\n",
3991 |        "    </tr>\n",
3992 |        "    <tr>\n",
3993 |        "      <th>1</th>\n",
3994 |        "      <td> 2014/01/31</td>\n",
3995 |        "      <td> 4103.88</td>\n",
3996 |        "      <td> 1782.59</td>\n",
3997 |        "      <td> 1130.88</td>\n",
3998 |        "      <td> 15698.85</td>\n",
3999 |        "    </tr>\n",
4000 |        "    <tr>\n",
4001 |        "      <th>2</th>\n",
4002 |        "      <td> 2014/02/03</td>\n",
4003 |        "      <td> 3996.96</td>\n",
4004 |        "      <td> 1741.89</td>\n",
4005 |        "      <td> 1094.58</td>\n",
4006 |        "      <td> 15372.80</td>\n",
4007 |        "    </tr>\n",
4008 |        "    <tr>\n",
4009 |        "      <th>3</th>\n",
4010 |        "      <td> 2014/02/04</td>\n",
4011 |        "      <td> 4031.52</td>\n",
4012 |        "      <td> 1755.20</td>\n",
4013 |        "      <td> 1102.84</td>\n",
4014 |        "      <td> 15445.24</td>\n",
4015 |        "    </tr>\n",
4016 |        "    <tr>\n",
4017 |        "      <th>4</th>\n",
4018 |        "      <td> 2014/02/05</td>\n",
4019 |        "      <td> 4011.55</td>\n",
4020 |        "      <td> 1751.64</td>\n",
4021 |        "      <td> 1093.59</td>\n",
4022 |        "      <td> 15440.23</td>\n",
4023 |        "    </tr>\n",
4024 |        "    <tr>\n",
4025 |        "      <th>5</th>\n",
4026 |        "      <td> 2014/02/06</td>\n",
4027 |        "      <td> 4057.12</td>\n",
4028 |        "      <td> 1773.43</td>\n",
4029 |        "      <td> 1103.93</td>\n",
4030 |        "      <td> 15628.53</td>\n",
4031 |        "    </tr>\n",
4032 |        "  </tbody>\n",
4033 |        "</table>\n",
4034 |        "</div>"
4035 |       ],
4036 |       "text/plain": [
4037 |        "  TradingDate   Nasdaq  S&P 500  Russell 2000      DJIA\n",
4038 |        "0  2014/01/30  4123.13  1794.19       1139.36  15848.61\n",
4039 |        "1  2014/01/31  4103.88  1782.59       1130.88  15698.85\n",
4040 |        "2  2014/02/03  3996.96  1741.89       1094.58  15372.80\n",
4041 |        "3  2014/02/04  4031.52  1755.20       1102.84  15445.24\n",
4042 |        "4  2014/02/05  4011.55  1751.64       1093.59  15440.23\n",
4043 |        "5  2014/02/06  4057.12  1773.43       1103.93  15628.53"
4044 |       ]
4045 |      },
4046 |      "execution_count": 164,
4047 |      "metadata": {},
4048 |      "output_type": "execute_result"
4049 |     }
4050 |    ],
4051 |    "source": [
4052 |     "USIndexDataDF=pd.read_csv('./data/us_index_data.csv')\n",
4053 |     "USIndexDataDF\n"
4054 |    ]
4055 |   },
4056 |   {
4057 |    "cell_type": "code",
4058 |    "execution_count": null,
4059 |    "metadata": {
4060 |     "collapsed": true
4061 |    },
4062 |    "outputs": [],
4063 |    "source": []
4064 |   }
4065 |  ],
4066 |  "metadata": {
4067 |   "kernelspec": {
4068 |    "display_name": "Python 2",
4069 |    "language": "python",
4070 |    "name": "python2"
4071 |   },
4072 |   "language_info": {
4073 |    "codemirror_mode": {
4074 |     "name": "ipython",
4075 |     "version": 2
4076 |    },
4077 |    "file_extension": ".py",
4078 |    "mimetype": "text/x-python",
4079 |    "name": "python",
4080 |    "nbconvert_exporter": "python",
4081 |    "pygments_lexer": "ipython2",
4082 |    "version": "2.7.9"
4083 |   }
4084 |  },
4085 |  "nbformat": 4,
4086 |  "nbformat_minor": 0
4087 | }
4088 | 


--------------------------------------------------------------------------------
