├── README.md
└── MovieLensDataAnalysis.ipynb


/README.md:
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1 | # py-analysis-visualisation
2 | Data Analytics and Visualisation using Python
3 | 
4 | You can get the movie-lens dataset used in this project at https://grouplens.org/datasets/movielens/100k/
5 | 


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/MovieLensDataAnalysis.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {
   7 |     "collapsed": false
   8 |    },
   9 |    "outputs": [
  10 |     {
  11 |      "data": {
  12 |       "text/html": [
  13 |        "<div>\n",
  14 |        "<table border=\"1\" class=\"dataframe\">\n",
  15 |        "  <thead>\n",
  16 |        "    <tr style=\"text-align: right;\">\n",
  17 |        "      <th></th>\n",
  18 |        "      <th>movieId</th>\n",
  19 |        "      <th>title</th>\n",
  20 |        "      <th>genres</th>\n",
  21 |        "    </tr>\n",
  22 |        "  </thead>\n",
  23 |        "  <tbody>\n",
  24 |        "    <tr>\n",
  25 |        "      <th>0</th>\n",
  26 |        "      <td>1</td>\n",
  27 |        "      <td>Toy Story (1995)</td>\n",
  28 |        "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
  29 |        "    </tr>\n",
  30 |        "    <tr>\n",
  31 |        "      <th>1</th>\n",
  32 |        "      <td>2</td>\n",
  33 |        "      <td>Jumanji (1995)</td>\n",
  34 |        "      <td>Adventure|Children|Fantasy</td>\n",
  35 |        "    </tr>\n",
  36 |        "    <tr>\n",
  37 |        "      <th>2</th>\n",
  38 |        "      <td>3</td>\n",
  39 |        "      <td>Grumpier Old Men (1995)</td>\n",
  40 |        "      <td>Comedy|Romance</td>\n",
  41 |        "    </tr>\n",
  42 |        "    <tr>\n",
  43 |        "      <th>3</th>\n",
  44 |        "      <td>4</td>\n",
  45 |        "      <td>Waiting to Exhale (1995)</td>\n",
  46 |        "      <td>Comedy|Drama|Romance</td>\n",
  47 |        "    </tr>\n",
  48 |        "    <tr>\n",
  49 |        "      <th>4</th>\n",
  50 |        "      <td>5</td>\n",
  51 |        "      <td>Father of the Bride Part II (1995)</td>\n",
  52 |        "      <td>Comedy</td>\n",
  53 |        "    </tr>\n",
  54 |        "  </tbody>\n",
  55 |        "</table>\n",
  56 |        "</div>"
  57 |       ],
  58 |       "text/plain": [
  59 |        "   movieId                               title  \\\n",
  60 |        "0        1                    Toy Story (1995)   \n",
  61 |        "1        2                      Jumanji (1995)   \n",
  62 |        "2        3             Grumpier Old Men (1995)   \n",
  63 |        "3        4            Waiting to Exhale (1995)   \n",
  64 |        "4        5  Father of the Bride Part II (1995)   \n",
  65 |        "\n",
  66 |        "                                        genres  \n",
  67 |        "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
  68 |        "1                   Adventure|Children|Fantasy  \n",
  69 |        "2                               Comedy|Romance  \n",
  70 |        "3                         Comedy|Drama|Romance  \n",
  71 |        "4                                       Comedy  "
  72 |       ]
  73 |      },
  74 |      "execution_count": 1,
  75 |      "metadata": {},
  76 |      "output_type": "execute_result"
  77 |     }
  78 |    ],
  79 |    "source": [
  80 |     "# relative paths to the movie lens data set for movies file and ratings file\n",
  81 |     "movies_path = './ml-latest-small/movies.csv'\n",
  82 |     "\n",
  83 |     "# import DataFrame from pandas package so that we can work with this tabular data\n",
  84 |     "import pandas as pd\n",
  85 |     "from pandas import DataFrame as df\n",
  86 |     "\n",
  87 |     "movies_df = pd.read_csv(movies_path)\n",
  88 |     "\n",
  89 |     "# now that we have a DataFrame lets peek into the data and see how it looks\n",
  90 |     "movies_df.head()"
  91 |    ]
  92 |   },
  93 |   {
  94 |    "cell_type": "code",
  95 |    "execution_count": 2,
  96 |    "metadata": {
  97 |     "collapsed": false
  98 |    },
  99 |    "outputs": [
 100 |     {
 101 |      "data": {
 102 |       "text/plain": [
 103 |        "9125"
 104 |       ]
 105 |      },
 106 |      "execution_count": 2,
 107 |      "metadata": {},
 108 |      "output_type": "execute_result"
 109 |     }
 110 |    ],
 111 |    "source": [
 112 |     "# now lets see how many movies are there in this data\n",
 113 |     "len(movies_df.index)"
 114 |    ]
 115 |   },
 116 |   {
 117 |    "cell_type": "code",
 118 |    "execution_count": 3,
 119 |    "metadata": {
 120 |     "collapsed": false
 121 |    },
 122 |    "outputs": [
 123 |     {
 124 |      "data": {
 125 |       "text/plain": [
 126 |        "(9125, 3)"
 127 |       ]
 128 |      },
 129 |      "execution_count": 3,
 130 |      "metadata": {},
 131 |      "output_type": "execute_result"
 132 |     }
 133 |    ],
 134 |    "source": [
 135 |     "# we can also get high level information of the width and height of the data frame using shape property\n",
 136 |     "movies_df.shape"
 137 |    ]
 138 |   },
 139 |   {
 140 |    "cell_type": "code",
 141 |    "execution_count": 4,
 142 |    "metadata": {
 143 |     "collapsed": false
 144 |    },
 145 |    "outputs": [
 146 |     {
 147 |      "name": "stdout",
 148 |      "output_type": "stream",
 149 |      "text": [
 150 |       "1\n",
 151 |       "Toy Story (1995)\n",
 152 |       "Adventure|Animation|Children|Comedy|Fantasy\n"
 153 |      ]
 154 |     }
 155 |    ],
 156 |    "source": [
 157 |     "# now we can access an element at a position using loc[x_pos][y_pos] syntax \n",
 158 |     "print movies_df.loc[0][0]\n",
 159 |     "print movies_df.loc[0][1]\n",
 160 |     "print movies_df.loc[0][2]"
 161 |    ]
 162 |   },
 163 |   {
 164 |    "cell_type": "code",
 165 |    "execution_count": 5,
 166 |    "metadata": {
 167 |     "collapsed": false
 168 |    },
 169 |    "outputs": [
 170 |     {
 171 |      "data": {
 172 |       "text/html": [
 173 |        "<div>\n",
 174 |        "<table border=\"1\" class=\"dataframe\">\n",
 175 |        "  <thead>\n",
 176 |        "    <tr style=\"text-align: right;\">\n",
 177 |        "      <th></th>\n",
 178 |        "      <th>movieId</th>\n",
 179 |        "      <th>title</th>\n",
 180 |        "      <th>genres</th>\n",
 181 |        "    </tr>\n",
 182 |        "  </thead>\n",
 183 |        "  <tbody>\n",
 184 |        "    <tr>\n",
 185 |        "      <th>0</th>\n",
 186 |        "      <td>1</td>\n",
 187 |        "      <td>Toy Story (1995)</td>\n",
 188 |        "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
 189 |        "    </tr>\n",
 190 |        "    <tr>\n",
 191 |        "      <th>1</th>\n",
 192 |        "      <td>2</td>\n",
 193 |        "      <td>Jumanji (1995)</td>\n",
 194 |        "      <td>Adventure|Children|Fantasy</td>\n",
 195 |        "    </tr>\n",
 196 |        "    <tr>\n",
 197 |        "      <th>2</th>\n",
 198 |        "      <td>3</td>\n",
 199 |        "      <td>Grumpier Old Men (1995)</td>\n",
 200 |        "      <td>Comedy|Romance</td>\n",
 201 |        "    </tr>\n",
 202 |        "    <tr>\n",
 203 |        "      <th>3</th>\n",
 204 |        "      <td>4</td>\n",
 205 |        "      <td>Waiting to Exhale (1995)</td>\n",
 206 |        "      <td>Comedy|Drama|Romance</td>\n",
 207 |        "    </tr>\n",
 208 |        "    <tr>\n",
 209 |        "      <th>4</th>\n",
 210 |        "      <td>5</td>\n",
 211 |        "      <td>Father of the Bride Part II (1995)</td>\n",
 212 |        "      <td>Comedy</td>\n",
 213 |        "    </tr>\n",
 214 |        "  </tbody>\n",
 215 |        "</table>\n",
 216 |        "</div>"
 217 |       ],
 218 |       "text/plain": [
 219 |        "   movieId                               title  \\\n",
 220 |        "0        1                    Toy Story (1995)   \n",
 221 |        "1        2                      Jumanji (1995)   \n",
 222 |        "2        3             Grumpier Old Men (1995)   \n",
 223 |        "3        4            Waiting to Exhale (1995)   \n",
 224 |        "4        5  Father of the Bride Part II (1995)   \n",
 225 |        "\n",
 226 |        "                                        genres  \n",
 227 |        "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
 228 |        "1                   Adventure|Children|Fantasy  \n",
 229 |        "2                               Comedy|Romance  \n",
 230 |        "3                         Comedy|Drama|Romance  \n",
 231 |        "4                                       Comedy  "
 232 |       ]
 233 |      },
 234 |      "execution_count": 5,
 235 |      "metadata": {},
 236 |      "output_type": "execute_result"
 237 |     }
 238 |    ],
 239 |    "source": [
 240 |     "# we can explicitly set the index column if our data has a unique identifier for each row using set_index\n",
 241 |     "movies_df.set_index('movieId')\n",
 242 |     "movies_df.head()"
 243 |    ]
 244 |   },
 245 |   {
 246 |    "cell_type": "code",
 247 |    "execution_count": 6,
 248 |    "metadata": {
 249 |     "collapsed": false
 250 |    },
 251 |    "outputs": [
 252 |     {
 253 |      "data": {
 254 |       "text/html": [
 255 |        "<div>\n",
 256 |        "<table border=\"1\" class=\"dataframe\">\n",
 257 |        "  <thead>\n",
 258 |        "    <tr style=\"text-align: right;\">\n",
 259 |        "      <th></th>\n",
 260 |        "      <th>movieId</th>\n",
 261 |        "      <th>title</th>\n",
 262 |        "      <th>genres</th>\n",
 263 |        "      <th>genres_arr</th>\n",
 264 |        "    </tr>\n",
 265 |        "  </thead>\n",
 266 |        "  <tbody>\n",
 267 |        "    <tr>\n",
 268 |        "      <th>0</th>\n",
 269 |        "      <td>1</td>\n",
 270 |        "      <td>Toy Story (1995)</td>\n",
 271 |        "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
 272 |        "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
 273 |        "    </tr>\n",
 274 |        "    <tr>\n",
 275 |        "      <th>1</th>\n",
 276 |        "      <td>2</td>\n",
 277 |        "      <td>Jumanji (1995)</td>\n",
 278 |        "      <td>Adventure|Children|Fantasy</td>\n",
 279 |        "      <td>[Adventure, Children, Fantasy]</td>\n",
 280 |        "    </tr>\n",
 281 |        "    <tr>\n",
 282 |        "      <th>2</th>\n",
 283 |        "      <td>3</td>\n",
 284 |        "      <td>Grumpier Old Men (1995)</td>\n",
 285 |        "      <td>Comedy|Romance</td>\n",
 286 |        "      <td>[Comedy, Romance]</td>\n",
 287 |        "    </tr>\n",
 288 |        "    <tr>\n",
 289 |        "      <th>3</th>\n",
 290 |        "      <td>4</td>\n",
 291 |        "      <td>Waiting to Exhale (1995)</td>\n",
 292 |        "      <td>Comedy|Drama|Romance</td>\n",
 293 |        "      <td>[Comedy, Drama, Romance]</td>\n",
 294 |        "    </tr>\n",
 295 |        "    <tr>\n",
 296 |        "      <th>4</th>\n",
 297 |        "      <td>5</td>\n",
 298 |        "      <td>Father of the Bride Part II (1995)</td>\n",
 299 |        "      <td>Comedy</td>\n",
 300 |        "      <td>[Comedy]</td>\n",
 301 |        "    </tr>\n",
 302 |        "  </tbody>\n",
 303 |        "</table>\n",
 304 |        "</div>"
 305 |       ],
 306 |       "text/plain": [
 307 |        "   movieId                               title  \\\n",
 308 |        "0        1                    Toy Story (1995)   \n",
 309 |        "1        2                      Jumanji (1995)   \n",
 310 |        "2        3             Grumpier Old Men (1995)   \n",
 311 |        "3        4            Waiting to Exhale (1995)   \n",
 312 |        "4        5  Father of the Bride Part II (1995)   \n",
 313 |        "\n",
 314 |        "                                        genres  \\\n",
 315 |        "0  Adventure|Animation|Children|Comedy|Fantasy   \n",
 316 |        "1                   Adventure|Children|Fantasy   \n",
 317 |        "2                               Comedy|Romance   \n",
 318 |        "3                         Comedy|Drama|Romance   \n",
 319 |        "4                                       Comedy   \n",
 320 |        "\n",
 321 |        "                                          genres_arr  \n",
 322 |        "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
 323 |        "1                     [Adventure, Children, Fantasy]  \n",
 324 |        "2                                  [Comedy, Romance]  \n",
 325 |        "3                           [Comedy, Drama, Romance]  \n",
 326 |        "4                                           [Comedy]  "
 327 |       ]
 328 |      },
 329 |      "execution_count": 6,
 330 |      "metadata": {},
 331 |      "output_type": "execute_result"
 332 |     }
 333 |    ],
 334 |    "source": [
 335 |     "# now lets find out how many animation vs comedy movies exist in this dataset\n",
 336 |     "\n",
 337 |     "movies_df['genres_arr'] = movies_df['genres'].str.split('|')\n",
 338 |     "movies_df.head()"
 339 |    ]
 340 |   },
 341 |   {
 342 |    "cell_type": "code",
 343 |    "execution_count": 7,
 344 |    "metadata": {
 345 |     "collapsed": false
 346 |    },
 347 |    "outputs": [
 348 |     {
 349 |      "name": "stdout",
 350 |      "output_type": "stream",
 351 |      "text": [
 352 |       "# of animation movies 447\n",
 353 |       "# of romance movies 3315\n"
 354 |      ]
 355 |     }
 356 |    ],
 357 |    "source": [
 358 |     "\n",
 359 |     "animation_df = movies_df[movies_df.genres_arr.map(lambda x: 'Animation' in x)]\n",
 360 |     "print '# of animation movies', len(animation_df.index)\n",
 361 |     "\n",
 362 |     "romance_df = movies_df[movies_df.genres_arr.map(lambda x: 'Comedy' in x)]\n",
 363 |     "print '# of romance movies', len(romance_df.index)"
 364 |    ]
 365 |   },
 366 |   {
 367 |    "cell_type": "code",
 368 |    "execution_count": 8,
 369 |    "metadata": {
 370 |     "collapsed": false
 371 |    },
 372 |    "outputs": [
 373 |     {
 374 |      "name": "stdout",
 375 |      "output_type": "stream",
 376 |      "text": [
 377 |       "# of masala movies 6\n"
 378 |      ]
 379 |     },
 380 |     {
 381 |      "data": {
 382 |       "text/html": [
 383 |        "<div>\n",
 384 |        "<table border=\"1\" class=\"dataframe\">\n",
 385 |        "  <thead>\n",
 386 |        "    <tr style=\"text-align: right;\">\n",
 387 |        "      <th></th>\n",
 388 |        "      <th>movieId</th>\n",
 389 |        "      <th>title</th>\n",
 390 |        "      <th>genres</th>\n",
 391 |        "      <th>genres_arr</th>\n",
 392 |        "    </tr>\n",
 393 |        "  </thead>\n",
 394 |        "  <tbody>\n",
 395 |        "    <tr>\n",
 396 |        "      <th>344</th>\n",
 397 |        "      <td>380</td>\n",
 398 |        "      <td>True Lies (1994)</td>\n",
 399 |        "      <td>Action|Adventure|Comedy|Romance|Thriller</td>\n",
 400 |        "      <td>[Action, Adventure, Comedy, Romance, Thriller]</td>\n",
 401 |        "    </tr>\n",
 402 |        "    <tr>\n",
 403 |        "      <th>1974</th>\n",
 404 |        "      <td>2468</td>\n",
 405 |        "      <td>Jumpin' Jack Flash (1986)</td>\n",
 406 |        "      <td>Action|Comedy|Romance|Thriller</td>\n",
 407 |        "      <td>[Action, Comedy, Romance, Thriller]</td>\n",
 408 |        "    </tr>\n",
 409 |        "    <tr>\n",
 410 |        "      <th>3699</th>\n",
 411 |        "      <td>4719</td>\n",
 412 |        "      <td>Osmosis Jones (2001)</td>\n",
 413 |        "      <td>Action|Animation|Comedy|Crime|Drama|Romance|Th...</td>\n",
 414 |        "      <td>[Action, Animation, Comedy, Crime, Drama, Roma...</td>\n",
 415 |        "    </tr>\n",
 416 |        "    <tr>\n",
 417 |        "      <th>3842</th>\n",
 418 |        "      <td>4956</td>\n",
 419 |        "      <td>Stunt Man, The (1980)</td>\n",
 420 |        "      <td>Action|Adventure|Comedy|Drama|Romance|Thriller</td>\n",
 421 |        "      <td>[Action, Adventure, Comedy, Drama, Romance, Th...</td>\n",
 422 |        "    </tr>\n",
 423 |        "    <tr>\n",
 424 |        "      <th>4702</th>\n",
 425 |        "      <td>6564</td>\n",
 426 |        "      <td>Lara Croft Tomb Raider: The Cradle of Life (2003)</td>\n",
 427 |        "      <td>Action|Adventure|Comedy|Romance|Thriller</td>\n",
 428 |        "      <td>[Action, Adventure, Comedy, Romance, Thriller]</td>\n",
 429 |        "    </tr>\n",
 430 |        "    <tr>\n",
 431 |        "      <th>6193</th>\n",
 432 |        "      <td>34435</td>\n",
 433 |        "      <td>Sholay (1975)</td>\n",
 434 |        "      <td>Action|Adventure|Comedy|Musical|Romance|Thriller</td>\n",
 435 |        "      <td>[Action, Adventure, Comedy, Musical, Romance, ...</td>\n",
 436 |        "    </tr>\n",
 437 |        "  </tbody>\n",
 438 |        "</table>\n",
 439 |        "</div>"
 440 |       ],
 441 |       "text/plain": [
 442 |        "      movieId                                              title  \\\n",
 443 |        "344       380                                   True Lies (1994)   \n",
 444 |        "1974     2468                          Jumpin' Jack Flash (1986)   \n",
 445 |        "3699     4719                               Osmosis Jones (2001)   \n",
 446 |        "3842     4956                              Stunt Man, The (1980)   \n",
 447 |        "4702     6564  Lara Croft Tomb Raider: The Cradle of Life (2003)   \n",
 448 |        "6193    34435                                      Sholay (1975)   \n",
 449 |        "\n",
 450 |        "                                                 genres  \\\n",
 451 |        "344            Action|Adventure|Comedy|Romance|Thriller   \n",
 452 |        "1974                     Action|Comedy|Romance|Thriller   \n",
 453 |        "3699  Action|Animation|Comedy|Crime|Drama|Romance|Th...   \n",
 454 |        "3842     Action|Adventure|Comedy|Drama|Romance|Thriller   \n",
 455 |        "4702           Action|Adventure|Comedy|Romance|Thriller   \n",
 456 |        "6193   Action|Adventure|Comedy|Musical|Romance|Thriller   \n",
 457 |        "\n",
 458 |        "                                             genres_arr  \n",
 459 |        "344      [Action, Adventure, Comedy, Romance, Thriller]  \n",
 460 |        "1974                [Action, Comedy, Romance, Thriller]  \n",
 461 |        "3699  [Action, Animation, Comedy, Crime, Drama, Roma...  \n",
 462 |        "3842  [Action, Adventure, Comedy, Drama, Romance, Th...  \n",
 463 |        "4702     [Action, Adventure, Comedy, Romance, Thriller]  \n",
 464 |        "6193  [Action, Adventure, Comedy, Musical, Romance, ...  "
 465 |       ]
 466 |      },
 467 |      "execution_count": 8,
 468 |      "metadata": {},
 469 |      "output_type": "execute_result"
 470 |     }
 471 |    ],
 472 |    "source": [
 473 |     "# now lets find the real masala movies which has Action and Romance and Comedy and Thriller :)\n",
 474 |     "masala_lambda = lambda x: set(['Romance','Comedy', 'Action', 'Thriller']).issubset(x)\n",
 475 |     "masala_movies = movies_df[movies_df.genres_arr.map(masala_lambda)]\n",
 476 |     "print '# of masala movies', len(masala_movies.index)\n",
 477 |     "masala_movies"
 478 |    ]
 479 |   },
 480 |   {
 481 |    "cell_type": "code",
 482 |    "execution_count": 9,
 483 |    "metadata": {
 484 |     "collapsed": false
 485 |    },
 486 |    "outputs": [
 487 |     {
 488 |      "data": {
 489 |       "text/html": [
 490 |        "<div>\n",
 491 |        "<table border=\"1\" class=\"dataframe\">\n",
 492 |        "  <thead>\n",
 493 |        "    <tr style=\"text-align: right;\">\n",
 494 |        "      <th></th>\n",
 495 |        "      <th>movieId</th>\n",
 496 |        "      <th>title</th>\n",
 497 |        "      <th>genres</th>\n",
 498 |        "      <th>genres_arr</th>\n",
 499 |        "      <th>genre_count</th>\n",
 500 |        "    </tr>\n",
 501 |        "  </thead>\n",
 502 |        "  <tbody>\n",
 503 |        "    <tr>\n",
 504 |        "      <th>0</th>\n",
 505 |        "      <td>1</td>\n",
 506 |        "      <td>Toy Story (1995)</td>\n",
 507 |        "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
 508 |        "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
 509 |        "      <td>5</td>\n",
 510 |        "    </tr>\n",
 511 |        "    <tr>\n",
 512 |        "      <th>1</th>\n",
 513 |        "      <td>2</td>\n",
 514 |        "      <td>Jumanji (1995)</td>\n",
 515 |        "      <td>Adventure|Children|Fantasy</td>\n",
 516 |        "      <td>[Adventure, Children, Fantasy]</td>\n",
 517 |        "      <td>3</td>\n",
 518 |        "    </tr>\n",
 519 |        "    <tr>\n",
 520 |        "      <th>2</th>\n",
 521 |        "      <td>3</td>\n",
 522 |        "      <td>Grumpier Old Men (1995)</td>\n",
 523 |        "      <td>Comedy|Romance</td>\n",
 524 |        "      <td>[Comedy, Romance]</td>\n",
 525 |        "      <td>2</td>\n",
 526 |        "    </tr>\n",
 527 |        "    <tr>\n",
 528 |        "      <th>3</th>\n",
 529 |        "      <td>4</td>\n",
 530 |        "      <td>Waiting to Exhale (1995)</td>\n",
 531 |        "      <td>Comedy|Drama|Romance</td>\n",
 532 |        "      <td>[Comedy, Drama, Romance]</td>\n",
 533 |        "      <td>3</td>\n",
 534 |        "    </tr>\n",
 535 |        "    <tr>\n",
 536 |        "      <th>4</th>\n",
 537 |        "      <td>5</td>\n",
 538 |        "      <td>Father of the Bride Part II (1995)</td>\n",
 539 |        "      <td>Comedy</td>\n",
 540 |        "      <td>[Comedy]</td>\n",
 541 |        "      <td>1</td>\n",
 542 |        "    </tr>\n",
 543 |        "  </tbody>\n",
 544 |        "</table>\n",
 545 |        "</div>"
 546 |       ],
 547 |       "text/plain": [
 548 |        "   movieId                               title  \\\n",
 549 |        "0        1                    Toy Story (1995)   \n",
 550 |        "1        2                      Jumanji (1995)   \n",
 551 |        "2        3             Grumpier Old Men (1995)   \n",
 552 |        "3        4            Waiting to Exhale (1995)   \n",
 553 |        "4        5  Father of the Bride Part II (1995)   \n",
 554 |        "\n",
 555 |        "                                        genres  \\\n",
 556 |        "0  Adventure|Animation|Children|Comedy|Fantasy   \n",
 557 |        "1                   Adventure|Children|Fantasy   \n",
 558 |        "2                               Comedy|Romance   \n",
 559 |        "3                         Comedy|Drama|Romance   \n",
 560 |        "4                                       Comedy   \n",
 561 |        "\n",
 562 |        "                                          genres_arr  genre_count  \n",
 563 |        "0  [Adventure, Animation, Children, Comedy, Fantasy]            5  \n",
 564 |        "1                     [Adventure, Children, Fantasy]            3  \n",
 565 |        "2                                  [Comedy, Romance]            2  \n",
 566 |        "3                           [Comedy, Drama, Romance]            3  \n",
 567 |        "4                                           [Comedy]            1  "
 568 |       ]
 569 |      },
 570 |      "execution_count": 9,
 571 |      "metadata": {},
 572 |      "output_type": "execute_result"
 573 |     }
 574 |    ],
 575 |    "source": [
 576 |     "# now lets add another column to our dataframe which will count the number of genres for each movie\n",
 577 |     "counter_lambda = lambda x: len(x)\n",
 578 |     "movies_df['genre_count'] = movies_df.genres_arr.apply(counter_lambda)\n",
 579 |     "movies_df.head()"
 580 |    ]
 581 |   },
 582 |   {
 583 |    "cell_type": "code",
 584 |    "execution_count": 11,
 585 |    "metadata": {
 586 |     "collapsed": false
 587 |    },
 588 |    "outputs": [
 589 |     {
 590 |      "data": {
 591 |       "image/png": 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DQ9J7ae7D/VeaW7MO3ljhud0tLSIieklND+OjwC627+52MRER0btqznb6PfBwtwuJiIje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\n",