/MasteringPandas_chap7-ClassicalStatistics.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 4,
   6 |    "metadata": {
   7 |     "collapsed": true
   8 |    },
   9 |    "outputs": [],
  10 |    "source": [
  11 |     "import numpy as np\n",
  12 |     "import pandas as pd\n",
  13 |     "import matplotlib.pyplot as plt\n",
  14 |     "%matplotlib inline  "
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "code",
  19 |    "execution_count": 5,
  20 |    "metadata": {
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  23 |    "outputs": [
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 140 |        "SUVORK5CYII=\n"
 141 |       ],
 142 |       "text/plain": [
 143 |        "<matplotlib.figure.Figure at 0x10a1bd450>"
 144 |       ]
 145 |      },
 146 |      "metadata": {},
 147 |      "output_type": "display_data"
 148 |     }
 149 |    ],
 150 |    "source": [
 151 |     "plt.hist([7,0,1,2,3,7,1,2,3,4,2,7,6,5,2,1,6,8,9,7])\n",
 152 |     "plt.xlabel('x')\n",
 153 |     "plt.ylabel('Count')\n",
 154 |     "plt.title('Bimodal distribution')\n",
 155 |     "plt.show()"
 156 |    ]
 157 |   },
 158 |   {
 159 |    "cell_type": "markdown",
 160 |    "metadata": {},
 161 |    "source": [
 162 |     "<h2>Computing measures of central tendency of a dataset in Python</h2>"
 163 |    ]
 164 |   },
 165 |   {
 166 |    "cell_type": "code",
 167 |    "execution_count": 6,
 168 |    "metadata": {
 169 |     "collapsed": true
 170 |    },
 171 |    "outputs": [],
 172 |    "source": [
 173 |     "grades = [10, 10, 14, 18, 18, 5, 10, 8, 1, 12, 14, 12, 13, 1, 18]"
 174 |    ]
 175 |   },
 176 |   {
 177 |    "cell_type": "code",
 178 |    "execution_count": 7,
 179 |    "metadata": {
 180 |     "collapsed": false
 181 |    },
 182 |    "outputs": [
 183 |     {
 184 |      "data": {
 185 |       "text/plain": [
 186 |        "u'%.3f'"
 187 |       ]
 188 |      },
 189 |      "execution_count": 7,
 190 |      "metadata": {},
 191 |      "output_type": "execute_result"
 192 |     }
 193 |    ],
 194 |    "source": [
 195 |     "%precision 3"
 196 |    ]
 197 |   },
 198 |   {
 199 |    "cell_type": "code",
 200 |    "execution_count": 8,
 201 |    "metadata": {
 202 |     "collapsed": false
 203 |    },
 204 |    "outputs": [
 205 |     {
 206 |      "data": {
 207 |       "text/plain": [
 208 |        "10.933"
 209 |       ]
 210 |      },
 211 |      "execution_count": 8,
 212 |      "metadata": {},
 213 |      "output_type": "execute_result"
 214 |     }
 215 |    ],
 216 |    "source": [
 217 |     " np.mean(grades)"
 218 |    ]
 219 |   },
 220 |   {
 221 |    "cell_type": "code",
 222 |    "execution_count": 9,
 223 |    "metadata": {
 224 |     "collapsed": false
 225 |    },
 226 |    "outputs": [
 227 |     {
 228 |      "data": {
 229 |       "text/plain": [
 230 |        "12.000"
 231 |       ]
 232 |      },
 233 |      "execution_count": 9,
 234 |      "metadata": {},
 235 |      "output_type": "execute_result"
 236 |     }
 237 |    ],
 238 |    "source": [
 239 |     " np.median(grades)"
 240 |    ]
 241 |   },
 242 |   {
 243 |    "cell_type": "code",
 244 |    "execution_count": 14,
 245 |    "metadata": {
 246 |     "collapsed": false
 247 |    },
 248 |    "outputs": [
 249 |     {
 250 |      "data": {
 251 |       "text/plain": [
 252 |        "(array([10]), array([ 3.]))"
 253 |       ]
 254 |      },
 255 |      "execution_count": 14,
 256 |      "metadata": {},
 257 |      "output_type": "execute_result"
 258 |     }
 259 |    ],
 260 |    "source": [
 261 |     "from scipy import stats\n",
 262 |     "stats.mode(grades)"
 263 |    ]
 264 |   },
 265 |   {
 266 |    "cell_type": "code",
 267 |    "execution_count": 10,
 268 |    "metadata": {
 269 |     "collapsed": true
 270 |    },
 271 |    "outputs": [],
 272 |    "source": [
 273 |     "%matplotlib inline"
 274 |    ]
 275 |   },
 276 |   {
 277 |    "cell_type": "code",
 278 |    "execution_count": 12,
 279 |    "metadata": {
 280 |     "collapsed": false
 281 |    },
 282 |    "outputs": [
 283 |     {
 284 |      "data": {
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 407 |       ],
 408 |       "text/plain": [
 409 |        "<matplotlib.figure.Figure at 0x10a1bd410>"
 410 |       ]
 411 |      },
 412 |      "metadata": {},
 413 |      "output_type": "display_data"
 414 |     }
 415 |    ],
 416 |    "source": [
 417 |     "plt.hist(grades)\n",
 418 |     "plt.title('Histogram of grades')\n",
 419 |     "plt.xlabel('Grade')\n",
 420 |     "plt.ylabel('Frequency')\n",
 421 |     "plt.show()"
 422 |    ]
 423 |   },
 424 |   {
 425 |    "cell_type": "code",
 426 |    "execution_count": 22,
 427 |    "metadata": {
 428 |     "collapsed": true
 429 |    },
 430 |    "outputs": [],
 431 |    "source": [
 432 |     "%precision 2\n",
 433 |     "salaries = [17, 23, 14, 16, 19, 22, 15, 18, 18, 93, 95]"
 434 |    ]
 435 |   },
 436 |   {
 437 |    "cell_type": "code",
 438 |    "execution_count": 19,
 439 |    "metadata": {
 440 |     "collapsed": false
 441 |    },
 442 |    "outputs": [
 443 |     {
 444 |      "data": {
 445 |       "text/plain": [
 446 |        "31.82"
 447 |       ]
 448 |      },
 449 |      "execution_count": 19,
 450 |      "metadata": {},
 451 |      "output_type": "execute_result"
 452 |     }
 453 |    ],
 454 |    "source": [
 455 |     "np.mean(salaries)"
 456 |    ]
 457 |   },
 458 |   {
 459 |    "cell_type": "markdown",
 460 |    "metadata": {},
 461 |    "source": [
 462 |     "<b>Bar plot of salaries data</b>"
 463 |    ]
 464 |   },
 465 |   {
 466 |    "cell_type": "code",
 467 |    "execution_count": 20,
 468 |    "metadata": {
 469 |     "collapsed": false
 470 |    },
 471 |    "outputs": [
 472 |     {
 473 |      "data": {
 474 |       "image/png": [
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 574 |        "QgAAAABJRU5ErkJggg==\n"
 575 |       ],
 576 |       "text/plain": [
 577 |        "<matplotlib.figure.Figure at 0x7fd7cbdcb1d0>"
 578 |       ]
 579 |      },
 580 |      "metadata": {},
 581 |      "output_type": "display_data"
 582 |     }
 583 |    ],
 584 |    "source": [
 585 |     "fig = plt.figure()\n",
 586 |     "ax = fig.add_subplot(111)\n",
 587 |     "ind = np.arange(len(salaries))\n",
 588 |     "width=0.2\n",
 589 |     "rects = ax.bar(ind,salaries,width)\n",
 590 |     "ax.set_ylabel('Salaries')\n",
 591 |     "ax.set_title('Bar chart of salaries')\n",
 592 |     "plt.show()"
 593 |    ]
 594 |   },
 595 |   {
 596 |    "cell_type": "code",
 597 |    "execution_count": 23,
 598 |    "metadata": {
 599 |     "collapsed": false
 600 |    },
 601 |    "outputs": [
 602 |     {
 603 |      "data": {