 592 |       "text/plain": [
 593 |        "<matplotlib.figure.Figure at 0x10495ff50>"
 594 |       ]
 595 |      },
 596 |      "metadata": {},
 597 |      "output_type": "display_data"
 598 |     }
 599 |    ],
 600 |    "source": [
 601 |     "# now lets analyze how many genres do various movies have by plotting a histogram of genres\n",
 602 |     "\n",
 603 |     "# introducing the matplotlib package which lets us plot charts, graphs in Python\n",
 604 |     "%matplotlib inline\n",
 605 |     "import matplotlib.pyplot as plt\n",
 606 |     "\n",
 607 |     "plt.hist(movies_df.genre_count)\n",
 608 |     "plt.title(\"Genres Histogram\")\n",
 609 |     "plt.xlabel(\"# of genres\")\n",
 610 |     "plt.ylabel(\"# of movies\")\n",
 611 |     "plt.axis([0, 9, 0, 5000])\n",
 612 |     "plt.show()"
 613 |    ]
 614 |   },
 615 |   {
 616 |    "cell_type": "code",
 617 |    "execution_count": 12,
 618 |    "metadata": {
 619 |     "collapsed": false
 620 |    },
 621 |    "outputs": [
 622 |     {
 623 |      "name": "stdout",
 624 |      "output_type": "stream",
 625 |      "text": [
 626 |       "{'Mystery': 543, 'Sci-Fi': 792, 'Crime': 1100, 'Drama': 4365, 'Animation': 447, 'IMAX': 153, 'Action': 1545, '(no genres listed)': 18, 'Comedy': 3315, 'Documentary': 495, 'Musical': 394, 'Romance': 1545, 'Horror': 877, 'Film-Noir': 133, 'War': 367, 'Fantasy': 654, 'Western': 168, 'Children': 583, 'Thriller': 1729, 'Adventure': 1117}\n"
 627 |      ]
 628 |     }
 629 |    ],
 630 |    "source": [
 631 |     "# now lets see how many movies are there for each genre\n",
 632 |     "\n",
 633 |     "from collections import Counter\n",
 634 |     "\n",
 635 |     "flattened_genres = [item for sublist in movies_df.genres_arr for item in sublist]\n",
 636 |     "\n",
 637 |     "genre_dict = dict(Counter(flattened_genres))\n",
 638 |     "\n",
 639 |     "print genre_dict"
 640 |    ]
 641 |   },
 642 |   {
 643 |    "cell_type": "code",
 644 |    "execution_count": 13,
 645 |    "metadata": {
 646 |     "collapsed": false
 647 |    },
 648 |    "outputs": [
 649 |     {
 650 |      "data": {
 651 |       "image/png": 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qQcGk4EIReQ54PCqOWZhuzyJaA/2BW0uos/g6WDX+eQrGww4mOflsUlIuICurGUcdJfzt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\nEUkAbgMaAW1UtS3wYfj03N1FbB+P8SS+BPgAmOH1mwlcICJxwGvA5ar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79uzAjBkzMn//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\nq4IDzczMqvA3UHh3cuvyAzwAAAAASUVORK5CYII=\n",
 652 |       "text/plain": [
 653 |        "<matplotlib.figure.Figure at 0x110a2d450>"
 654 |       ]
 655 |      },
 656 |      "metadata": {},
 657 |      "output_type": "display_data"
 658 |     }
 659 |    ],
 660 |    "source": [
 661 |     "# now lets plot this genre distribution as a pie chart\n",
 662 |     "plt.pie(genre_dict.values(), labels=genre_dict.keys())\n",
 663 |     "plt.title('Genre distribution of movies')\n",
 664 |     "plt.show()"
 665 |    ]
 666 |   },
 667 |   {
 668 |    "cell_type": "code",
 669 |    "execution_count": 14,
 670 |    "metadata": {
 671 |     "collapsed": false
 672 |    },
 673 |    "outputs": [
 674 |     {
 675 |      "data": {
 676 |       "text/plain": [
 677 |        "<matplotlib.figure.Figure at 0x11145b350>"
 678 |       ]
 679 |      },
 680 |      "metadata": {},
 681 |      "output_type": "display_data"
 682 |     }
 683 |    ],
 684 |    "source": [
 685 |     "# we can also save the plot as an image to share with other\n",
 686 |     "plt.savefig('./movie-genres-pie.png')"
 687 |    ]
 688 |   },
 689 |   {
 690 |    "cell_type": "code",
 691 |    "execution_count": 17,
 692 |    "metadata": {
 693 |     "collapsed": false
 694 |    },
 695 |    "outputs": [
 696 |     {
 697 |      "data": {
 698 |       "text/plain": [
 699 |        "[]"
 700 |       ]
 701 |      },
 702 |      "execution_count": 17,
 703 |      "metadata": {},
 704 |      "output_type": "execute_result"
 705 |     },
 706 |     {
 707 |      "data": {
 708 |       "image/png": 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C3wf2xkMzNwLbm5lJuhU4Gbgd+AXwdTObUeV8EaPvgPIPBHFNg6A2tWL0DT16SfsA7wPu\nlXQX/pR9CnivpG5gOTAf+DCAmc2VdC0wF1gGnJCz2icC04C1genVjHwQBEHQXhrG6M3sFjMbambd\nZra7me1hZjPM7Fgz2zVtP9zMluaOOc/MtjOzCWZ2Q277HWa2i5ltb2anDFShoJxxssHSoZNktIOy\nlKVTZJRBh5BRn5gZGwRB0OHEWjcDSMST209c0yCoTax1EwRBsJrSsYa+jHGywdKhk2S0g7KUpVNk\nlEGHkFGfjjX0QRAEgRMx+gEk4sntJ65pENQmYvRBEASrKR1r6MsYJxssHTpJRjsoS1k6RUYZdAgZ\n9elYQx8EQRA4EaMfQCKe3H7imgZBbSJGHwRBsJrSsYa+jHGywdKhk2S0g7KUpVNklEGHkFGfjjX0\nQRAEgRMx+gEk4sntJ65pENQmYvRBEASrKR1r6MsYJxssHTpJRjsoS1k6RUYZdAgZ9Wlo6CWNlTRT\n0v2S7pV0cto+UtINkh6UdL2k4bljzpI0T9IDkibltu8h6R5JD0ma2taSBEEQBFVpGKOX1AV0mdkc\nSesDdwCHAR8EnjazL0o6AxhpZmfm3hn7WvwF4DdReWfsbcBJZna7pOnA18zs+irnjBh9B5R/IIhr\nGgS1aTlGb2ZLzGxO+v4S8ABuwA8Drki7XQEcnr4fClxjZq+a2XxgHrBXajA2MLPb035X5o4JgiAI\nBoimYvSSxgHdwK3A6Ow9sWa2BNg07TYGWJg7bHHaNgZYlNu+KG0bEMoYJxssHTpJRjsoS1k6RUYZ\ndAgZ9Sls6FPY5kfAKcmz79sPjn5xEARBCRlWZCdJw3Ajf5WZ/TRtXipptJktTWGZJ9L2xcAWucPH\npm21tldl8uTJjBs3DoARI0bQ3d1NT08PUGntGv3OKLp/u3/nNEh/ewr+dhk9PT309PT0W5+8vHaU\nZ7CuZ6U8mT7N/u5dhk64HmWoH/09Pq5n9d95WbX2nz17NtOmTQNYYS+rUWjClKQrgafM7OO5bRcA\nz5jZBTUGY/fGQzM3UhmMvRU4Gbgd+AXwdTObUeV8MRjbAeUfCOKaBkFtWh6MlbQP8D7gLZLuknSn\npIOBC4ADJT0I7A+cD2Bmc4FrgbnAdOCEnNU+EfgO8BAwr5qRbxd9W8XBklEGHTpJRjsoS1k6RUYZ\ndAgZ9WkYujGzW4ChNf59QI1jzgPOq7L9DmCXZhQMgiAI+kesdTOARJih/cQ1DYLaxFo3QRAEqykd\na+jLGCcbLB06SUY7KEtZOkVGGXQIGfXpWEMfBEEQOBGjH0Aintx+4poGQW0iRh8EQbCa0rGGvoxx\nssHSoZNktIOylKVTZJRBh5BRn4419EEQBIETMfoBJOLJ7SeuaRDUJmL0QRAEqykda+jLGCcbLB06\nSUY7KEtZOkVGGXQok4xRo7qQ1PSnq2tcW/XIU2iZ4iAIgqAYzz67lFbCi0uXrhRxaRsRox9AIp7c\nfuKaBmVnMOtoxOiDIAhWUzrW0JclXlcGHTpJRjsoS1k6RUYZdCiTjHYQefRBEARBUzSM0Uv6DvB2\nYKmZ7Zq2nQ18iMp7Yj+VvS1K0lnAccCr+IvEb0jb9wCmAWsD083s1DrnjBh9B5R/IIhrGpSdf9UY\n/eXAQVW2f8XM9kifzMhPAI4CJgCHABfLSw3wLWCKmY0HxkuqJjMIgiBoMw0NvZn9Fni2yr+q5QId\nBlxjZq+a2XxgHrCXpC5gAzO7Pe13JXB4ayoXo1PidWUpR1lktIOylKVTZJRBhzLJaAdlitGfJGmO\npMskDU/bxgALc/ssTtvGAIty2xelbcG/CEcccXS/J4EEvWnlmsb1DFqhUB69pK2A63Ix+k2Ap8zM\nJH0O6DKz4yVdBPzezK5O+10GTAcWAOeZ2aS0fV/gdDM7tMb5IkZfsvKXpSxl0aMdtFaW8pUj6E0Z\nY/QtzYw1sydzPy8FrkvfFwNb5P43Nm2rtb0mkydPZty4cQCMGDGC7u5uenp6gEq3puy/K2S/ewr+\ndhmDrX+7ytNufZq/nr31j+sZvwfytzObVVE/Z8+ezbRp0wBW2MuqmFnDDzAOuDf3uyv3/WPA1en7\nROAuYE1ga+BhKr2GW4G98Nj+dODgOuez/jJr1qxBlwEYWAufSvnLUA6z8pSlLHoMXll6PxtlKEsZ\ndCiTjMGso0nGSja1oUcv6Wq86dlI0uPA2cCbJXUDy4H5wIeTdZ4r6VpgLrAMOCGdHOBEeqdXzmh0\n7iAIgqD/xFo3A0jEkyFi9LWJGH1nUsYYfcyMDYIg6HA61tCvPOA1ODLKoEMZygGdpUeUpVw6lElG\nO2i3Hh1r6IMgCAInYvQDSMSTIWL0tYkYfWcSMfogCIJgldOxhr5T4nWdUg7oLD2iLOXSoUwy2kHE\n6IMgCIKmiBj9ABLxZIgYfW0iRt+ZRIw+CIIgWOV0rKHvlHhdp5QDOkuPKEu5dCiTjHYQMfogCIKg\nKSJGP4BEPBkiRl+biNF3JhGjD4IgCFY5HWvoOyVe1ynlgM7SI8pSLh3KJKMdRIw+CIIgaIqI0Q8g\nEU+GiNHXJmL0nUk76mhX1ziWLl3Q0vlbitFL+o6kpZLuyW0bKekGSQ9Kul7S8Nz/zpI0T9IDkibl\ntu8h6R5JD0ma2lIJVjFdXeOQ1NSnq2vcYKsdBMG/OG7krYVPdYqEbi4HDuqz7UzgJjPbAZgJnAUg\naSJwFDABOAS4WN68AXwLmGJm44HxkvrKbCvtiHG1crFbbYVrEXHH9hPXtL0yyqBDmWSUkYaG3sx+\nCzzbZ/NhwBXp+xXA4en7ocA1Zvaqmc0H5gF7SeoCNjCz29N+V+aOCYIgCAaQQjF6SVsB15nZrun3\nM2Y2Kvf/Z8xslKSLgN+b2dVp+2XAdGABcJ6ZTUrb9wVON7NDa5yvFDH6/sZQI54MEaOvTcToO5N2\n1NF+yhiwPPqoeUEQBCVlWIvHLZU02syWprDME2n7YmCL3H5j07Za22syefJkxo0bB8CIESPo7u6m\np6cHqMTR6v2eM2cOp556auH9q/2ukP3uKfS7v8dnMnp6enrJalb/7PfUqVObvn7Vfrdanr5laPX8\n2e/mr2dv/dtxPcpQv8pSP8p0Pftbv9pxPZ3ZtFo/m6sfs4Fp6fc4amJmDT9Jwr253xcAZ6TvZwDn\np+8TgbuANYGtgYephIduBfYChIdzDq5zPusvs2bN6rcMwMCa/NDP43vLaEc5Bu9atL8sZdGjDPWr\nXXr0V0YZdCiTjHbU0X7KWMmmNozRS7o6NR0bAUuBs4GfAD/EvfQFwFFm9lza/yxgCrAMOMXMbkjb\n90xNz9rAdDM7pc45rZFeq4KI0VcoS1nKokc7iBh9Z1LGGH1MmKqvB2HonbKUpSx6tIMw9J1JGQ19\nxy6B0Cn5sJ2UG9xJekRZyqVDmWSUkY419EEQBIEToZv6ehChG6csZSmLHu2gHaGbVtZEGT16K5Ys\nmd/keYOilDF0E4a+vh6EoXfKUpay6NEO2mHoI85fPspo6Ds2dNMpsbZOijt2kh5lKUs7iBh9e2WU\nkY419EEQBIHTsaGbdsQuI3RToSxlKYse7SBCN51JGUM3HWvoy/AQhVGCMPS1KUMdDdpPGQ19hG5K\nTifFHTtJj7KUpR1EjL69MspIGPogCIIOJ0I3vY+K0E0NylKWsujRDspQR4P2E6GbIAiCYJUThr7k\ndFLcsZP0KEtZ2kHE6Nsro4yEoQ+CYNA54oijkdT0p6tr3GCr/i9BxOh7HxUx+hqUpSxl0aMdlKGO\nloW4rxAx+iAIgqBl+mXoJc2XdLekuyT9IW0bKekGSQ9Kul7S8Nz+Z0maJ+kBSZP6q/zqQCfFHTtJ\nj7KUpR2UIUbfDtqhx6hRXR0ZQuqvR78c6DGz3c1sr7TtTOAmM9sBmAmcBSBpInAUMAE4BLhY3j8J\ngiAoBc8+uxQPmTT3aXa5lVVNv2L0kh4DXmNmT+e2/QnYz8yWSuoCZpvZjpLOxF9ce0Ha75fAOWZ2\nWxW5TSvV7nVq2iEj4o4QMfralKGOloWy3NcSxNfbIaPtMXoDbpR0u6Tj07bRZrYUwMyWAJum7WOA\nhbljF6dtdUR3TosaBEEwWAzr5/H7mNlfJG0C3CDpQVZuhlpsbicD49L3EUA30JN+z05/+/5Ov1aK\n1dXav93H97Tl+ExGT09PL1k9Pb3lF/09depUuru7Wz6+ndcjX6ZW9Wn+evbWvx3XY86cOZx66qkt\nH9+b5srT/+tRqV/Q/+sR9auv/tm21o5v7nrMBqal3+Oq6JEws7Z8gLOBTwAP4F49QBfwQPp+JnBG\nbv8ZwN41ZBlYkx8sTxlktHZ8bxmzZs2y/tIOGWUpSzv0GDlydHPdRbDRo7fqpcfglaX99by/ZSnL\nfS2LHiWQQd9PyzF6SesCQ8zsJUnrATcA5wL7A8+Y2QWSzgBGmtmZaTD2+8DeeMjmRmB7q6KAx+ib\n1av9scuI0VcoS1kGL/7Z/ntShjpaFqJ+tVXGSjH6/oRuRgP/lwZOhwHfN7MbJP0RuFbSccACPNMG\nM5sr6VpgLrAMOKGakQ9608oLVCBeAB0EQYWWB2PN7DEz6zZPrdzFzM5P258xswPMbAczm2Rmz+WO\nOc/MtjOzCWZ2QzsK0Om4kS8cYVjxyTcOnZTnXBaiLO07vl2URY8yEjNjgyAIOpzSrnVThthlGWL0\nnRS7LIse7agbZXgncbtklIGoX22VEWvdBEE7aCWk1qlzPbq6xnXksgGdRBj61YCyxC7LokfQm/7e\nl3aMI7WDqF+1CUMfBEHQ4USMfgBllCBW18JxNaR1kB5lqBtlktFfop731qMEMiJGHwRBsLoRhn41\noCyxy7LoEfSmU+5Lp5RjIAhDHwRB0OFEjH4AZZQgVtfCcTWkdZAeZagbZZLRX6Ke99ajBDIiRh8E\nQbC6EYZ+NaAsscuy6BH0plPuS6eUYyAIQx8EQUdwxBFHxwzdGkSMfgBllCBWB7RnqeOIoZavfrVL\nRn8pSz0PGStkRIx+daQsU9SDctLKWjWrgxfcSaxyQy/pYEl/kvRQegNVsJoQMdRyEgu0dT6r1NBL\nGgJ8AzgI2Al4j6QdV6UOweAxZ86cwVYhCFZLVrVHvxcwz8wWmNky4BrgsFWsQzBIPPfcc413CoKg\n7axqQz8GWJj7vShtC4IgCAaIGIwNVhkXXHBhpL8FwSAwbBWfbzGwZe732LStCitlCDXEU5LKJqP5\n4ztbRvMsXbqgFGUpZ/0qi4yy1K+QUVXuqsyjlzQUeBDYH/gL8AfgPWb2wCpTIgiCYDVjlXr0ZvZP\nSScBN+Bho++EkQ+CIBhYSjkzNgiCIGgfMRgbBEHQ4ZTW0CvR4rFDJQ1p9fj+nj8YeNJ4T9AG0rOy\nTgn0KMUz10+7sa2k9QZTh2qUztBL2lDS3pZo4riNs+9m9k8zW54/vtkL1/f8ZaiAzZA9uAOhd39l\nStpe0nhJW0raTdKOktYteOwHwO9xf3RIstaRtEF/5bSbRtc3zTBH0hRJ+xY5psF5dgU+0sLx62e6\ntIP8M9eOepsasKYdgmbsTu5cmb7nA2s0e3w1HdrZ+K7q9MoibA18Q9I9wNfM7J6Cx50t6Wo8o+cI\nYDpwO3C7mf05XTgVuYmStgWOA/5gZj+Flm/+BGAC8BDwiJn9rQUZawI74IuMPA08bWavFDj0FEmX\nm9nS9DCqVeMoaSKwAT7B7c+tXIucrDOBjYC3AP/A02tfBpZKug2YbWbP1Dh2Q+AASf8GXAH8rMVr\nOsTMlgN7AIdL+m/g0ZQskP2v2nGfBXYE7gMeAf6Ujnu2WR1yMkcAE5OcJdBUXXsbcE/+mKJ1vA9b\nA5vmdBIwFFhe7VpIGprq0sXANGCmpGFm9mqT512hr6R3AF8ATjKzm1t83tYHXgssNLOHa93HBjIm\nAK8BFqTPE0XqWCrDEOAFYBSwYhp4s/ckGfjdgYMkPWlm35A0GnihlfoOJR2MlbQ98C5gY+BG4Ldm\n9nK9hzB37HjgYKA7fTbHK/EbzeyWOsdlFW5P3LvZCBhjZntJehuwrZl9vYDuQ5PBOA7YFn8YhwP/\nxBvWz5jZlU3ImYI3OmOBZ/CK9Dfgy2Z2Y53jvwhMAo42sz+lbcOB9YEzgP80s+cL6PElvOIasBbw\nCvAqcGbn6aG6AAAgAElEQVSzBi55V+8GZpnZE2nbGGA7YG/gQNyAnmdmVVfNkrQpfk0n4kZuupk9\nnf7X7AM1ATgd2AzPAPth7n8ryZL0RmB7fDb3lnj9XBNvqF4BTsuMdYPzZnVte2AKcAzwJzM7IHno\n69S7tzk51wLPApfjDcUTRcqdO36Ymb0q6TzgAOA84P8aXcOc/pcA3zKzu5s5bx1578Lv7RLg+2Z2\nfxMyNgEuAEbj92Zd4EXgHjM7tqCMzYDv4R75UNy5WYYv23J0Af23A34FzAUuwevnwqINYGbfJB0O\nHIk/b8+Z2fGSDgW6zOy/i8haCTMr7Qf4NF6RrwS2arDvh+v8bxSwRoPjh6S/JwNnAm8Avpu2HQNc\nWVDnTM4M3Hj9D/BmvAL/EHhNk3JmAXul7+vj3sbJQHcBGe9JehyVPl/HDelpBXUYiXs1e+Le75vx\nBviEFu/nmsCG6bN2jX3eAVxeQNaxwJ+Be4HD+1nPJgI/w9N+j2qw71BgHTzsuQmwW9L5VGDNJu/t\nJ4D/wBvk/87qMW48i1zL84CZwHeBbwMXAee0UP7JwPV47+ol4HG8t7JDg+vwaKof5wPvxJ2h/tyH\nIcAuwNnA1cBbmriW7wR+ndvehffu31NARubwvgO4Nrd9WNLnoIL6b4w7iZ8DbsbnDC3FIxNFjh+a\n/l6CO6sfzO4n8F/Al1q9tqUL3UjaD1/obBQwB3/4NgRukDQVuNT6tJDJi58CfFvS5sDJZnampGHA\nP61GKKAGY/CJXAcDv07bdsYrfkOs0uPYDLgj/X3azGZJ+nfgySblLARGSlrDzF4C/pg+NUldyDWA\n+3EP5/v4BLXPmNnJTcQtR+Ce7h195BeKp1ehB2807wFelLQY7+Iuxb3i583sOkm9vNmcpzMJN0pK\nZZuO95ROkHQw3kspdH2T3K2BrfCe0v8BFwJrSXodMNXMHu97jHkva13cOM/Dvb1WPdqtUxlej4eD\nwL3RRQWOXQZ8Hu9p7Y73Gjdv5uSSxpjZYjObhodgsu3jgX3wOlOPD+KOxzZ4r3MrSc+YWU8zeuTY\nDi/Li3hv+JeSbsIdi1rrIme9j1eBm5L+w8x7Vg17V4nsLR9rAC8nz/wJM3sBdyTubdRbTP9/StJl\neCTg02n7ZkDRwdnsmX8KdyYmAVelbdsDPy0oZyVKZeglrY1Xnplp01/MbGr630bAbXhstm9XaBf8\noQP3PHdP35fXuzl5cob1ItyrmgJcmOLB3cC5TZRjDeBr+M26BXhX6qbviXtLReWsjVfC84GfSHoS\nrwRPmtmsOoe+Hu8NrY97fRvjXms2Oa1R+CuLwR4InCOpG28s7jKzR83sry3GghcAv8ArcA/uPc4B\ntgCeAL6Mj6n8PX9Q7t7shVf25cBiM/tdTucHcCNZyNCneO6HgHHpuGuTXhvg3uF/AR/I7Z81Nlnv\nbE9gPPB3ScuBq83s7CLnzpXn5/gDfCjwkTQW8gbgnCJygLfihvYB4D4z+4