 604 |       "text/plain": [
 605 |        "18.00"
 606 |       ]
 607 |      },
 608 |      "execution_count": 23,
 609 |      "metadata": {},
 610 |      "output_type": "execute_result"
 611 |     }
 612 |    ],
 613 |    "source": [
 614 |     "np.median(salaries)"
 615 |    ]
 616 |   },
 617 |   {
 618 |    "cell_type": "markdown",
 619 |    "metadata": {},
 620 |    "source": [
 621 |     "<b>Improved version</b>"
 622 |    ]
 623 |   },
 624 |   {
 625 |    "cell_type": "code",
 626 |    "execution_count": 24,
 627 |    "metadata": {
 628 |     "collapsed": false
 629 |    },
 630 |    "outputs": [
 631 |     {
 632 |      "data": {
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 753 |        "mxJlAk7FJ4IAAAAASUVORK5CYII=\n"
 754 |       ],
 755 |       "text/plain": [
 756 |        "<matplotlib.figure.Figure at 0x7fd7cbc11fd0>"
 757 |       ]
 758 |      },
 759 |      "metadata": {},
 760 |      "output_type": "display_data"
 761 |     }
 762 |    ],
 763 |    "source": [
 764 |     "salaries = [17, 23, 14, 16, 19, 22, 15, 18, 18, 93, 95]\n",
 765 |     "\n",
 766 |     "fig = plt.figure()\n",
 767 |     "ax = fig.add_subplot(111)\n",
 768 |     "ind = np.arange(len(salaries))\n",
 769 |     "width = 0.2\n",
 770 |     "#rects = ax.bar(salaries, ind, width)\n",
 771 |     "plt.hist(salaries, bins=xrange(min(salaries), max(salaries)).__len__())\n",
 772 |     "ax.set_xlabel('Salary')\n",
 773 |     "ax.set_ylabel('# of employees')\n",
 774 |     "ax.set_title('Bar chart of salaries')\n",
 775 |     "plt.show()"
 776 |    ]
 777 |   },
 778 |   {
 779 |    "cell_type": "code",
 780 |    "execution_count": 25,
 781 |    "metadata": {
 782 |     "collapsed": false
 783 |    },
 784 |    "outputs": [
 785 |     {
 786 |      "data": {
 787 |       "text/plain": [
 788 |        "31.82"
 789 |       ]
 790 |      },
 791 |      "execution_count": 25,
 792 |      "metadata": {},
 793 |      "output_type": "execute_result"
 794 |     }
 795 |    ],
 796 |    "source": [
 797 |     "np.mean(salaries)"
 798 |    ]
 799 |   },
 800 |   {
 801 |    "cell_type": "code",
 802 |    "execution_count": 26,
 803 |    "metadata": {
 804 |     "collapsed": false
 805 |    },
 806 |    "outputs": [
 807 |     {
 808 |      "data": {
 809 |       "text/plain": [
 810 |        "18.00"
 811 |       ]
 812 |      },
 813 |      "execution_count": 26,
 814 |      "metadata": {},
 815 |      "output_type": "execute_result"
 816 |     }
 817 |    ],
 818 |    "source": [
 819 |     "np.median(salaries)"
 820 |    ]
 821 |   },
 822 |   {
 823 |    "cell_type": "markdown",
 824 |    "metadata": {},
 825 |    "source": [
 826 |     "<b>Histogram of the data</b>"
 827 |    ]
 828 |   },
 829 |   {
 830 |    "cell_type": "code",
 831 |    "execution_count": 31,
 832 |    "metadata": {
 833 |     "collapsed": false
 834 |    },
 835 |    "outputs": [
 836 |     {
 837 |      "data": {
 838 |       "image/png": [
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 929 |        "/q3AG8ZV07B5CRwPvHFgvX8BDptr3+O+SNu9q+q6dvo64N7jKqTtgRwIfJMx15VkpyQXtm2fVVUX\n",
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 931 |        "pjnr74aqOmOcNQHrgCe3Qyq7Ac+m6fAslddutjruR/MB2Snzflh23MF/h2reqsZypDnJ7sApwHFV\n",
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 934 |        "/uuS3AcgyX2Bn466gCQ704T+J6rqi0ulLoCq2gj8M8247DhregLwvCSX0/QWj0jyiTHXBEBVXdP+\n",