WktYocKGkL4KOSPgNc\nSmXc4WHgcTO7vEEZ/ol7rTfnZK6D9wKbIh33IB72eBR3MD6PG+5Dge9IeqtVH5vKjPQuwGHpmblJ\n0lLgeWCp+Uq5dYuT/o7Cl0//PDBH0gv4ONIvzazGUi296sYUvNF7u6Tj8UZiLGkcpRG55+l8/FnY\nF3hY0gn4+NzNtY5tRCkMfc5o7ASsb7kYds7ovABcbGZ/rSJiNyo36/W48QDvCjY1IGNmiyT9GK8k\nh+Ld2Y9TMZJFZCzDu9NIOgdvPN4OfKBJ4zgMN7Br4IO6W+JlXYSHdGrxRuCbwMzseqWK91VJF1ga\nYK6jfzZo+3PcGO4MnAJsnWLqJ5jZJU2UI5P7IPCgpCNxz/4O3KM+MJ1npQHF9OAenfb9hpnVWuv4\nbCpecU1ysvcExprZe6uc8y48zNbr0PT3UDwG+yze6Mwseu6+mNkM+fsYXgF+iScPXATcWkf/7Hk4\nG792z+KOzX9I+omZnVbw3AslfQ6vW3Nx4/pv+NjUOpJuNrMzq5w/i0dvgdeJXYC7cQN9P5Vnrxn+\njjtTa5lZr16EpFnA+2sY+Xyj+SzeWG8DfDKVaz3gvcBj9U6eeyZvxh2z8XgDPBo31Dc0OD7T4Xjg\nNDwEtNTM/p7GP44H5teTkSHPAjsIHzu6Fb8fPwRu6HttmqLVmE87P1TibP8GXFhnv2E1tp8A/Ba/\nIXfjXuPHcQPxRrzxKKrLJnjoaG+88g9tpTx4a/xR3HNdL/c/NSlrNP4wbZLkjqRO7LTK8UOz64Zn\n7zwG7N6kDpvhA4TgIYKR/bzPdwFb9vnfTbjh7XvM1nhP7r6k++N4A/RfwOF4N7kVHT4KfCJ/T9K1\nGoL3xDarcdwN6TpeAhybtl0KHNCkHsOBt+XrBbBRE/r/jj4xcbyx2KvFezMKj/srlW/PGvtldenn\n6Xm9Bw973Ys7Vfu3cC8OAo5P37fCe9S75K7LZg3krEGfcR28Z7ZnM88v3gM+FtgjKyuwYcFjR+K9\nUYDbsvKl69Lwmc/VwTcBN+e2r9XK/ez7KYVHn2Md4KjUrZ6Jd/X/DCwyp+rotZldDFycwjtj8fDN\nODzVakd8ULJmjD3X9doPOAsPA22I37wNJP3BzBq+9jDnbZ2LG6i/ksYbUrbIaWZ2TQE5mT7vwrNU\n9sAr8z+AU83s+kYyMpI+yNOznsErUs1uaB89PoT3RP4CDJV0u7U66u+6LE/jJj8C/lvSt/FrtCcw\n3MxWik2b2WPA3ikb5Vh8fGII7ln/B55xMkUFMrL6MALYSdKuuLf1glV6Mn9Ln166p6//g3ej7wF2\nS+MGB+Chuobk9Hw98H7gF2n8ZZmk0ZIOMLMf1Do+p8dTwCHylOK/m/fcNqX4vc3X+cPxsan1cY/8\ns+be6ErhudwzONrMfiDpI2b2ziTzMgqOZWVqpL+H4z02gJPwHt7bJZ1qZo/QeKxgYz+9RlrKBDOz\nF3MyaytQ6aFsgydhbItfix3xe7QNHi5uxHJguqRvAmumcbJJwIt9r2EtVfAe7cbAPyS9FrjbzP5R\n4NiGlMLQ5yrvYjw3dzgej90P92jPAe6rFRfOKq15mt3TwN2SNrUm081w7/9BMzslGeYNcW/25YLH\nZ+U4APcWf5f0G44b/j83qc8ngE+b2cwk5wDgeEl/strph2vj3b3leNbB3sDa+L3eCVjXzHZudOLU\nNf8g8Fl8UGsL4ERJG5rZhU2WYwXJUHxePvHpoKTny3jK50ppjZLWNO+2H4N7TJemf10i6TQqXeJC\nE2xyde0F/CH+HPDnXDz2a2b2VJ3jL5cPZn8HH//4MHCGmRUN3WR67kslHJiVdz98QLKmoc9xMn5v\nNgaelvR64H6rE0vuQ3bOT+G9qSvwENKJwBclfdp8MLKiuDfSo/BG8EX5vAYkHYb3pN9Ac3U802F7\n4Hx5WuEwM+uWdCUeMnykViOe2/5aPHliHUnfxUNRf7Zi80YyA/smPDHgVNyBAL+276CAoTez5yV9\nDw8bLcLHG5bhDVdDcuVbns77HWCBpOdwZ+hi60caaykMfYZ57vCNKQ68Dm5oNyXF2Gq1jNlF6tM6\nX4QPmhU5b3aRbwG2T8bsCXyA8OEm9M/0Ow/YUtLjeKz/RTMrHLvM6TOMlDmQynaTpK/gFagWk/CK\n+Rgewvoj8D78eu7LygPZvcg9PLsBT5nZjPSvOfIsmc/h2SktkQYLt8QN9D24Z7oo3bdqDXlW1r8C\n3ZnXJkl4g/qt9P9mJ8f8CM9o2BpvxDbHe4MrNeq5epWl2l2IZ/t8vO8+jU6aMz4PAT0pRr8Qvy+7\nUyfWn9PjDfj9+RLeY9wC72nMqHVsFT1MnjSwvZkdlDvHyXiorFo92QZvYL4HHGxmr6QxqA/hed9z\nCnqvmQ7ZPbsB+DH+rL8zbZuANx75/Wodfx+VXvRncEO5qaSzrPhY0tZ4w7s/cGfath0N4vvJ8wZv\n4ObjMfod8Of2iWY9cjP7CZ54sRl+vbfEQ7f98uxLY+iTAXgv7tndamaT5WtGPGJmRT3qYbhh2J1U\nUXPhlHrnzh7SbfDW/EBJf8AHmP6Ce5KNRu4zWcPxcMcBuDF5AnhC0l+swCSYnJyh+AP1MUlXAEvk\nE7deMLN6XlM2uHYf8HMzu02e1bBFejDrTlnPPTwPAEMknY/HY5fgjUizvZKsPFkD8gE8H/9w4DAz\nu0vS0Sk89mgVfTLDcRaeiXCtpAfxe/0CaeCyGQOTeAH34uYB11v9GYdDcMP+JvzhR9JaZvYPeVbW\nlmb2pSbP/1M8hfEM4LHkzd4J/KTOMZn3uQ9e5HvxGHCrrIWHGz6L19WHcYO3rlVPeliGD5xeAawh\n6Yd443IV3lg3PSANYGZflPRLfB7CHak3/biZzW90bHp2H026rGNmp6ftW+I9lEbnzur7f+P25zhg\nqqQ34c/w1AYiJuOh1RfxAeFH8EZ8Q2BE6n0Xsh1J76l4PbjNfILnLXgj3i8GfWZsLlZ4CN49/xFw\nipn1SHovcKSZvbNO2GZ7vFL8I7dtO/zhm1nE0KdjhuFZA6filX083pqOxgfa6k49zpXjUNzTOxtP\naRyLe1wLzOw/C1ySvnI/jntta+JZAT+0PnntuX0zj28t3KC+D58T8E48lPTTotcjydsHX05iCB5W\n+ANwfpEHsI5u9+APxo+B/zKz2yX9Dvikmd3SyDOW57i/Ae+aNxzv6HNsdo/eiDc03bjn+DfckP+g\n2j1SZZbyZbgTclnuf1/A8/8vaEaX3PEH4THheXiKZM3Zxjn9T8F7q5fgDfITwDMtNHZI2hmvq0Nw\nT/Rp4PNmdkOf/fqG1PbAl7HYEE+NvBt/fppudOXzEjbFGx5wg/n3vqGjOsefjj+nb8bDgcKfuzm1\negNVZLwZD3kelOTMw3uvt1mDma3JKdsSj+vvjF/HzZJOB9a7p33krIM3LDvi9mckfj9mmdkxRWTU\nojQePX5xbsW7zg+mbcvx0AdUvKq+HAVcKun/4Ysz3Y+HKx6TtJGl6fEF2Aq408x+2aL+GevjBqNv\njnEzi3bNw8u6K24Qv5Y3zrViltkDljzNy/DMjLfj13SrFJKq+/CkBncG6UHBu6Ob4mMFRXtWK5F7\n+J+kMrknC2etj/dC6GNMssZhOH6fzwK+YmZfkS9+17A8fYuX/r6dSm/teipZPA+k8/ZqDHPfrwU+\nLunveCrki3ijVWggNleuNXGDsjM+YPg7fM5I3SUp+tzzF/D48evxsNZySd+0OuMLufPvjF/zJfjg\n6b/hzs0LwD+qXdN0H7rxRvEpvH6+ks5/Bt6T3Ndycxsa6JA1WgdQ8YofxePkr+BebJFlIEaSHBk8\nrPakPEVxqpm9qaAuI/H6vT/uaBYiHXcM3hNajNeJGVkdllSrZ1SV5Ex+OCd/Y5qYv1OPQTf0ucr7\nNJ4JMQVP1wL3Wm5rIOIyM3tC0s14WGFz/MZvCGwm6d/qhTpyD/VYPGb6v3ia2oN42OjPReOviTcA\nH0gezyw8Dv2gFZ9kdCces30tHlM/GFhXPlgI8MU6Hv3QVI6nklG+Dx/E/jU+oH100q8qKWa7r5n9\nUp7N8TQeo3wUz6FfaGa3N74ENeUPw2dgnpM27SDpWLxHVs3ryRr3E/C6eg0eryRtM+CCJu5Pts+u\n+PyEKbgXPVc+wSbToZYXeBMeunkD7s3uj0+umVlj/17kGuh34IOeT+Pe48Z4N//H1iAPPjUSo4GP\n4T3FMXhD3IUb6iIci0+0ehqftPYobvCfxBeX+7tVz1v/Bu4QPQz8Jh13Lf6MrkVzcwmyRvcj+JyP\nl/EGZDF+ba6F2k5N7p7vjDsjv6eSnbMFBVaQzMkYASyWZ2A9jDd2RXq9m+DJDnvg5f878KykJaks\n99JgFnsffbrwtNBn8B7NU+nZ3aqojFoMuqGHFRf8KvlrBncGNpT0Vvym/yztVmtAZqkk4WGF2/Ay\nrYsb+uENjHx+RcdFeFdtKzzdbxKwhaRLzKzIqHum3yX4Q7M9nhp5EjBOnjY3t5Ec3PCYJMNnt2aZ\nDpsm3eplVbwen6h1s6R8zHAObuTHNyjDMnzijfCQzc54ps4OuAe6jMpgWSusjfdyNsYN5pn4tfoo\n1B3QHA9chhvWLCY9Gm8QoXZvrxe5e3Q5bhTuBybKUyQPwjMdepF6E9vhDfAm+ODvDrgh+XAz8dcc\nr8MX7VpxPnmWU5FlabcFdjWzhVTK3xRmdnryFrOF2d6ED6iOxcMG2+FGfAWpR/qrdP7nceN6k1VJ\niS1Idi9G4GGfU/G1XB6Wp3xmE+NqJWBk2xficyuuxXtY4OmZK4331JGxB55xtwneu3ssGevHGoRd\nHjazY1KodBR+bbbFU7sPwp/dhoY+15hlz9wCfExuKO7QrFQvm6UUhj4Ztn/HjeT1eGrlS3hFejnb\np+9x6p2T/Akzexfe7furpL9R8dDqnffNwG/M83W/mZO9AT44u7TJstxHzrNJXuwYCj6UuXL+u5m9\nO31/LMk6gvpT/NfDDdA6eKO3Bp6d8WbcU/gV9WddrpEM13uAHc3sM+Rm4CZvsj8cjcfWvyrPoV8P\nj4s+BSvf41wj/CTuMb2ByrIEWVgLmsi4Sb2WBbhRuAJfGvff8WyJO6voMR5v+J/HZyj+EW88Hwc2\nljSvCWOXyX0Oz8HfBQ+fPJUMdz29s0ZwbTxM8wF8LabngZet+eyOp+TJDrvjzsPzeB05zaoPiv8V\nj+Ujz/rZDzhV0qO4N/2A9Vm6osH587NRDXfUPi/pBnzsZF6f/WrJmS/pf/Cw3hD5rOY78eybuigN\nqOM9+CX4vd4dH4TdHnf86o0DHStpOj7OMwqPAjxAGi9RwTWlcg7I43gSSRfeo1+GhxRnF5FTj0Ed\njE2GY0v8Ap9rZq/t8/8zrM4glyqDZCcC483z39c3s5ckfRB4k5l9sIEO0/Au/EO4YbwLr3S/x5c4\nbRjzzMlaCx/kO57KTM65wNx6PYvc8Rvg3eoD8Qo3BTdy9+K9lB+a2SE1js2nlp6Vjr8Jz9z5G5U1\nz2su/SrPVNgIDwssNLP/kDTKzJ6RZ2YsN7NzGl+JleTulfQ5Ae9xXGSVlNjPAH80s+m1PPp0XS/D\nG4qb8K7tEnyRtkLjBrm6siPwMTPLx0I3xGdi3tLnmPyyCEPxxmUi7vVunn5fa01m3MgHcN+I92b+\nghv+vwNX1CpP7v6+Bw93vITX2SfxRuumIj1G+cS5D+B1bG469/N4D2OpagzWy9P9Nscb5Sfwnt57\n8DDUevhcgu8Vvwornn+Zjyltgi+pMBafGXp5QRlfwRvrrI4/YgUXMZT0UbzhnI9fi2xQ+59JtyH1\nGi95ptTP8Xq9I96r/Cd+P17Fl+woOkaIpC+Z2Sdzv9cENu1Hr2kFg+3Rb4hX+A/hMcrP4h75Q7jx\n3x+PwdYafMwq5GgqXuFLadv2FJilZ2aTAeQr9u2VPq/DU63GyPO267aGOf2OwlPf7sVnbs7DR9Ev\nJTfIUodX8Xjv6/AH+d34w7UG3sWumdOf6Zi8sQ/J5yLsjQ+0XWtm1xU4/ya4ETkMn6gyBnhS0v14\nr6nVLuTL+ODf1nhI7ABJr+A9roOoeOZVSYbgWLwBewPwrDWRqprokvR2PPS0bmp8/ol7f/viZe6V\n9ZMMa/ZCjT1wI3IXQM6haOUtQJelz/a4o7M5vvxDzUYrp9P/SLoOf3Z2wI3b62g8lpXxPnzRrAdx\nI38L7txk8v9Zo8E9Mp1nh6TvnXjv5n/xe9gKB+P344Pmq45+Wp5a2TAtMu33Ir6UcTaf4fb0v2vx\nsFqjbJdszfm34oOevweel/Qy3pOvOwvczH6SjPFyPPW3i0q2TReVUFK9cqyDe+/7AIdK+lqSvQhv\nhC7EHZx+UQaPflPc0G+GhwnGk+Lr+AtHptXxMrKLPALPS16KTxIaChwCfNXMft33uNzxmZe3P+7l\nvb3FcmRyvow/OKPwNTa+LX+j0p+twMtGcvK2Ssc/Kmksfm3Ac5VrTgeXvxlrCH49JuG9i23x7t/F\nRbv3kt6Nd0HH4YOf4/AG6OfWRBZBTp5wQ38kHj7aJJVpBO7J/qivYcl5sBvhoacD8YbhPtzzesia\nSPNMhmEiPpHuWdwwjsKN/Z5Jh/P61rWcHrPxEM+j+MM3EbjEzApnaeRkros/2AtJ2TZ1xif6Hjsc\nj2dviRvb6ebLRBQ9dxd+TzfEBy0n4L24bCzoPKuSeSZf0nlNKuGFMbghWlrAoPaVtSM+GHwE3njm\nvdjT8XdPnFjLwUvG8XTcKRmJh9QexjOYnsZ7RtsX1GVL3BH7Hu5cbYvPCL/FzP5fneOyejERT5Bo\n1XZshNftE/H78T38Oj+B36fNzGxSK7LzDKpHbz6yv0jSVPPZjkPxQnblK281I584BbjGfCW+d+IN\nxml4mOPwzPuqp0L6O5KUA9wiWWUcjTc2OwPD5ZOTXkuxKe0VpcwWSDpG0mvwLuWdVlmzplZ4Yxwe\n71wDb+y+j6eKLbMqMddapPDRLsmA3Z9krZRy2IS87IFYH5ifypbFhIea2Z9SQ9CXbID1dLzCX4kb\nol3wbvLP8bTaQmvcWJrpnOpJ9paubXDv9JtUBu+W9znOlKb+J13fi3trZwHflDS9SOOnSjrh2/CG\naye8wfurfNzg+3hWSz0ZG+D39Ld4r3Ff4K2STjOzQqurWlqnPZVpLfx5G4k7VltRY0ndKo3J/L5l\nK3L+xDK8Z7A38Br53JNn8EZ8DGnl1zpl+BtwrqSf46myC/Ee5354b+CcRgrkdO4G/mpmV+X+dzO5\nWc8NWBNfgqIH71H8A38HRiEPOoV2rpFnfW2Ij5dk829+g/cy+s2gGvqc0Zol6X1mdr+kC4G1JU0z\ns9/UOXY8vnrgl5JB3Qm/6T/FjUSt5WzzZDdjLHBS8jRm4gZuAT6qXmRae7bP/8MNxVN4Xu938ZBL\n4RQrAPmaGY/jWSnP4QurvQTsbLXXMhmFx3ufwD3UCbjH9yf5gNkfzazmwHLuXuyGe1srBqvk+dPv\nxrOAmiWbzXkk/mDPxD2xk/HsoO+YWbXKnBmONfDc+ZuSQVwDfyCyQfpmlz54Dh8HydbCv8VyeeM1\n7vfm+Noun8HDDe9Lcihi5BNZY/ZOvOe6Dp548Ag+plMzvp67N7vgRuTc3P8m4zHqprKhUjjqVfxF\nGwcAPy7gGNWS1exS4I8A/yl/qcjv8GuxKx4e+1/S4GMtuTkj/Tfgp+bLi3xXaV0kFXtheX5exwj5\nON/NuKPWg/c0i7APHn7eEu9RPIqvnXSrFXit5Apl/MVEe+P1+8Z6PfdWaNsb3FsheUvr4SGk++UD\nTZvjXsVZyeuoxe5UvI+JeHfrt/hCT69QzCi9S55adxs+aeO3uFf+UdyD7C5altQF+5GZ/d183fVv\n4vMBDmnSox6JZ7x8Cp+FOQqP7/+wlpFPhuBO88Hst+MDZJ/BK93OePzxI41Onf5OJHlruVDP1unT\nCpncXYAZ8pmt2+JjIM/hcV/6Ppw5g3s97rXujg/6LTOzJearEzaniIdvforXsTfhHvTd8jGImpi/\naepM/CH8TvJu30jxNxhBxbCMx9d2GYnPr7gO9wLrhdWya7gp8Ip6v1xkGcUX3VtZsA/OXthKb60/\npGf7T7gXvjceTrsK+J01zt7JrseZuJOGpLOA6+Vr9RR2zpKTMRWvn8fgve+98RBf3eNTT/RivC79\nB95oj8d7oRMa6dCrQNKl+DP6MeB7km5Kn00bHFqIwR6MBe+qLZLP1ns/XtB5wHHmLy6uFbtcj8rD\n8Tbcy/tP84lJD+KedCM+ib8MJHt59hp4V+wQPIbcsDuc029Lcg+c+UpzhVeby8nZCb8eXVTuz+/x\nwd2qpEr3UXyG5G/M7CG8R7BiwLKRl5Pznu7Bl4o+GTdI6+KD4oXeklOFTO7d+EPwHtx7vF2+vEO9\nF7avhXvf++C9lGdxb2kBHhct1D3u4xG/ZLlJSSneu1mf/XodJ2kE7v3/Idf4PUJllcOG5K7v1bgh\nugd4g3x1wtfiPci6x5oP/nUDv5d0N97N3w5PD2yKnFe8EenFGq2G55o8b3aNd8Dj0U/h13IYXlce\nBr7YQEx2LXfEowH74L3Qs9NnJg0mb8nTqh81swXmS4P8AR8c/zYFX+id1RX5nATDXwv5XWsy1TU5\nd5PwRJBX8fGsjfGximZX4K3KoBt6M3tI/o7Qa/CXIj8o6TgqGTO1JsPMwNcpvxE3sudaZene/fEU\nyZqk0M+rKe6aLXO8DFiWHqJ/N7O6rXofRgF7SPo+Hj9eiE/CWljk4ckZmMeAr6bvf5Sn4nXROBPh\nfjyscFxqNJ/Br+FtuJG9jgL55mZ2q6QL8Fzid+CVL3tBe9PkPKdvyNdo+SHusWyEx4VvT/9foVuf\nMNJYMxsnn1A0Hu/ij8o8qoLGPgsfvYK//3Mb3EguM4/3PprXtcpxU3FDcn9qZP6EG5JWGr/LUzjs\nAjzz5gt4amLNFDpJe+IG8S/4wPov8Pj8JsDZqQfZFObjBaPw8l2Utq0Krz67pnsBs83sY8mp2Rw3\ntH+D+nH/dO+HpH1PwrNSTjaz38gHq2s2mkn2mvjKnyPkr4FcjIc55+DO44ukLL4GcjbAQ00v4c/7\nMOCfkv5qVd7OVYcN8N7Ms+bjlk/jSxTfWf+w4gyaoVfvBab+z8ymJo8a3BvNUvlqzYz7s6RTcWOw\ngDSxSf5y8GV4GKYeO5CL46du2BrpQq9LgSnU2aFJx2fx0fuReCV+E94qX4aHHwqRwjOLk06fw8NI\nN5NefFznuJnkpuKnkNhWeNzzcNxra7Q408ZU8rN78GyAh/EYaivZNqPx+PMF+JyAGfjkoGX4ANYH\nrPrLvLNruinu7WGVmaC/SrJbeWftAXgsezweG35M0l/wxa+qre+SGZrj8ZDWLnjI8NO4J7wzzc9O\nHSvpLXgd/wS+IFqjchyH9zSXUVkO9xZSY1X0xLln7hh8zCRb4uIp+ZIdN1iB+R79JAu7jMDr2ooB\nYqd70bcAABbxSURBVCrLAzeM+6dynISHKq9KRn47/1f9sF56xrNxqLF4iDbLAtoBr+uvKVCWHXE7\ncSzes9oYd8oKvRsh15j14A3WrvLU0Pvw3kYz6zjVP1fzz0p7kfRFvKDfsoKTJBrIWw/PJni23gOU\nvLr/AH5mfd6hKukMfALWlILn3BD3BJbjjafhqVI74avfFR5Ykad7fSDJmY0PojYdj24G+WSYT+Hp\ncvuRVlLEQ2mX44aw7ztUi8gdhXtp8/GFv4ZSCbm9CPzKqiwvkQuZHI33bp7Ec70X4l7tz6zBTNIa\n+gzHG4/t8HJuhcdS32++5G9ROWvg9fX4gvtn6bcfwXPOH8V7gCNx43CF1XhzlzwT7bX4vcne3fs9\n3JtfF/ibmZ1YUI/suv4Bvx9/w1NnN8PHYE63JsaT+kOKqX+SymzSBbixv8YaLO6WkzEUj6c/iDta\n2wMbW5+Jb1WO2x2PEjwGbNC3cavXm0j/3xFveMfj42mfK6JvHXmbpXJsh9vCLdL3j5pZ3QykwucY\nbEMPKy7cmbjXdCE+qNlsNkUr552CD4DchWc9DMVbaQFXmq9A2UjGtrjuh+EGfj7uLX7NWlvO97f4\n1PatcE+jC/d+ump4v/1GPqV9Br6Oy7nm4xzvBv6ftSGHN3ee9fExmSx/e4mZ/bDWgyWfLTkiHTMa\nN0bd+ODhH5v16pOBnoLPR/h5g30zozgWz7JZQCV8sgX+ur29Cp4386SvxfPep8kH2Ybjg4kLihhY\neXbMKWb2Dnn+9kb4M1xzrkg1XfDn64iixwwE6bquhae4bkVlhvxH69Xz3LXcHx+32gtfI+fHKWS5\nyGq/QD6TcSRu5Mfjz+6DeCMzD+8l/dbqZ6j9GJ+49xhus+bhPc178Ear8PLIOZnj8fX470u/18Nn\n5rbFySuLoc/emXkc7llOx2OZLaV7FTxn/m32h+IGNXs58iUFH7zNga/jsfisO78n3gUU8CFrYoJR\nqvy/MLPd+mzf2JpYiqFZ0sPfg1/7kXjq437AJimGms0ObVbuN/AH4na8hzAHXxOlUFnkg7E7kLJN\n8HDFunjec1PxZPkktPPwEN8bzGxv+ev3trNcDnWV43bF02aX4b2stfF48m/M7PwmdTgSdyZ+VqRe\n9G3IJH0SGGFmhQeBq8gcgmeXnIbHl+cA964qTz6nx5r4ooNNOS9ZXZSvMfMV/Ln7spldJ+n/8MHQ\nIrPAs3Bt1tDshBv+1wCfMrNZdY6bg/d2H8ZDxxPxeroN/vwfYwWXa07yLsBtTw/+3GUL6d3cLod3\nUAx9rlXeDo+b7owbhDl4fGw43sX/gpk1lYPeD52G4tejsEGTv13oSDN7d/6hTKGci4Ff1+qS15C3\nJh7vMzz3finwdGoEW4lJN408g+F9ePjof3Aj10q+emZcd8J7STvhD8IoPHTzKvDeWr2e5P1/Fm+E\nf4M/CAAnWRMzMXN17Uh83OTH+MDdOyUdiM+IfmvfXoWkCaTufbr+a+EhjpH4rNymUhqTUfk0Puby\nM9Iy2Phgfc2ln1Mc+h/46oxfw9ekaXb5h3z4KFsr5xY8u2Mb3EjNLhqKapWcc7U1Xs+nJB2exnPQ\n/7doiFDSbamx/iWeofcXSbcAUyxl0dU5dsW9lme8rIevcVPIKZO/PGeSVcmTl6eNLi/6vKRQ7ZV4\nw3upme2eens/M7PXFZFRhEEZjM1dhI3wGW3fxleKWw8fhDB56t0Z+KDRqtCplYyDHams7jgkPcxr\nmtkLKQSzZxEhqqS1vQN/AMBb9FcBk3RTM93zVki6Y/6Wp8fxlM598KyQ86i8AKYw5llQC/AeWuZN\njsDDFTtTJQ899xDuh08Q207+wvOtcAN1PJ4x0Szb4xlIm1JZwXM3Kim0fQfQrkq6PiRf8nkhntn0\nIN77e6TISXMN9C74QPCReObQeNyDe4X6k53m4nH96ficg4Mk/RE3jHcD1xV0TjInYSfcoK54WYo8\ne2Stqke1lyyD7n143T4bTxaYjd/TPwM/rNWDTEZ0E3zM5peSvoQ/g+vIlzZem8pLi2qSGv718OSJ\nsXgd+Ks81fUpM/tmrWOTc7oFnt2WTY58AnfKnkjHF1njJqsXu+L3+GEq2ULbkgaq28WgGHpJ78c9\nu9vwqeTP2sqv6vsNuVH4knIgcI+k0bmYXlZBt8Zj/0XIHsL34d3pbM2fjZOcfr0YuJACud6C+UDn\nVfKZi8dQYJGpgudYjqd9PkPj1MQRVDJa/mGednszfo0K53znnIrv4D2Eo4AfyBc425vK8