 935 |        "3EAzbr16zHVdBVxVVee18yfTvBFcO+7niubN8fz2uYLxPk+HAF+vqp9X1W3A54HHM+bnqapOqqpD\n",
 936 |        "quqpwPXA91gCv+et2eq4GthvYL1922WzGnfwnwoc204fSzPGPjJJAnwEWF9V714KdSXZZ+pofZJd\n",
 937 |        "acY9LxhnTVX15qrar6r2pxkq+Leq+i/jrAkgyW5J9minV9KMX68dZ11VdS1wZZID2kVPBy6iGcMe\n",
 938 |        "23PVehF3DvPAeF+/S4DDkuza/h0+nebEgbE+T0nu1f68P/CfgE8x5t/zAbPVcSpwTJJdkuwPPJjm\n",
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 941 |        "Oy+Bmr7W1nQhzdl1Y3metjYvgTcD36d5Q33mfPv3A1yS1DPjHuqRJI2YwS9JPWPwS1LPGPyS1DMG\n",
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 943 |        "ZKuuxZJkWVXdPv+a0tJhj187uvsAP6uqzdBcFbKqrknytiTntl+68cGZNkzy9pnWSTKZ5MQk5wFv\n",
 944 |        "SfLDNN8xTZI92/llo3hw0kIY/NrRnQ7sl+TSJO9P8pR2+fuqanU1XwKya3sF0ilTVyB97yzrFLBz\n",
 945 |        "VR1aVe8AJmn+q4DmWkan+F+AljKDXzu0qrqJ5uqmrwQ2AJ9NcizNFUa/keS7wBE03150x2btz7nW\n",
 946 |        "+ezA9IdprqUC8DKab0+SlizH+LXDq6otwNnA2e13mP53movhHVxVVyc5AVgxuE2SFcD751jnpoH9\n",
 947 |        "fz3JqiQTwLKqWt/tI5K2jT1+7dCSHJDkwQOLDqS5gmEBP2+/hOd3Z9h0KuTnWmfQx4FPAidtY8lS\n",
 948 |        "5+zxa0e3O/De9jsObgMuA14F3EBzKedrab5u8y6q6oYkH5prnWk+Bfw5d73evbQkeVlmaREkeSHw\n",
 949 |        "3Ko6dt6VpTGzxy9toyTvBZ5J8+Xc0pJnj1+SesaDu5LUMwa/JPWMwS9JPWPwS1LPGPyS1DMGvyT1\n",
 950 |        "zP8H52pExupoZh0AAAAASUVORK5CYII=\n"
 951 |       ],
 952 |       "text/plain": [
 953 |        "<matplotlib.figure.Figure at 0x7fd7cba69490>"
 954 |       ]
 955 |      },
 956 |      "metadata": {},
 957 |      "output_type": "display_data"
 958 |     }
 959 |    ],
 960 |    "source": [
 961 |     "plt.hist(salaries, bins=len(salaries))\n",
 962 |     "plt.title('Histogram of salaries')\n",
 963 |     "plt.xlabel('Salary')\n",
 964 |     "plt.ylabel('Frequency')\n",
 965 |     "plt.show()"
 966 |    ]
 967 |   },
 968 |   {
 969 |    "cell_type": "markdown",
 970 |    "metadata": {},
 971 |    "source": [
 972 |     "<h1>Measures of Dispersion</h1>"
 973 |    ]
 974 |   },
 975 |   {
 976 |    "cell_type": "markdown",
 977 |    "metadata": {},
 978 |    "source": [
 979 |     "<h2>Quartiles</h2>"
 980 |    ]
 981 |   },
 982 |   {
 983 |    "cell_type": "code",
 984 |    "execution_count": 13,
 985 |    "metadata": {
 986 |     "collapsed": false
 987 |    },
 988 |    "outputs": [
 989 |     {
 990 |      "data": {
 991 |       "text/plain": [
 992 |        "[14, 45, 77, 71, 73, 43, 80, 53, 8, 46, 4, 94, 95, 33, 31, 77, 20, 18, 19, 35]"
 993 |       ]
 994 |      },
 995 |      "execution_count": 13,
 996 |      "metadata": {},
 997 |      "output_type": "execute_result"
 998 |     }
 999 |    ],
1000 |    "source": [
1001 |     "import random\n",
1002 |     "random.seed(100)\n",
1003 |     "testScores = [random.randint(0,100) for p in xrange(0,20)]\n",
1004 |     "testScores"
1005 |    ]
1006 |   },
1007 |   {
1008 |    "cell_type": "code",
1009 |    "execution_count": 14,
1010 |    "metadata": {
1011 |     "collapsed": false
1012 |    },
1013 |    "outputs": [],
1014 |    "source": [
1015 |     "sortedScores = np.sort(testScores)"
1016 |    ]
1017 |   },
1018 |   {
1019 |    "cell_type": "code",
1020 |    "execution_count": 15,
1021 |    "metadata": {
1022 |     "collapsed": false
1023 |    },
1024 |    "outputs": [
1025 |     {