hR9e0u/\nx+/Bb/DQ0xZ443QgMFrSkVZslcQsg2gMvsJk3zePNZrfUCubancKZlPl9AA3RrulcZkFpOUg2hUL\nLshEfMGwA/B49v+mGHX2vNS6r9nLyafhDsgH8Z7vCXijfW4Tvd5d8HvyHvw+b4Ff1+EFjnsQH1Df\nIR27C36Ph+LOQMMXkuf0vAdv8H+Oz5/ZCF9kreGCjM0wWOmVn8UN/a/xlmt9+evZFuIt46X1urMl\n4gzcC88Ggp7BK8GNuOf2noJyspt+BzDTPANkhSFUwXWt2415tlAr3nMvag221mCN1HW9D3iHfKG4\nGSmU8hYqbx9rCvMleM/H14jZDl8Y7AukHllf/czso/LU3xPx8NGl+BoqawCjCxr5PFsAH5SnnM4m\nGQszW9JMWC6FjOamz/eLnjzXKO6IG7QT8R7VE/iKjVc2M57UCjkd7qPSyLxGPjP5/aQ5FXXIXk7+\nfTyUNxMP994DnFmkZ5Pq0TK80ZhpuXTmguyLL7twTWqkN8R7iZslmU0tKWxmj0v6Jj72NBJvuG+i\nwHo9zTBYMfrX4Q/PGrhxW4x7r+Pwi3VmszHQMtDH23oj/jKUwuul4+MTL+Mx3N/iSyA083q20lJl\nYLHW4mxfxKfB/0S+UNsH8RDfRnhm0I9b8T5TaGrnJOcFUkNa0DiMw5fIWBPP8GhqtcYkY3c8FLYx\n6TWXeG/tE9YgHbC/yLONxuOvbHwxjX9sk7ZtB2xhBVM0+6lHtnZSFqtfEw/fjME95C/Xel6q1J89\n8Ou5Lh7yuZc0E71eoyl/t/RxePbUnngvYjbecM7Hx1/qHX9M2ucP/Rk3y12DS4CfmNkMeZrlEy2G\nkeufbzAMPawYhHgnvpLfrbgX/7Sk4VYwj7bTSIOXe+JeQzfeJXzRzLYZVMVaIDcIegb+wuS75amo\nGwD3W5VX8CWD9Dt8Ovhz2UOUDNNueDzz0oIx6fzDtCPenX6KShx0CL5E7korRqbBwmziy4FUZqHO\nB84xszmtPuSq5GA/iceH7x9oTzqFRT6fzvky7nU+io8zLF4Vz1vypD9uZh+SLzsx0nzS4wZ4D6nm\nuxZyMvIvJx+L2463405VoZeTJ0O/Lt7QZ9ktI3AnYFPgBGsy2y8b34LGb8Sqcuy78RntL+Ozxu8c\niPow2OvRZ+9CPQXPh51ivhLdaoOk3fAY+EJ88sZf+vy/2SVgS4V8GveR+MM5E59lOwNfl+jlPvvu\nCnzbzF6fvL1lOWO/Be75FBrgTsdkjc3x+KD/yXiDsSmet73EzK6t4i3+HI+TzsLXXbkz6VJkRdRq\negzFp+mfhme7PItPpKuby98uUk9zL9ygbYaXfxQ+eDkSHzv49gDrcAxwqJkdlYzbIVZwQmJOxm9Z\n+eXka6RtawH/ZQ1mk8ozcz6QNSypnq1BZX2rS62JVyL2l3T+3fH6NgIft/xBu736VR6jl3QQPggz\nH8+y2RcffLkcT7Na3RiPT7w4FF+x80/0nrzxGzyO+i9H8sTXNbNHUkhmmpldJOkOqg/wdpHKaj5N\nPd9l3xwPuTTT+GXG+yn8pesv4z2G3jut7O1cgIfO3oJ7WYuAufI1kO6jYKZLTvfX46mEJ+AN3Wvw\nZXpfNbMZBcrRX7ajsvRv1nCuh09C2wYPfQw0O+EDjuAh2oeSHmvjM8qXVbkPK1AbXk4uaQfcuX1Y\nlfWtXsFXBG1lfat+k85/m6Rn8TG9C/n/7Z19zJV1Gcc/FwLKSyLTEIQZyFBA2DDEXCm6fBnaCxZr\ng1ZNy/Wm5R9OgjasBtl01mZltia2lqnxT2Ea8yVNqZUGfyBu8ACKDgZTmBBNUEGv/vj+bs6tPg8+\n55z7Pvc5z7k+Gxs7z8Nz/4D7XOe6r5fvV5N384q8ThXN2BWoPLEXbWJ+u57/rIGGp7nhFPye4d3L\nG4tQg7cjAz2aQnjSzO5BQWVFKtm5974b8DjwKTNbiSSEd7r7nvQmX0StWdcvLZEcu9K1b0BZ+jNo\n2qNXo3SXD8JaZLmXBcWJNDbp4qgssNFruvtPmNk29IZuRaD/NAqMy8wss9jcisYKN3nfHgdFchlq\nRp+O/i/vBOhv9uzFmJOfiZ6mSD+nUX2rQjBJfNyEnuZfRr2K9fRjaqfua7W6dGOahT0FrcBfgm6A\nqaQZVOQM1ef68UDDmlzeaHdMmkJzgfXuvtG0/Xy+u3/dehmRTFnX9ah++hq6+aejhaF7XG5i9Uof\nbEcTUo6y6Y+i0cqzvQHNnHpJT7GL0QLaWtQf+A6SPri1Bdcf5bIrPA8lEuNR6WpM+v0Cr3+KqN4z\nDEZPZTNQoncFSmj2pF/HfN9bAebkVqC+VTOY2YdSU/xCarsFu12GLH0OKjR1zSpr9HlyWdOmTq5J\nN0LKWO8mt7yBlDWPubzRCZiMXcajkbTX0msj+pquSF8fgj78p6DH+peAzY3UTk1zyX9090sbOH5T\n5N+wZjYfLT69gcol22lQD6nOM4wBLnH3+3OvnYBq2iOBE72fNoQlna9f73uTP0Jv5uRD0b/rzz4o\n0Kef07S+VbOYfHH/i0bJt6CSdTY6eri3QYWmr9kugb4byU2FXICmIhahGzlb3njb3W+p8oyNkGuC\n3oFu5iUog3sWzW/36ZZV4Bmyf9sp6FH4JTS2+ip6Y+1y90K3D/s4x0nA66lUNRUFqZ50/VZIWixC\nzcd5pu3Rue6+vOzrFo0VYE6euyca1rdqFpOUxhJU1ZiMnjAfQ/fkIdRL6lN7qVEqNx7pcrIa7qlI\nsncXrWmMlUouM5uLGpHz0HLQYTPLtn/LPkMWRE9Adc/haAjAUTb7CNK9KYWUqV6JasmDzWy1u68x\nsz1oHHAiMjQpm8nUmqAfR1k8acTxraKnO8rCCzAnz+6JVK670xrQt2oWl/vUj8xsJlqKWoAa9LPR\nzshoJL9RKBHo24PxwI0mJ6F/oPLNZjTn3ZGPXCaJ4f2o/nicS3RqBGp+taIunpVNtrv74vTaaFQe\nm07SqimjHpr4JDIWuRe9z75rZp+hJjv9xDH+bJHMQ2v5oL/37wD8/ZIjHUujpd4qPuSspuNzMXqq\nW5u+9KhJFbPQaZuMCPQVkrtBH0clhVEo85yPJFgX0v56P32xF8kGPASMM22GXkXS8KhjRLJZfmVa\n1HoRzV9n1oo9UP+CSx2ci3YCfgtHSw+zgaW5N3cr+B6SkvgnqnFfao2JogXFkH24vAx8Ie14/Bv1\noj5L/Y5l/SJq9BVjsu87gDK9K1B9fiTa+ru80yaQcnXQ6eiDaxYqX5yFauQ/dal7tkp2+QxqpiVn\nISP5QcBFXqKQl5mtRZn0CnffaWa/QKqRfy/rmv08V16m43I03txxciMDAZOw3kUoKZqOpoeWeD+2\nhOu+VgT6arD32/dtQx34q5FZ83p3/3NlB2wQq+merwTu9WMYOLSSVI8dih6N57j790u+3ifQ6N+F\naBTwRLQHsBo91TwYmXR3YjVv5nHI7+E8tNcwGGkfFS5JEaWb6piEzD3uAma57PsWANPcfVm1R2uK\nrBzTgxqOLSf3VDEeiZhtSaOZh8zsIGoQl1o+cgmVHRUrSyO0E1EmPR81gyPQdxG9JHeZN/NUtPS1\noYwgD5HRV4aVZN/XLpjZy6hE8jfSJiqw1Uu0ROzlDF9FMst70PJVpo2y3t1v621hKwjKwlrkzdzr\ntSPQV48VaN/XLqRZ5cnosfQcVL54x91nVXCWk5ES6DS0ZLOuU6eZgs6lyuQuAn2FJK0NckscFyON\n7f3AT8p6jGsVJnXSt7LFpFY1YHPXvwWpEW7PvXYK8uGNGz+ojFYndxHo24xUx/sS8MtOm3XONWI/\nD3wTNZx2oHXvN4E17t6ScdE0XbLe3aemD9RBaHT1VmQO3rFlsaBzqSq5i2Zsm+EF2fdVRJY1XIO2\nX9chXZfxaLRxCLQss5+EXIR0MH0ADUYSAEda/XQRBNAab+beiIw+KBwz+z1wg5esiPgBZxiL9IP2\nIkncCWjdfJS7XxuN2KCbiEAfFIrJMedp1GxahcbHepCcQ2kLSu85Q168aikSEnsdjbPd7+49LdzM\nDYLKiUAfFEoSy7oMlU5GoU3fcUhy+eoWnmMQ0lsfjkpH2/w9No1B0C1EoA8KJxdkhyLp1ZPQvbal\nhdIH1yL3ntdQrf4gWi/vWjezoHsZVPUBgoFBCu7Z2NhvkEfpme6+BzVpd0CpImL5s4xDRtzT0Zzy\nD5D/7jVlXzsI2pEI9EHR/BgF+rzR+51ohOzoeFkZ5H72JGTT9zZy7NmYzvC59H1x3wddRYxXBoWQ\na2we7+7PmtmbSAYX1Jjdlr6vtIw+97NfAXab2Q+B+5LmzUL0lAH1m4sHQUcTmU1QGKkR+7SZ3YSs\n0g6bjLHxkr1R0/WzZZQXgJuBDwN3oK3DLSirh5rwWhB0BdGMDQohN9J4MrAMuAB5xI4FHnD3Va1o\nxJrZLOBjyHy7B1kzDnP3A2VeNwjamQj0QdPkgvxJ7r4/vfYRNL++Ian0tSLILwCuBw6jBalTgf8B\ni919VZnXDoJ2Jmr0QRFkJuc3J9nVDciT9T+Am1mPu+8r9QBm04DrgNvd/eHc61cBN5jZc+6+ucwz\nBEG7EjX6oAiyTH038FfgL2ic8Uakvb3SzGaUfIY5yGz5YTMbZmZDAJJL1yPIuavUqZ8gaFciow+a\nJleS+Row090PJ+u+p5AM6/PAcjP7SokyCNOANek8h+BdDlJvICs/UHITGjdBVxEZfVAIaUlpL5q2\nIc2w9wAXu/tdwOSStW4uA243s1vN7MtmNoXak8ZYZNVG7rUg6Boiow8Kwd13m9mvgXVmthVtwh4G\nVpvZRKRJXybno+bvDDTx8y3gbDPbhSSSz82OWvI5gqDtiKmboFCSPPAcZN33ors/YGYzgQnuvqaC\n84ygJmoW8/NBVxKBPmgJYfQRBNURgT4olDTVchwauTwSwT0IqicCfVAYkbUHQXsSgT5omjSzPhNZ\n9U0ENiEnpxeqPFcQBCLGK4OGycn9XgnchkxG/oCcpa4zszFVnS0IghqR0QcNkxlsm9ndwL/cfaWZ\nDUfOUncBj7n7PeHPGgTVEnP0QTNkwXsPafPU3Q8CB5NJeChGBkEbEBl90DTJ2ONBJDWwA3nEvgrc\n5O6vVHm2IAgi0AcFYmZzgHEou19dsuRBEAT9JEo3QVOY2UjgHOB04Aiy8XsOGI604IMgqJgI9EFD\nZI1Y4IvAN4CtqFY/FBgBPAQ8ELP1QVA9EeiDRsmC92xgqbs/amYnIqXIM5GVXxAEbUAE+qBRskA/\nBJhqZk8lX9YDyIhb3xTZfBBUTgT6oFEys6ZDwGJgvpntRc5SO4Gfu/uRCs8XBEEipm6CpjCz04AT\nUMnmNCSBcLK7L436fBC0BxHog7oxs3nAQmB5Xs8mad4cD7yd2fkFQVA9EeiDujGzYWja5gzgRWAt\nsDXL3s1sNHJ16nH3fZUdNAgCIAJ90CBJ02Y+yuynpJffQcqVzyNLv/vc/U/VnDAIgowI9EEhmNkE\nVKc/Hdjn7k9WfKQgCBIR6INSiEZsELQPEeiDIAgGOGE8EgRBMMCJQB8EQTDAiUAfBEEwwIlAHwRB\nMMCJQB8EQTDA+T+DRvswE8gFbAAAAABJRU5ErkJggg==\n",
 709 |       "text/plain": [
 710 |        "<matplotlib.figure.Figure at 0x1117d9550>"
 711 |       ]
 712 |      },
 713 |      "metadata": {},
 714 |      "output_type": "display_data"
 715 |     }
 716 |    ],
 717 |    "source": [
 718 |     "# we can also plot a bar chart (with grid lines and slanted x axis labels for better readability)\n",
 719 |     "x = list(range(len(genre_dict)))\n",
 720 |     "plt.xticks(x, genre_dict.keys(), rotation=80)\n",
 721 |     "plt.bar(x, genre_dict.values())\n",
 722 |     "plt.grid()\n",
 723 |     "plt.plot()"
 724 |    ]
 725 |   },
 726 |   {
 727 |    "cell_type": "code",
 728 |    "execution_count": 18,
 729 |    "metadata": {
 730 |     "collapsed": false
 731 |    },
 732 |    "outputs": [
 733 |     {
 734 |      "data": {
 735 |       "text/html": [
 736 |        "<div>\n",
 737 |        "<table border=\"1\" class=\"dataframe\">\n",
 738 |        "  <thead>\n",
 739 |        "    <tr style=\"text-align: right;\">\n",
 740 |        "      <th></th>\n",
 741 |        "      <th>userId</th>\n",
 742 |        "      <th>movieId</th>\n",
 743 |        "      <th>rating</th>\n",
 744 |        "      <th>timestamp</th>\n",
 745 |        "    </tr>\n",
 746 |        "  </thead>\n",
 747 |        "  <tbody>\n",
 748 |        "    <tr>\n",
 749 |        "      <th>0</th>\n",
 750 |        "      <td>1</td>\n",
 751 |        "      <td>31</td>\n",
 752 |        "      <td>2.5</td>\n",
 753 |        "      <td>1260759144</td>\n",
 754 |        "    </tr>\n",
 755 |        "    <tr>\n",
 756 |        "      <th>1</th>\n",
 757 |        "      <td>1</td>\n",
 758 |        "      <td>1029</td>\n",
 759 |        "      <td>3.0</td>\n",
 760 |        "      <td>1260759179</td>\n",
 761 |        "    </tr>\n",
 762 |        "    <tr>\n",
 763 |        "      <th>2</th>\n",
 764 |        "      <td>1</td>\n",
 765 |        "      <td>1061</td>\n",
 766 |        "      <td>3.0</td>\n",
 767 |        "      <td>1260759182</td>\n",
 768 |        "    </tr>\n",
 769 |        "    <tr>\n",
 770 |        "      <th>3</th>\n",
 771 |        "      <td>1</td>\n",
 772 |        "      <td>1129</td>\n",
 773 |        "      <td>2.0</td>\n",
 774 |        "      <td>1260759185</td>\n",
 775 |        "    </tr>\n",
 776 |        "    <tr>\n",
 777 |        "      <th>4</th>\n",
 778 |        "      <td>1</td>\n",
 779 |        "      <td>1172</td>\n",
 780 |        "      <td>4.0</td>\n",
 781 |        "      <td>1260759205</td>\n",
 782 |        "    </tr>\n",
 783 |        "  </tbody>\n",
 784 |        "</table>\n",
 785 |        "</div>"
 786 |       ],
 787 |       "text/plain": [
 788 |        "   userId  movieId  rating   timestamp\n",
 789 |        "0       1       31     2.5  1260759144\n",
 790 |        "1       1     1029     3.0  1260759179\n",
 791 |        "2       1     1061     3.0  1260759182\n",
 792 |        "3       1     1129     2.0  1260759185\n",
 793 |        "4       1     1172     4.0  1260759205"
 794 |       ]
 795 |      },
 796 |      "execution_count": 18,
 797 |      "metadata": {},
 798 |      "output_type": "execute_result"
 799 |     }
 800 |    ],
 801 |    "source": [
 802 |     "# now lets move onto the movie ratings data\n",
 803 |     "ratings_path = './ml-latest-small/ratings.csv'\n",
 804 |     "ratings_df = pd.read_csv(ratings_path)\n",
 805 |     "\n",
 806 |     "# lets see how the data looks\n",
 807 |     "ratings_df.head()"
 808 |    ]
 809 |   },
 810 |   {
 811 |    "cell_type": "code",
 812 |    "execution_count": 22,
 813 |    "metadata": {
 814 |     "collapsed": false
 815 |    },
 816 |    "outputs": [
 817 |     {
 818 |      "name": "stdout",
 819 |      "output_type": "stream",
 820 |      "text": [
 821 |       "# ratings 100004\n",
 822 |       "# movies 9066\n",
 823 |       "rating values [ 2.5  3.   2.   4.   3.5  1.   5.   4.5  1.5  0.5]\n"
 824 |      ]
 825 |     }
 826 |    ],
 827 |    "source": [
 828 |     "# how many ratings do we have?\n",
 829 |     "print '# ratings', len(ratings_df.index)\n",
 830 |     "\n",
 831 |     "# how many different movies have been rated?\n",
 832 |     "print '# movies', len(ratings_df.movieId.unique())\n",
 833 |     "\n",
 834 |     "# what are the different ratings given by users?\n",
 835 |     "print 'rating values', ratings_df.rating.unique()\n"
 836 |    ]
 837 |   },
 838 |   {
 839 |    "cell_type": "code",
 840 |    "execution_count": 23,
 841 |    "metadata": {
 842 |     "collapsed": false
 843 |    },
 844 |    "outputs": [
 845 |     {
 846 |      "data": {
 847 |       "text/plain": [
 848 |        "3.5436082556697732"
 849 |       ]
 850 |      },
 851 |      "execution_count": 23,
 852 |      "metadata": {},
 853 |      "output_type": "execute_result"
 854 |     }
 855 |    ],
 856 |    "source": [
 857 |     "# now lets find the average movie rating across all ratings\n",
 858 |     "\n",
 859 |     "# introducing the numpy package which is used to find various stats out of data\n",
 860 |     "import numpy as np\n",
 861 |     "\n",
 862 |     "# lets find average rating using the numpy's mean method\n",
 863 |     "np.mean(ratings_df.rating)"
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "code",
 868 |    "execution_count": 24,
 869 |    "metadata": {
 870 |     "collapsed": false
 871 |    },
 872 |    "outputs": [
 873 |     {
 874 |      "data": {
 875 |       "text/plain": [
 876 |        "4.0"
 877 |       ]
 878 |      },
 879 |      "execution_count": 24,
 880 |      "metadata": {},
 881 |      "output_type": "execute_result"
 882 |     }
 883 |    ],
 884 |    "source": [
 885 |     "# lets find the median rating\n",
 886 |     "np.median(ratings_df.rating)"
 887 |    ]
 888 |   },
 889 |   {
 890 |    "cell_type": "code",
 891 |    "execution_count": 25,
 892 |    "metadata": {
 893 |     "collapsed": false
 894 |    },
 895 |    "outputs": [
 896 |     {
 897 |      "data": {
 898 |       "text/plain": [
 899 |        "3.0"
 900 |       ]
 901 |      },
 902 |      "execution_count": 25,
 903 |      "metadata": {},
 904 |      "output_type": "execute_result"
 905 |     }
 906 |    ],
 907 |    "source": [
 908 |     "# lets find the 30th percentile rating \n",
 909 |     "np.percentile(ratings_df.rating, 30)"