1026 |      "data": {
1027 |       "text/plain": [
1028 |        "{1: 4,\n",
1029 |        " 2: 8,\n",
1030 |        " 3: 14,\n",
1031 |        " 4: 18,\n",
1032 |        " 5: 19,\n",
1033 |        " 6: 20,\n",
1034 |        " 7: 31,\n",
1035 |        " 8: 33,\n",
1036 |        " 9: 35,\n",
1037 |        " 10: 43,\n",
1038 |        " 11: 45,\n",
1039 |        " 12: 46,\n",
1040 |        " 13: 53,\n",
1041 |        " 14: 71,\n",
1042 |        " 15: 73,\n",
1043 |        " 16: 77,\n",
1044 |        " 17: 77,\n",
1045 |        " 18: 80,\n",
1046 |        " 19: 94,\n",
1047 |        " 20: 95}"
1048 |       ]
1049 |      },
1050 |      "execution_count": 15,
1051 |      "metadata": {},
1052 |      "output_type": "execute_result"
1053 |     }
1054 |    ],
1055 |    "source": [
1056 |     "rankedScores = {i+1: sortedScores[i] for i in range(len(sortedScores))}\n",
1057 |     "rankedScores"
1058 |    ]
1059 |   },
1060 |   {
1061 |    "cell_type": "code",
1062 |    "execution_count": 46,
1063 |    "metadata": {
1064 |     "collapsed": false,
1065 |     "scrolled": true
1066 |    },
1067 |    "outputs": [
1068 |     {
1069 |      "data": {
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1120 |        "L2/ndVTV6u1b+oEPweoZhsPFwBdn9tLHmM3/jczB60dtR69fmq35/RtovzSPPdpDctpMl97n0UoB\n",
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1122 |        "HuuRJA2pt3Coqv/HEFsmVXVKXzVIkmbGM6QlSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwHSVKH\n",
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1134 |        "qVX10+3tvKpet43nPwZ8bHv7lST1a5gzpK8Gnth3IZKkhWOYLYcnAtcn+QZb9znM1aGskqQFaJhw\n",
1135 |        "mPHebknSjmmY+zmMzUMdkqQFZJgzpB9g60lvu9IcbfRAVe3VZ2GSpNEZZsthz/HHSXYCVgFH9lmU\n",
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1137 |        "b5h9DifOQx2SpAVkutuETnV+QwFU1R/1UpEkaeSm23J4kO59Gx4PvBXYFzAcJGmRmu42oR8cf5xk\n",
1138 |        "L+CdwEnAucCH+i9NkjQq0+5zSPKzwKnAG4BPAc+tqnvmozBJ0uhMt8/hg8CvAX8FPGfwrm2SpMVt\n",
1139 |        "upPg/iNwAPB+4PYk9w/83Dc/5UmSRmG6fQ7bdfa0JGnxMAAkSR2GgySpw3CQJHUYDpKkDsNBktRh\n",
1140 |        "OEiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR19BoOST6ZZEOSq6dpc3qSG5JcmeTwPuuRJA2n7y2H\n",
1141 |        "s4BjpnoyybHAwVV1CHAy8PGe65EkDaHXcKiqS4Dpbg60Cji7bbse2DvJfn3WJEnatlHvczgAuGVg\n",
1142 |        "+lZg+YhqkSS1pr1N6DzJhOmatFGyemByrKrG+ipIkGTS34OGNxfrsKom/n3MG/8P7HiSrARWzkVf\n",
1143 |        "ow6H24AVA9PL23kdVbV6PgrSoNl8NozsM22B2dHX4Wzr39Hf/46l/dI8Nj6d5LSZ9jXqYaW1wJsA\n",
1144 |        "khwJbKyqDaMtSZLU65ZDknOAo4B9k9wCnAbsAlBVa6pqXZJjk9wIPAic1Gc9kqTh9BoOVfW6Idqc\n",
1145 |        "0mcNkqTtN+phJUnSAmQ4SJI6DAdJUofhIEnqMBwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwH\n",
1146 |        "SVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSh+EgSeowHCRJHYaDJKnDcJAk\n",
1147 |        "dRgOkqQOw0GS1GE4SJI6DAdJUofhIEnq6DUckhyT5PokNyT5w0meX5nk3iSXtz/v77MeSdJwlvXV\n",
1148 |        "cZKdgY8CLwNuA76RZG1VXTeh6cVVtaqvOiRJ26/PLYcjgBur6qaqehg4Fzh+knbpsQZJ0gz0GQ4H\n",
1149 |        "ALcMTN/azhtUwAuTXJlkXZLDeqxHkjSk3oaVaD74t+UyYEVVbUrySuB84NAea5IkDaHPcLgNWDEw\n",
1150 |        "vYJm6+FRVXX/wOMLkpyZZJ+quntiZ0lWD0yOVdXY3JYrSTu2JCuBlXPRV5/hcClwSJKDgNuB1wKv\n",
1151 |        "G2yQZD/gzqqqJEcAmSwYAKpqdY+1StIOr/3SPDY+neS0mfbVWzhU1eYkpwBfAnYGPlFV1yV5W/v8\n",
1152 |        "GuDVwNuTbAY2ASf0VY8kaXh9bjlQVRcAF0yYt2bg8ceAj/VZgyRp+3mGtCSpw3CQJHUYDpKkDsNB\n",
1153 |        "ktRhOEiSOgwHSVKH4SBJ6jAcJEkdhoMkqcNwkCR1GA6SpA7DQZLUYThIkjoMB0lSh+EgSeowHCRJ\n",
1154 |        "HYaDJKnDcJAkdRgOkqQOw0GS1GE4SJI6DAdJUofhIEnqMBwkSR2GgySpw3CQJHUYDpKkDsNBktTR\n",
1155 |        "azgkOSbJ9UluSPKHU7Q5vX3+yiSH91mPJGk4vYVDkp2BjwLHAIcBr0vyzAltjgUOrqpDgJOBj/dV\n",
1156 |        "z+IxNuoCFowkK0ddw0Lhuhg0NuoCFoU+txyOAG6sqpuq6mHgXOD4CW1WAWcDVNV6YO8k+/VY0yIw\n",
1157 |        "NuoCFpKVoy5gAVk56gIWjrFRF7Ao9BkOBwC3DEzf2s7bVpvlPdYkSRrCsh77riHbZYav24ardoOj\n",
1158 |        "753Za+9ZBpsfmZs6JGnHk6o5+iye2HFyJLC6qo5pp98DbKmqPxto85fAWFWd205fDxxVVRsm9NVP\n",
1159 |        "kZK0yFXVxC/gQ+lzy+FS4JAkBwG3A68FXjehzVrgFODcNkw2TgwGmPmbkyTNTG/hUFWbk5wCfAnY\n",
1160 |        "GfhEVV2X5G3t82uqal2SY5PcCDwInNRXPZKk4fU2rCRJ2nEt6DOkhzmJbrFKsiLJRUmuSfKtJO9s\n",
1161 |        "5++T5MIk30ny5SR7j7rW+ZJk5ySXJ/lCO70k10WSvZOcl+S6JNcmef4SXhfvaf9Grk7yP5I8bqms\n",
1162 |        "iySfTLIhydUD86Z87+26uqH9TH3FtvpfsOEwzEl0i9zDwKlV9SzgSOAd7ft/N3BhVR0KfLWdXire\n",
1163 |        "BVzL1iPaluq6+O/Auqp6JvAc4HqW4Lpo92f+FvDcqno2zfD1CSyddXEWzefjoEnfe5LDaPb7Hta+\n",
1164 |        "5swk037+L9hwYLiT6Batqrqjqq5oHz8AXEdzXsijJw62//6b0VQ4v5IsB44F/oathz8vuXWR5GeA\n",
1165 |        "X66qT0Kzb6+q7mUJrgvgPpovUXskWQbsQXPwy5JYF1V1CXDPhNlTvffjgXOq6uGqugm4keYzdkoL\n",
1166 |        "ORyGOYluSWi/IR0OrAf2GziiawOwVM4o/zDw+8CWgXlLcV08FbgryVlJLkvy10kezxJcF1V1N/Ah\n",
1167 |        "4Ps0obCxqi5kCa6LAVO99yfTfIaO2+bn6UIOB/eUA0n2BD4HvKuq7h98rpqjCRb9ekryr4E7q+py\n",
1168 |        "uidNAktnXdAcYfhc4Myqei7NUX6PGTZZKusiydOB/wAcRPPht2eSNw62WSrrYjJDvPdp18tCDofb\n",
1169 |        "gBUD0yt4bPItekl2oQmGT1fV+e3sDUme1D6/P3DnqOqbRy8EViX5Z+Ac4Ogkn2ZprotbgVur6hvt\n",
1170 |        "9Hk0YXHHElwXzwO+VlU/qqrNwP8GXsDSXBfjpvqbmPh5urydN6WFHA6PnkSXZFeanSlrR1zTvEkS\n",
1171 |        "4BPAtVX1kYGn1gJvbh+/GTh/4msXm6p6b1WtqKqn0uxw/Luq+k2W5rq4A7glyaHtrJcB1wBfYImt\n",
1172 |        "C5od8Ucm2b39e3kZzQELS3FdjJvqb2ItcEKSXZM8FTgE+Pp0HS3o8xySvBL4CFtPovvTEZc0b5K8\n",
1173 |        "GPh74Cq2bv69h+YX+lngKcBNwG9U1cZR1DgKSY4CfreqViXZhyW4LpL8As2O+V2B79KcPLozS3Nd\n",
1174 |        "/AHNh+AW4DLg3wFPYAmsiyTnAEcB+9LsX/gA8HmmeO9J3gu8BdhMM0z9pWn7X8jhIEkajYU8rCRJ\n",
1175 |        "GhHDQZLUYThIkjoMB0lSh+EgSeowHCRJHYaDNCDJ+9pLpF/ZXh582ouTSYtVn7cJlXYoSV4AHAcc\n",
1176 |        "XlUPtyfZPW4W/S1rL+sg7XDccpC2ehLww/YS8VTV3VX1gyS/lOQfklyRZH2SxyfZrb0y6lXt1VFX\n",
1177 |        "AiQ5McnaJF8FLkyyR3tTlvVtu1UjfH/S0NxykLb6MvCBJN8GvgL8T+CfaO4l8htV9c32KrkP0VwN\n",
1178 |        "9JGqek6SZwBfHrje0eHAs6tqY5I/Ab5aVW9p78q1PslXqmrTfL85aXu45SC1qupB4BeBk4G7aMLh\n",
1179 |        "ZOAHVfXNts0DVfUI8CLgM+28bwM3A4fSXAfrwoFr+bwCeHeSy4GLaIapBq+OKS1IbjlIA6pqC3Ax\n",
1180 |        "cHF7b953TNN80ntL0NxjYdC/raob5qI+ab645SC1khya5JCBWYfT3J71SUme17Z5Qnt/80uAN4y/\n",
1181 |        "juYqmNfTDYwvAe8cWMbh/b0Dae645SBttSdwRrtvYDNwA82w0lnt/N2BTTT3DTgT+HiSq9q2b26P\n",
1182 |        "cJp4963/AnykbbcT8D2a+/xKC5qX7JYkdTisJEnqMBwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiS\n",
1183 |        "OgwHSVLH/wemLGnX4NSjaAAAAABJRU5ErkJggg==\n"
1184 |       ],
1185 |       "text/plain": [
1186 |        "<matplotlib.figure.Figure at 0x7fd7d42ef9d0>"
1187 |       ]
1188 |      },
1189 |      "metadata": {},
1190 |      "output_type": "display_data"
1191 |     }
1192 |    ],
1193 |    "source": [
1194 |     "fig = plt.figure()\n",
1195 |     "ax = fig.add_subplot(111)\n",