 910 |    ]
 911 |   },
 912 |   {
 913 |    "cell_type": "code",
 914 |    "execution_count": 26,
 915 |    "metadata": {
 916 |     "collapsed": false
 917 |    },
 918 |    "outputs": [
 919 |     {
 920 |      "data": {
 921 |       "text/plain": [
 922 |        "ModeResult(mode=array([ 4.]), count=array([28750]))"
 923 |       ]
 924 |      },
 925 |      "execution_count": 26,
 926 |      "metadata": {},
 927 |      "output_type": "execute_result"
 928 |     }
 929 |    ],
 930 |    "source": [
 931 |     "# lets find the most common rating given by users to movies (called mode of the data)\n",
 932 |     "from scipy import stats\n",
 933 |     "stats.mode(ratings_df.rating)"
 934 |    ]
 935 |   },
 936 |   {
 937 |    "cell_type": "code",
 938 |    "execution_count": 27,
 939 |    "metadata": {
 940 |     "collapsed": false
 941 |    },
 942 |    "outputs": [
 943 |     {
 944 |      "data": {
 945 |       "image/png": 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O4IpxVsnavxbyIGP/0nNN9J6ZtX+9oG4DSCsnFY5e55Ex1smZB/CWdEh7k6Tj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\nxMzM2uIBxMzM2uIBxMzM2vL/AeOXgkUZ6mE0AAAAAElFTkSuQmCC\n",
 946 |       "text/plain": [
 947 |        "<matplotlib.figure.Figure at 0x11191fe10>"
 948 |       ]
 949 |      },
 950 |      "metadata": {},
 951 |      "output_type": "display_data"
 952 |     }
 953 |    ],
 954 |    "source": [
 955 |     "# now lets plot a histogram of movie ratings to get an overall picture\n",
 956 |     "plt.hist(ratings_df.rating)\n",
 957 |     "plt.xticks([0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])\n",
 958 |     "plt.xlabel('Rating')\n",
 959 |     "plt.ylabel('# of movies')\n",
 960 |     "plt.grid()\n",
 961 |     "plt.show()"
 962 |    ]
 963 |   },
 964 |   {
 965 |    "cell_type": "code",
 966 |    "execution_count": 28,
 967 |    "metadata": {
 968 |     "collapsed": false
 969 |    },
 970 |    "outputs": [
 971 |     {
 972 |      "data": {
 973 |       "text/html": [
 974 |        "<div>\n",
 975 |        "<table border=\"1\" class=\"dataframe\">\n",
 976 |        "  <thead>\n",
 977 |        "    <tr style=\"text-align: right;\">\n",
 978 |        "      <th></th>\n",
 979 |        "      <th>userId</th>\n",
 980 |        "      <th>movieId</th>\n",
 981 |        "      <th>rating</th>\n",
 982 |        "      <th>timestamp</th>\n",
 983 |        "    </tr>\n",
 984 |        "  </thead>\n",
 985 |        "  <tbody>\n",
 986 |        "    <tr>\n",
 987 |        "      <th>0</th>\n",
 988 |        "      <td>1</td>\n",
 989 |        "      <td>31</td>\n",
 990 |        "      <td>2.5</td>\n",
 991 |        "      <td>1260759144</td>\n",
 992 |        "    </tr>\n",
 993 |        "    <tr>\n",
 994 |        "      <th>1</th>\n",
 995 |        "      <td>1</td>\n",
 996 |        "      <td>1029</td>\n",
 997 |        "      <td>3.0</td>\n",
 998 |        "      <td>1260759179</td>\n",
 999 |        "    </tr>\n",
1000 |        "    <tr>\n",
1001 |        "      <th>2</th>\n",
1002 |        "      <td>1</td>\n",
1003 |        "      <td>1061</td>\n",
1004 |        "      <td>3.0</td>\n",
1005 |        "      <td>1260759182</td>\n",
1006 |        "    </tr>\n",
1007 |        "    <tr>\n",
1008 |        "      <th>3</th>\n",
1009 |        "      <td>1</td>\n",
1010 |        "      <td>1129</td>\n",
1011 |        "      <td>2.0</td>\n",
1012 |        "      <td>1260759185</td>\n",
1013 |        "    </tr>\n",
1014 |        "    <tr>\n",
1015 |        "      <th>4</th>\n",
1016 |        "      <td>1</td>\n",
1017 |        "      <td>1172</td>\n",
1018 |        "      <td>4.0</td>\n",
1019 |        "      <td>1260759205</td>\n",
1020 |        "    </tr>\n",
1021 |        "  </tbody>\n",
1022 |        "</table>\n",
1023 |        "</div>"
1024 |       ],
1025 |       "text/plain": [
1026 |        "   userId  movieId  rating   timestamp\n",
1027 |        "0       1       31     2.5  1260759144\n",
1028 |        "1       1     1029     3.0  1260759179\n",
1029 |        "2       1     1061     3.0  1260759182\n",
1030 |        "3       1     1129     2.0  1260759185\n",
1031 |        "4       1     1172     4.0  1260759205"
1032 |       ]
1033 |      },
1034 |      "execution_count": 28,
1035 |      "metadata": {},
1036 |      "output_type": "execute_result"
1037 |     }
1038 |    ],
1039 |    "source": [
1040 |     "# now lets find the highest rated movies (highest average rating)\n",
1041 |     "ratings_df.head()"
1042 |    ]
1043 |   },
1044 |   {
1045 |    "cell_type": "code",
1046 |    "execution_count": 29,
1047 |    "metadata": {
1048 |     "collapsed": false
1049 |    },
1050 |    "outputs": [
1051 |     {
1052 |      "data": {
1053 |       "text/html": [
1054 |        "<div>\n",
1055 |        "<table border=\"1\" class=\"dataframe\">\n",
1056 |        "  <thead>\n",
1057 |        "    <tr style=\"text-align: right;\">\n",
1058 |        "      <th></th>\n",
1059 |        "      <th>movieId</th>\n",
1060 |        "      <th>rating</th>\n",
1061 |        "    </tr>\n",
1062 |        "  </thead>\n",
1063 |        "  <tbody>\n",
1064 |        "    <tr>\n",
1065 |        "      <th>9065</th>\n",
1066 |        "      <td>163949</td>\n",
1067 |        "      <td>5.0</td>\n",
1068 |        "    </tr>\n",
1069 |        "    <tr>\n",
1070 |        "      <th>7297</th>\n",
1071 |        "      <td>71180</td>\n",
1072 |        "      <td>5.0</td>\n",
1073 |        "    </tr>\n",
1074 |        "    <tr>\n",
1075 |        "      <th>6629</th>\n",
1076 |        "      <td>51471</td>\n",
1077 |        "      <td>5.0</td>\n",
1078 |        "    </tr>\n",
1079 |        "    <tr>\n",
1080 |        "      <th>6662</th>\n",
1081 |        "      <td>52617</td>\n",
1082 |        "      <td>5.0</td>\n",
1083 |        "    </tr>\n",
1084 |        "    <tr>\n",
1085 |        "      <th>6704</th>\n",
1086 |        "      <td>53887</td>\n",
1087 |        "      <td>5.0</td>\n",
1088 |        "    </tr>\n",
1089 |        "    <tr>\n",
1090 |        "      <th>6717</th>\n",
1091 |        "      <td>54251</td>\n",
1092 |        "      <td>5.0</td>\n",
1093 |        "    </tr>\n",
1094 |        "    <tr>\n",
1095 |        "      <th>6726</th>\n",
1096 |        "      <td>54328</td>\n",
1097 |        "      <td>5.0</td>\n",
1098 |        "    </tr>\n",
1099 |        "    <tr>\n",
1100 |        "      <th>6785</th>\n",
1101 |        "      <td>55555</td>\n",
1102 |        "      <td>5.0</td>\n",
1103 |        "    </tr>\n",
1104 |        "    <tr>\n",
1105 |        "      <th>6836</th>\n",
1106 |        "      <td>56869</td>\n",
1107 |        "      <td>5.0</td>\n",
1108 |        "    </tr>\n",
1109 |        "    <tr>\n",
1110 |        "      <th>6843</th>\n",
1111 |        "      <td>57038</td>\n",
1112 |        "      <td>5.0</td>\n",
1113 |        "    </tr>\n",
1114 |        "  </tbody>\n",
1115 |        "</table>\n",
1116 |        "</div>"
1117 |       ],
1118 |       "text/plain": [
1119 |        "      movieId  rating\n",
1120 |        "9065   163949     5.0\n",
1121 |        "7297    71180     5.0\n",
1122 |        "6629    51471     5.0\n",
1123 |        "6662    52617     5.0\n",
1124 |        "6704    53887     5.0\n",
1125 |        "6717    54251     5.0\n",
1126 |        "6726    54328     5.0\n",
1127 |        "6785    55555     5.0\n",
1128 |        "6836    56869     5.0\n",
1129 |        "6843    57038     5.0"
1130 |       ]
1131 |      },
1132 |      "execution_count": 29,
1133 |      "metadata": {},
1134 |      "output_type": "execute_result"
1135 |     }
1136 |    ],
1137 |    "source": [
1138 |     "ratings_view = ratings_df[['movieId', 'rating']]\n",
1139 |     "ratings_view.groupby(['movieId'], as_index=False).mean().sort_values(by='rating', ascending=False).head(10)"
1140 |    ]
1141 |   },
1142 |   {
1143 |    "cell_type": "code",
1144 |    "execution_count": 30,
1145 |    "metadata": {
1146 |     "collapsed": false
1147 |    },
1148 |    "outputs": [
1149 |     {
1150 |      "data": {
1151 |       "text/html": [
1152 |        "<div>\n",
1153 |        "<table border=\"1\" class=\"dataframe\">\n",
1154 |        "  <thead>\n",
1155 |        "    <tr style=\"text-align: right;\">\n",
1156 |        "      <th></th>\n",
1157 |        "      <th>userId</th>\n",
1158 |        "      <th>movieId</th>\n",
1159 |        "      <th>rating</th>\n",
1160 |        "      <th>timestamp</th>\n",
1161 |        "      <th>title</th>\n",
1162 |        "      <th>genres</th>\n",
1163 |        "      <th>genres_arr</th>\n",
1164 |        "      <th>genre_count</th>\n",
1165 |        "    </tr>\n",
1166 |        "  </thead>\n",
1167 |        "  <tbody>\n",
1168 |        "    <tr>\n",
1169 |        "      <th>0</th>\n",
1170 |        "      <td>1</td>\n",
1171 |        "      <td>31</td>\n",
1172 |        "      <td>2.5</td>\n",
1173 |        "      <td>1260759144</td>\n",
1174 |        "      <td>Dangerous Minds (1995)</td>\n",
1175 |        "      <td>Drama</td>\n",
1176 |        "      <td>[Drama]</td>\n",
1177 |        "      <td>1</td>\n",
1178 |        "    </tr>\n",
1179 |        "    <tr>\n",
1180 |        "      <th>1</th>\n",
1181 |        "      <td>7</td>\n",
1182 |        "      <td>31</td>\n",
1183 |        "      <td>3.0</td>\n",
1184 |        "      <td>851868750</td>\n",
1185 |        "      <td>Dangerous Minds (1995)</td>\n",
1186 |        "      <td>Drama</td>\n",
1187 |        "      <td>[Drama]</td>\n",
1188 |        "      <td>1</td>\n",
1189 |        "    </tr>\n",
1190 |        "    <tr>\n",
1191 |        "      <th>2</th>\n",
1192 |        "      <td>31</td>\n",
1193 |        "      <td>31</td>\n",
1194 |        "      <td>4.0</td>\n",
1195 |        "      <td>1273541953</td>\n",
1196 |        "      <td>Dangerous Minds (1995)</td>\n",
1197 |        "      <td>Drama</td>\n",
1198 |        "      <td>[Drama]</td>\n",
1199 |        "      <td>1</td>\n",
1200 |        "    </tr>\n",
1201 |        "    <tr>\n",
1202 |        "      <th>3</th>\n",
1203 |        "      <td>32</td>\n",
1204 |        "      <td>31</td>\n",
1205 |        "      <td>4.0</td>\n",
1206 |        "      <td>834828440</td>\n",
1207 |        "      <td>Dangerous Minds (1995)</td>\n",
1208 |        "      <td>Drama</td>\n",
1209 |        "      <td>[Drama]</td>\n",
1210 |        "      <td>1</td>\n",
1211 |        "    </tr>\n",
1212 |        "    <tr>\n",
1213 |        "      <th>4</th>\n",
1214 |        "      <td>36</td>\n",
1215 |        "      <td>31</td>\n",
1216 |        "      <td>3.0</td>\n",
1217 |        "      <td>847057202</td>\n",
1218 |        "      <td>Dangerous Minds (1995)</td>\n",
1219 |        "      <td>Drama</td>\n",
1220 |        "      <td>[Drama]</td>\n",
1221 |        "      <td>1</td>\n",
1222 |        "    </tr>\n",
1223 |        "  </tbody>\n",
1224 |        "</table>\n",
1225 |        "</div>"
1226 |       ],
1227 |       "text/plain": [
1228 |        "   userId  movieId  rating   timestamp                   title genres  \\\n",
1229 |        "0       1       31     2.5  1260759144  Dangerous Minds (1995)  Drama   \n",
1230 |        "1       7       31     3.0   851868750  Dangerous Minds (1995)  Drama   \n",
1231 |        "2      31       31     4.0  1273541953  Dangerous Minds (1995)  Drama   \n",
1232 |        "3      32       31     4.0   834828440  Dangerous Minds (1995)  Drama   \n",
1233 |        "4      36       31     3.0   847057202  Dangerous Minds (1995)  Drama   \n",
1234 |        "\n",
1235 |        "  genres_arr  genre_count  \n",
1236 |        "0    [Drama]            1  \n",
1237 |        "1    [Drama]            1  \n",
1238 |        "2    [Drama]            1  \n",
1239 |        "3    [Drama]            1  \n",
1240 |        "4    [Drama]            1  "
1241 |       ]
1242 |      },
1243 |      "execution_count": 30,
1244 |      "metadata": {},
1245 |      "output_type": "execute_result"
1246 |     }
1247 |    ],
1248 |    "source": [