1196 |     "ind = np.arange(len(rankedScores))\n",
1197 |     "width = 0.2\n",
1198 |     "#rects = ax.bar(salaries, ind, width)\n",
1199 |     "plt.hist(rankedScores.values(), bins=xrange(min(rankedScores), max(rankedScores)).__len__())\n",
1200 |     "ax.set_xlabel('Score')\n",
1201 |     "ax.set_ylabel('Number')\n",
1202 |     "ax.set_title('Histogram of scores')\n",
1203 |     "plt.show()"
1204 |    ]
1205 |   },
1206 |   {
1207 |    "cell_type": "code",
1208 |    "execution_count": 45,
1209 |    "metadata": {
1210 |     "collapsed": false
1211 |    },
1212 |    "outputs": [
1213 |     {
1214 |      "data": {
1215 |       "text/plain": [
1216 |        "xrange(1, 20)"
1217 |       ]
1218 |      },
1219 |      "execution_count": 45,
1220 |      "metadata": {},
1221 |      "output_type": "execute_result"
1222 |     }
1223 |    ],
1224 |    "source": [
1225 |     "xrange(min(rankedScores), max(rankedScores))"
1226 |    ]
1227 |   },
1228 |   {
1229 |    "cell_type": "code",
1230 |    "execution_count": 47,
1231 |    "metadata": {
1232 |     "collapsed": true
1233 |    },
1234 |    "outputs": [],
1235 |    "source": [
1236 |     "from scipy.stats.mstats import mquantiles"
1237 |    ]
1238 |   },
1239 |   {
1240 |    "cell_type": "code",
1241 |    "execution_count": 48,
1242 |    "metadata": {
1243 |     "collapsed": false
1244 |    },
1245 |    "outputs": [
1246 |     {
1247 |      "data": {
1248 |       "text/plain": [
1249 |        "array([ 19.45,  44.  ,  75.2 ])"
1250 |       ]
1251 |      },
1252 |      "execution_count": 48,
1253 |      "metadata": {},
1254 |      "output_type": "execute_result"
1255 |     }
1256 |    ],
1257 |    "source": [
1258 |     "mquantiles(sortedScores)"
1259 |    ]
1260 |   },
1261 |   {
1262 |    "cell_type": "code",
1263 |    "execution_count": 49,
1264 |    "metadata": {
1265 |     "collapsed": false
1266 |    },
1267 |    "outputs": [
1268 |     {
1269 |      "data": {
1270 |       "text/plain": [
1271 |        "[19.75, 44.00, 74.00]"
1272 |       ]
1273 |      },
1274 |      "execution_count": 49,
1275 |      "metadata": {},
1276 |      "output_type": "execute_result"
1277 |     }
1278 |    ],
1279 |    "source": [
1280 |     "[np.percentile(sortedScores, perc) for perc in [25,50,75]]"
1281 |    ]
1282 |   },
1283 |   {
1284 |    "cell_type": "code",
1285 |    "execution_count": null,
1286 |    "metadata": {
1287 |     "collapsed": true
1288 |    },
1289 |    "outputs": [],
1290 |    "source": []
1291 |   }
1292 |  ],
1293 |  "metadata": {
1294 |   "kernelspec": {
1295 |    "display_name": "Python 2",
1296 |    "language": "python",
1297 |    "name": "python2"
1298 |   },
1299 |   "language_info": {
1300 |    "codemirror_mode": {
1301 |     "name": "ipython",
1302 |     "version": 2
1303 |    },
1304 |    "file_extension": ".py",
1305 |    "mimetype": "text/x-python",
1306 |    "name": "python",
1307 |    "nbconvert_exporter": "python",
1308 |    "pygments_lexer": "ipython2",
1309 |    "version": "2.7.9"
1310 |   }
1311 |  },
1312 |  "nbformat": 4,
1313 |  "nbformat_minor": 0
1314 | }
1315 | 


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/README.md:
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1 | # mastering_pandas
2 | This readme is for Mastering Pandas
3 | 


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