1249 |     "# now lets merge/join the movies_df and ratings_df so that we can see the actual movie titles of top 10 movies\n",
1250 |     "merged_df = pd.merge(ratings_df, movies_df, on='movieId')\n",
1251 |     "merged_df.head()"
1252 |    ]
1253 |   },
1254 |   {
1255 |    "cell_type": "code",
1256 |    "execution_count": 31,
1257 |    "metadata": {
1258 |     "collapsed": false
1259 |    },
1260 |    "outputs": [
1261 |     {
1262 |      "data": {
1263 |       "text/html": [
1264 |        "<div>\n",
1265 |        "<table border=\"1\" class=\"dataframe\">\n",
1266 |        "  <thead>\n",
1267 |        "    <tr style=\"text-align: right;\">\n",
1268 |        "      <th></th>\n",
1269 |        "      <th>movieId</th>\n",
1270 |        "      <th>title</th>\n",
1271 |        "      <th>rating</th>\n",
1272 |        "    </tr>\n",
1273 |        "  </thead>\n",
1274 |        "  <tbody>\n",
1275 |        "    <tr>\n",
1276 |        "      <th>9065</th>\n",
1277 |        "      <td>163949</td>\n",
1278 |        "      <td>The Beatles: Eight Days a Week - The Touring Y...</td>\n",
1279 |        "      <td>5.0</td>\n",
1280 |        "    </tr>\n",
1281 |        "    <tr>\n",
1282 |        "      <th>7297</th>\n",
1283 |        "      <td>71180</td>\n",
1284 |        "      <td>Padre padrone (1977)</td>\n",
1285 |        "      <td>5.0</td>\n",
1286 |        "    </tr>\n",
1287 |        "    <tr>\n",
1288 |        "      <th>6629</th>\n",
1289 |        "      <td>51471</td>\n",
1290 |        "      <td>Amazing Grace (2006)</td>\n",
1291 |        "      <td>5.0</td>\n",
1292 |        "    </tr>\n",
1293 |        "    <tr>\n",
1294 |        "      <th>6662</th>\n",
1295 |        "      <td>52617</td>\n",
1296 |        "      <td>Woman on the Beach (Haebyeonui yeoin) (2006)</td>\n",
1297 |        "      <td>5.0</td>\n",
1298 |        "    </tr>\n",
1299 |        "    <tr>\n",
1300 |        "      <th>6704</th>\n",
1301 |        "      <td>53887</td>\n",
1302 |        "      <td>O Lucky Man! (1973)</td>\n",
1303 |        "      <td>5.0</td>\n",
1304 |        "    </tr>\n",
1305 |        "    <tr>\n",
1306 |        "      <th>6717</th>\n",
1307 |        "      <td>54251</td>\n",
1308 |        "      <td>Dorian Blues (2004)</td>\n",
1309 |        "      <td>5.0</td>\n",
1310 |        "    </tr>\n",
1311 |        "    <tr>\n",
1312 |        "      <th>6726</th>\n",
1313 |        "      <td>54328</td>\n",
1314 |        "      <td>My Best Friend (Mon meilleur ami) (2006)</td>\n",
1315 |        "      <td>5.0</td>\n",
1316 |        "    </tr>\n",
1317 |        "    <tr>\n",
1318 |        "      <th>6785</th>\n",
1319 |        "      <td>55555</td>\n",
1320 |        "      <td>Edge of Heaven, The (Auf der anderen Seite) (2...</td>\n",
1321 |        "      <td>5.0</td>\n",
1322 |        "    </tr>\n",
1323 |        "    <tr>\n",
1324 |        "      <th>6836</th>\n",
1325 |        "      <td>56869</td>\n",
1326 |        "      <td>Drained (O cheiro do Ralo) (2006)</td>\n",
1327 |        "      <td>5.0</td>\n",
1328 |        "    </tr>\n",
1329 |        "    <tr>\n",
1330 |        "      <th>6843</th>\n",
1331 |        "      <td>57038</td>\n",
1332 |        "      <td>To the Left of the Father (Lavoura Arcaica) (2...</td>\n",
1333 |        "      <td>5.0</td>\n",
1334 |        "    </tr>\n",
1335 |        "  </tbody>\n",
1336 |        "</table>\n",
1337 |        "</div>"
1338 |       ],
1339 |       "text/plain": [
1340 |        "      movieId                                              title  rating\n",
1341 |        "9065   163949  The Beatles: Eight Days a Week - The Touring Y...     5.0\n",
1342 |        "7297    71180                               Padre padrone (1977)     5.0\n",
1343 |        "6629    51471                               Amazing Grace (2006)     5.0\n",
1344 |        "6662    52617       Woman on the Beach (Haebyeonui yeoin) (2006)     5.0\n",
1345 |        "6704    53887                                O Lucky Man! (1973)     5.0\n",
1346 |        "6717    54251                                Dorian Blues (2004)     5.0\n",
1347 |        "6726    54328           My Best Friend (Mon meilleur ami) (2006)     5.0\n",
1348 |        "6785    55555  Edge of Heaven, The (Auf der anderen Seite) (2...     5.0\n",
1349 |        "6836    56869                  Drained (O cheiro do Ralo) (2006)     5.0\n",
1350 |        "6843    57038  To the Left of the Father (Lavoura Arcaica) (2...     5.0"
1351 |       ]
1352 |      },
1353 |      "execution_count": 31,
1354 |      "metadata": {},
1355 |      "output_type": "execute_result"
1356 |     }
1357 |    ],
1358 |    "source": [
1359 |     "# now lets find the titles of the top 10 movies to see if we are missing on some awesome movies!\n",
1360 |     "titles_df = merged_df[['movieId', 'title', 'rating']]\n",
1361 |     "titles_df.groupby(['movieId', 'title'], as_index=False).mean().sort_values(by='rating', ascending=False).head(10)"
1362 |    ]
1363 |   },
1364 |   {
1365 |    "cell_type": "code",
1366 |    "execution_count": 32,
1367 |    "metadata": {
1368 |     "collapsed": false
1369 |    },
1370 |    "outputs": [
1371 |     {
1372 |      "data": {
1373 |       "text/plain": [
1374 |        "1"
1375 |       ]
1376 |      },
1377 |      "execution_count": 32,
1378 |      "metadata": {},
1379 |      "output_type": "execute_result"
1380 |     }
1381 |    ],
1382 |    "source": [
1383 |     "# these movies are not what we expected to be in the Top 10 movies list, something's wrong here\n",
1384 |     "# lets check how many ratings have these movies received, lets take an example of movieId 163949\n",
1385 |     "len(merged_df[merged_df['movieId'] == 163949].index)"
1386 |    ]
1387 |   },
1388 |   {
1389 |    "cell_type": "code",
1390 |    "execution_count": 33,
1391 |    "metadata": {
1392 |     "collapsed": false
1393 |    },
1394 |    "outputs": [
1395 |     {
1396 |      "data": {
1397 |       "text/html": [
1398 |        "<div>\n",
1399 |        "<table border=\"1\" class=\"dataframe\">\n",
1400 |        "  <thead>\n",
1401 |        "    <tr style=\"text-align: right;\">\n",
1402 |        "      <th></th>\n",
1403 |        "      <th>movieId</th>\n",
1404 |        "      <th>title</th>\n",
1405 |        "      <th>rating</th>\n",
1406 |        "    </tr>\n",
1407 |        "  </thead>\n",
1408 |        "  <tbody>\n",
1409 |        "    <tr>\n",
1410 |        "      <th>321</th>\n",
1411 |        "      <td>356</td>\n",
1412 |        "      <td>Forrest Gump (1994)</td>\n",
1413 |        "      <td>341</td>\n",
1414 |        "    </tr>\n",
1415 |        "    <tr>\n",
1416 |        "      <th>266</th>\n",
1417 |        "      <td>296</td>\n",
1418 |        "      <td>Pulp Fiction (1994)</td>\n",
1419 |        "      <td>324</td>\n",
1420 |        "    </tr>\n",
1421 |        "    <tr>\n",
1422 |        "      <th>284</th>\n",
1423 |        "      <td>318</td>\n",
1424 |        "      <td>Shawshank Redemption, The (1994)</td>\n",
1425 |        "      <td>311</td>\n",
1426 |        "    </tr>\n",
1427 |        "    <tr>\n",
1428 |        "      <th>525</th>\n",
1429 |        "      <td>593</td>\n",
1430 |        "      <td>Silence of the Lambs, The (1991)</td>\n",
1431 |        "      <td>304</td>\n",
1432 |        "    </tr>\n",
1433 |        "    <tr>\n",
1434 |        "      <th>232</th>\n",
1435 |        "      <td>260</td>\n",
1436 |        "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
1437 |        "      <td>291</td>\n",
1438 |        "    </tr>\n",
1439 |        "  </tbody>\n",
1440 |        "</table>\n",
1441 |        "</div>"
1442 |       ],
1443 |       "text/plain": [
1444 |        "     movieId                                      title  rating\n",
1445 |        "321      356                        Forrest Gump (1994)     341\n",
1446 |        "266      296                        Pulp Fiction (1994)     324\n",
1447 |        "284      318           Shawshank Redemption, The (1994)     311\n",
1448 |        "525      593           Silence of the Lambs, The (1991)     304\n",
1449 |        "232      260  Star Wars: Episode IV - A New Hope (1977)     291"
1450 |       ]
1451 |      },
1452 |      "execution_count": 33,
1453 |      "metadata": {},
1454 |      "output_type": "execute_result"
1455 |     }
1456 |    ],
1457 |    "source": [
1458 |     "# now lets only consider movies which have atleast 100 ratings and see how the top 10 movies change\n",
1459 |     "temp_df = titles_df.groupby(['movieId', 'title'], as_index=False).count()\n",
1460 |     "well_rated_df = temp_df[temp_df['rating'] > 100].sort_values(by='rating', ascending=False)\n",
1461 |     "well_rated_df.head()"
1462 |    ]
1463 |   },
1464 |   {
1465 |    "cell_type": "code",
1466 |    "execution_count": 34,
1467 |    "metadata": {
1468 |     "collapsed": false
1469 |    },
1470 |    "outputs": [
1471 |     {
1472 |      "data": {
1473 |       "text/html": [
1474 |        "<div>\n",
1475 |        "<table border=\"1\" class=\"dataframe\">\n",
1476 |        "  <thead>\n",
1477 |        "    <tr style=\"text-align: right;\">\n",
1478 |        "      <th></th>\n",
1479 |        "      <th>movieId</th>\n",
1480 |        "      <th>title</th>\n",
1481 |        "      <th>rating</th>\n",
1482 |        "    </tr>\n",
1483 |        "  </thead>\n",
1484 |        "  <tbody>\n",
1485 |        "    <tr>\n",
1486 |        "      <th>62</th>\n",
1487 |        "      <td>858</td>\n",
1488 |        "      <td>Godfather, The (1972)</td>\n",
1489 |        "      <td>4.487500</td>\n",
1490 |        "    </tr>\n",
1491 |        "    <tr>\n",
1492 |        "      <th>27</th>\n",
1493 |        "      <td>318</td>\n",
1494 |        "      <td>Shawshank Redemption, The (1994)</td>\n",
1495 |        "      <td>4.487138</td>\n",
1496 |        "    </tr>\n",
1497 |        "    <tr>\n",
1498 |        "      <th>81</th>\n",
1499 |        "      <td>1221</td>\n",
1500 |        "      <td>Godfather: Part II, The (1974)</td>\n",
1501 |        "      <td>4.385185</td>\n",
1502 |        "    </tr>\n",
1503 |        "    <tr>\n",
1504 |        "      <th>10</th>\n",
1505 |        "      <td>50</td>\n",
1506 |        "      <td>Usual Suspects, The (1995)</td>\n",
1507 |        "      <td>4.370647</td>\n",
1508 |        "    </tr>\n",
1509 |        "    <tr>\n",
1510 |        "      <th>43</th>\n",
1511 |        "      <td>527</td>\n",
1512 |        "      <td>Schindler's List (1993)</td>\n",
1513 |        "      <td>4.303279</td>\n",
1514 |        "    </tr>\n",
1515 |        "    <tr>\n",
1516 |        "      <th>71</th>\n",
1517 |        "      <td>1193</td>\n",
1518 |        "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
1519 |        "      <td>4.256944</td>\n",
1520 |        "    </tr>\n",
1521 |        "    <tr>\n",
1522 |        "      <th>55</th>\n",
1523 |        "      <td>608</td>\n",
1524 |        "      <td>Fargo (1996)</td>\n",
1525 |        "      <td>4.256696</td>\n",
1526 |        "    </tr>\n",
1527 |        "    <tr>\n",
1528 |        "      <th>25</th>\n",
1529 |        "      <td>296</td>\n",
1530 |        "      <td>Pulp Fiction (1994)</td>\n",
1531 |        "      <td>4.256173</td>\n",
1532 |        "    </tr>\n",
1533 |        "    <tr>\n",
1534 |        "      <th>117</th>\n",
1535 |        "      <td>2858</td>\n",
1536 |        "      <td>American Beauty (1999)</td>\n",
1537 |        "      <td>4.236364</td>\n",
1538 |        "    </tr>\n",
1539 |        "    <tr>\n",
1540 |        "      <th>147</th>\n",
1541 |        "      <td>58559</td>\n",
1542 |        "      <td>Dark Knight, The (2008)</td>\n",
1543 |        "      <td>4.235537</td>\n",
1544 |        "    </tr>\n",
1545 |        "    <tr>\n",
1546 |        "      <th>63</th>\n",
1547 |        "      <td>912</td>\n",
1548 |        "      <td>Casablanca (1942)</td>\n",
1549 |        "      <td>4.235043</td>\n",
1550 |        "    </tr>\n",
1551 |        "    <tr>\n",
1552 |        "      <th>72</th>\n",
1553 |        "      <td>1196</td>\n",
1554 |        "      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n",
1555 |        "      <td>4.232906</td>\n",
1556 |        "    </tr>\n",
1557 |        "    <tr>\n",
1558 |        "      <th>128</th>\n",
1559 |        "      <td>4226</td>\n",
1560 |        "      <td>Memento (2000)</td>\n",
1561 |        "      <td>4.227273</td>\n",
1562 |        "    </tr>\n",
1563 |        "    <tr>\n",
1564 |        "      <th>12</th>\n",
1565 |        "      <td>111</td>\n",
1566 |        "      <td>Taxi Driver (1976)</td>\n",
1567 |        "      <td>4.224576</td>\n",
1568 |        "    </tr>\n",
1569 |        "    <tr>\n",
1570 |        "      <th>70</th>\n",
1571 |        "      <td>1136</td>\n",
1572 |        "      <td>Monty Python and the Holy Grail (1975)</td>\n",
1573 |        "      <td>4.224138</td>\n",
1574 |        "    </tr>\n",
1575 |        "    <tr>\n",
1576 |        "      <th>21</th>\n",
1577 |        "      <td>260</td>\n",
1578 |        "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
1579 |        "      <td>4.221649</td>\n",
1580 |        "    </tr>\n",
1581 |        "    <tr>\n",
1582 |        "      <th>59</th>\n",
1583 |        "      <td>750</td>\n",
1584 |        "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
1585 |        "      <td>4.209524</td>\n",
1586 |        "    </tr>\n",
1587 |        "    <tr>\n",
1588 |        "      <th>73</th>\n",
1589 |        "      <td>1197</td>\n",
1590 |        "      <td>Princess Bride, The (1987)</td>\n",
1591 |        "      <td>4.208589</td>\n",
1592 |        "    </tr>\n",
1593 |        "    <tr>\n",
1594 |        "      <th>79</th>\n",
1595 |        "      <td>1213</td>\n",
1596 |        "      <td>Goodfellas (1990)</td>\n",
1597 |        "      <td>4.202290</td>\n",
1598 |        "    </tr>\n",
1599 |        "    <tr>\n",
1600 |        "      <th>74</th>\n",
1601 |        "      <td>1198</td>\n",
1602 |        "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
1603 |        "      <td>4.193182</td>\n",
1604 |        "    </tr>\n",
1605 |        "  </tbody>\n",
1606 |        "</table>\n",
1607 |        "</div>"
1608 |       ],
1609 |       "text/plain": [
1610 |        "     movieId                                              title    rating\n",
1611 |        "62       858                              Godfather, The (1972)  4.487500\n",
1612 |        "27       318                   Shawshank Redemption, The (1994)  4.487138\n",
1613 |        "81      1221                     Godfather: Part II, The (1974)  4.385185\n",
1614 |        "10        50                         Usual Suspects, The (1995)  4.370647\n",
1615 |        "43       527                            Schindler's List (1993)  4.303279\n",
1616 |        "71      1193             One Flew Over the Cuckoo's Nest (1975)  4.256944\n",
1617 |        "55       608                                       Fargo (1996)  4.256696\n",
1618 |        "25       296                                Pulp Fiction (1994)  4.256173\n",
1619 |        "117     2858                             American Beauty (1999)  4.236364\n",
1620 |        "147    58559                            Dark Knight, The (2008)  4.235537\n",
1621 |        "63       912                                  Casablanca (1942)  4.235043\n",
1622 |        "72      1196  Star Wars: Episode V - The Empire Strikes Back...  4.232906\n",
1623 |        "128     4226                                     Memento (2000)  4.227273\n",
1624 |        "12       111                                 Taxi Driver (1976)  4.224576\n",
1625 |        "70      1136             Monty Python and the Holy Grail (1975)  4.224138\n",
1626 |        "21       260          Star Wars: Episode IV - A New Hope (1977)  4.221649\n",
1627 |        "59       750  Dr. Strangelove or: How I Learned to Stop Worr...  4.209524\n",
1628 |        "73      1197                         Princess Bride, The (1987)  4.208589\n",
1629 |        "79      1213                                  Goodfellas (1990)  4.202290\n",
1630 |        "74      1198  Raiders of the Lost Ark (Indiana Jones and the...  4.193182"
1631 |       ]
1632 |      },
1633 |      "execution_count": 34,
1634 |      "metadata": {},
1635 |      "output_type": "execute_result"
1636 |     }
1637 |    ],
1638 |    "source": [
1639 |     "# now lets created a filtered df from merged_df which only has these movies and then find top 20 movies\n",
1640 |     "filtered_df = merged_df[merged_df['movieId'].apply(lambda x: x in list(well_rated_df['movieId']))]\n",
1641 |     "titles_df = filtered_df[['title', 'rating', 'movieId']]\n",
1642 |     "titles_df.groupby(['movieId', 'title'], as_index=False).mean().sort_values(by='rating', ascending=False).head(20)"
1643 |    ]
1644 |   },
1645 |   {
1646 |    "cell_type": "code",
1647 |    "execution_count": null,
1648 |    "metadata": {
1649 |     "collapsed": false
1650 |    },
1651 |    "outputs": [],
1652 |    "source": [
1653 |     "# Great, so far we have seen how to extract various stats from our datasets using NumPy, Pandas \n",
1654 |     "# and visualize our data using Matplotlib, now lets do some predictive analytics..\n"
1655 |    ]
1656 |   },
1657 |   {
1658 |    "cell_type": "code",
1659 |    "execution_count": null,
1660 |    "metadata": {
1661 |     "collapsed": false
1662 |    },
1663 |    "outputs": [],
1664 |    "source": [
1665 |     "# now lets add a column called rating_year which depicts the year when the rating was given\n",
1666 |     "import datetime\n",
1667 |     "year_lambda = lambda x: int(datetime.datetime.fromtimestamp(x).strftime('%Y'))\n",
1668 |     "merged_df['rating_year'] = merged_df['timestamp'].apply(year_lambda)\n",
1669 |     "merged_df.head()"
1670 |    ]
1671 |   },
1672 |   {
1673 |    "cell_type": "code",
1674 |    "execution_count": null,
1675 |    "metadata": {
1676 |     "collapsed": false
1677 |    },
1678 |    "outputs": [],
1679 |    "source": [
1680 |     "# now lets create a new data frame which contains number of ratings given on each year\n",
1681 |     "ratings_per_year = merged_df.groupby(['rating_year'])['rating_year'].count()\n",
1682 |     "ratings_per_year.head()"
1683 |    ]
1684 |   },
1685 |   {
1686 |    "cell_type": "code",
1687 |    "execution_count": null,
1688 |    "metadata": {
1689 |     "collapsed": false
1690 |    },
1691 |    "outputs": [],
1692 |    "source": [
1693 |     "# now lets get some stats on number of ratings per year\n",
1694 |     "\n",
1695 |     "years = ratings_per_year.keys()\n",
1696 |     "num_ratings = ratings_per_year.get_values()\n",
1697 |     "print 'average ratings per year', np.mean(num_ratings)\n",
1698 |     "print 'median ratings per year', np.median(num_ratings)\n",
1699 |     "print '90% ratings per year', np.percentile(num_ratings, 90)"
1700 |    ]
1701 |   },
1702 |   {
1703 |    "cell_type": "code",
1704 |    "execution_count": null,
1705 |    "metadata": {
1706 |     "collapsed": false
1707 |    },
1708 |    "outputs": [],
1709 |    "source": [
1710 |     "# now lets scatter plot this data to visualize how ratings are spead across years\n",
1711 |     "plt.scatter(years, num_ratings)\n",
1712 |     "plt.title('# of rating across years')\n",
1713 |     "plt.xlabel('Year')\n",
1714 |     "plt.ylabel('# of ratings')\n",
1715 |     "plt.show()"
1716 |    ]
1717 |   },
1718 |   {
1719 |    "cell_type": "code",
1720 |    "execution_count": null,
1721 |    "metadata": {
1722 |     "collapsed": false
1723 |    },
1724 |    "outputs": [],
1725 |    "source": [
1726 |     "# now lets try to build a linear regression model using which we will predict how many ratings we get each year\n",
1727 |     "from scipy import stats\n",
1728 |     "slope, intercept, r_value, p_value, std_err = stats.linregress(years, num_ratings)\n",
1729 |     "print 'Generated linear model is  y = ' + str(slope) + ' * x + ' + str(intercept)"
1730 |    ]
1731 |   },
1732 |   {
1733 |    "cell_type": "code",
1734 |    "execution_count": null,
1735 |    "metadata": {
1736 |     "collapsed": false
1737 |    },
1738 |    "outputs": [],
1739 |    "source": [
1740 |     "# now lets use the slope and intercept to create a predict function which will predict num_ratings given a year\n",
1741 |     "def predict_num_ratings(year):\n",
1742 |     "    return slope * year + intercept\n",
1743 |     "\n",
1744 |     "predicted_ratings = predict_num_ratings(years)"
1745 |    ]
1746 |   },
1747 |   {
1748 |    "cell_type": "code",
1749 |    "execution_count": null,
1750 |    "metadata": {
1751 |     "collapsed": false
1752 |    },
1753 |    "outputs": [],
1754 |    "source": [
1755 |     "# now lets plot our predicted values along side the actual data to see how well we did\n",
1756 |     "plt.scatter(years, num_ratings)\n",
1757 |     "plt.plot(years, predicted_ratings, c='r')\n",
1758 |     "plt.show()"
1759 |    ]
1760 |   },
1761 |   {
1762 |    "cell_type": "code",
1763 |    "execution_count": null,
1764 |    "metadata": {
1765 |     "collapsed": false
1766 |    },
1767 |    "outputs": [],
1768 |    "source": [
1769 |     "# now lets see how good our prediction is by calculating the r-squared value\n",
1770 |     "r_square = r_value ** 2\n",
1771 |     "print 'Linear Model r_square value', r_square"
1772 |    ]
1773 |   },
1774 |   {
1775 |    "cell_type": "code",
1776 |    "execution_count": null,
1777 |    "metadata": {
1778 |     "collapsed": true
1779 |    },
1780 |    "outputs": [],
1781 |    "source": [
1782 |     "# thats a poor model r_square ranges from 0 to 1, the close to 1 the better the model accuracy"
1783 |    ]
1784 |   },
1785 |   {
1786 |    "cell_type": "code",
1787 |    "execution_count": null,
1788 |    "metadata": {
1789 |     "collapsed": false,
1790 |     "scrolled": true
1791 |    },
1792 |    "outputs": [],
1793 |    "source": [
1794 |     "# now lets try a polynomial function instead of a linear function and see if that fits better\n",
1795 |     "polynomial = np.poly1d(np.polyfit(years, num_ratings, 3))\n",
1796 |     "plt.scatter(years, num_ratings)\n",
1797 |     "plt.plot(years, polynomial(years), c='r')\n",
1798 |     "plt.show()"
1799 |    ]
1800 |   },
1801 |   {
1802 |    "cell_type": "code",
1803 |    "execution_count": null,
1804 |    "metadata": {
1805 |     "collapsed": false
1806 |    },
1807 |    "outputs": [],
1808 |    "source": [
1809 |     "# now lets calculate the r-square for this polynomial regression\n",
1810 |     "\n",
1811 |     "from sklearn.metrics import r2_score\n",
1812 |     "r2 = r2_score(num_ratings, polynomial(years))\n",
1813 |     "print 'Polynomial Model r_square value', r2"
1814 |    ]
1815 |   },
1816 |   {
1817 |    "cell_type": "code",
1818 |    "execution_count": null,
1819 |    "metadata": {
1820 |     "collapsed": false
1821 |    },
1822 |    "outputs": [],
1823 |    "source": [
1824 |     "# now we can predict how many ratings we expect in any year using our polynomial function\n",
1825 |     "print polynomial(2017)          \n",
1826 |     "print polynomial(2018)     "
1827 |    ]
1828 |   },
1829 |   {
1830 |    "cell_type": "code",
1831 |    "execution_count": null,
1832 |    "metadata": {
1833 |     "collapsed": true
1834 |    },
1835 |    "outputs": [],
1836 |    "source": [
1837 |     "# awesome, now lets implement another important ML technique 'Classification'\n"
1838 |    ]
1839 |   }
1840 |  ],
1841 |  "metadata": {
1842 |   "kernelspec": {
1843 |    "display_name": "Python 2",
1844 |    "language": "python",
1845 |    "name": "python2"
1846 |   },
1847 |   "language_info": {
1848 |    "codemirror_mode": {
1849 |     "name": "ipython",
1850 |     "version": 2
1851 |    },
1852 |    "file_extension": ".py",
1853 |    "mimetype": "text/x-python",
1854 |    "name": "python",
1855 |    "nbconvert_exporter": "python",
1856 |    "pygments_lexer": "ipython2",
1857 |    "version": "2.7.11"
1858 |   }
1859 |  },
1860 |  "nbformat": 4,
1861 |  "nbformat_minor": 0
1862 | }
1863 | 


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