├── README.md
├── output.xls
└── douban.ipynb


/README.md:
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1 | # douban_top250
2 | analysis of douban top250 movies data
3 | 


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/output.xls:
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https://raw.githubusercontent.com/sorrowise/douban_top250/HEAD/output.xls


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/douban.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 2,
   6 |    "metadata": {
   7 |     "collapsed": true
   8 |    },
   9 |    "outputs": [],
  10 |    "source": [
  11 |     "# Import the required modules for the following codes\n",
  12 |     "\n",
  13 |     "import numpy as np\n",
  14 |     "import matplotlib.pyplot as plt\n",
  15 |     "import warnings\n",
  16 |     "warnings.filterwarnings(\"ignore\")\n",
  17 |     "import seaborn as sns\n",
  18 |     "import pandas as pd\n",
  19 |     "sns.set(style=\"white\",color_codes=True)\n",
  20 |     "%matplotlib inline\n",
  21 |     "plt.rcParams['figure.figsize'] = (15,9.27)\n",
  22 |     "plt.rcParams['font.size'] = 10.0\n",
  23 |     "plt.rcParams['xtick.labelsize'] = 'large'\n",
  24 |     "plt.rcParams['ytick.labelsize'] = 'large'"
  25 |    ]
  26 |   },
  27 |   {
  28 |    "cell_type": "markdown",
  29 |    "metadata": {},
  30 |    "source": [
  31 |     "# 1.Data Exploration"
  32 |    ]
  33 |   },
  34 |   {
  35 |    "cell_type": "code",
  36 |    "execution_count": 3,
  37 |    "metadata": {},
  38 |    "outputs": [
  39 |     {
  40 |      "data": {
  41 |       "text/html": [
  42 |        "<div>\n",
  43 |        "<style>\n",
  44 |        "    .dataframe thead tr:only-child th {\n",
  45 |        "        text-align: right;\n",
  46 |        "    }\n",
  47 |        "\n",
  48 |        "    .dataframe thead th {\n",
  49 |        "        text-align: left;\n",
  50 |        "    }\n",
  51 |        "\n",
  52 |        "    .dataframe tbody tr th {\n",
  53 |        "        vertical-align: top;\n",
  54 |        "    }\n",
  55 |        "</style>\n",
  56 |        "<table border=\"1\" class=\"dataframe\">\n",
  57 |        "  <thead>\n",
  58 |        "    <tr style=\"text-align: right;\">\n",
  59 |        "      <th></th>\n",
  60 |        "      <th>director</th>\n",
  61 |        "      <th>movie_name</th>\n",
  62 |        "      <th>rank</th>\n",
  63 |        "      <th>score</th>\n",
  64 |        "      <th>score_num</th>\n",
  65 |        "      <th>time</th>\n",
  66 |        "      <th>total_stars</th>\n",
  67 |        "      <th>wa_star</th>\n",
  68 |        "      <th>wa_star_std</th>\n",
  69 |        "      <th>year</th>\n",
  70 |        "    </tr>\n",
  71 |        "  </thead>\n",
  72 |        "  <tbody>\n",
  73 |        "    <tr>\n",
  74 |        "      <th>0</th>\n",
  75 |        "      <td>弗兰克·德拉邦特</td>\n",
  76 |        "      <td>肖申克的救赎</td>\n",
  77 |        "      <td>1</td>\n",
  78 |        "      <td>9.6</td>\n",
  79 |        "      <td>875072</td>\n",
  80 |        "      <td>142</td>\n",
  81 |        "      <td>4196845.0</td>\n",
  82 |        "      <td>4.796</td>\n",
  83 |        "      <td>0.469</td>\n",
  84 |        "      <td>1994</td>\n",
  85 |        "    </tr>\n",
  86 |        "    <tr>\n",
  87 |        "      <th>1</th>\n",
  88 |        "      <td>陈凯歌</td>\n",
  89 |        "      <td>霸王别姬</td>\n",
  90 |        "      <td>2</td>\n",
  91 |        "      <td>9.5</td>\n",
  92 |        "      <td>629139</td>\n",
  93 |        "      <td>171</td>\n",
  94 |        "      <td>2990926.0</td>\n",
  95 |        "      <td>4.754</td>\n",
  96 |        "      <td>0.515</td>\n",
  97 |        "      <td>1993</td>\n",
  98 |        "    </tr>\n",
  99 |        "    <tr>\n",
 100 |        "      <th>2</th>\n",
 101 |        "      <td>吕克·贝松</td>\n",
 102 |        "      <td>这个杀手不太冷</td>\n",
 103 |        "      <td>3</td>\n",
 104 |        "      <td>9.4</td>\n",
 105 |        "      <td>836619</td>\n",
 106 |        "      <td>110</td>\n",
 107 |        "      <td>3929599.0</td>\n",
 108 |        "      <td>4.697</td>\n",
 109 |        "      <td>0.551</td>\n",
 110 |        "      <td>1994</td>\n",
 111 |        "    </tr>\n",
 112 |        "    <tr>\n",
 113 |        "      <th>3</th>\n",
 114 |        "      <td>罗伯特·泽米吉斯</td>\n",
 115 |        "      <td>阿甘正传</td>\n",
 116 |        "      <td>4</td>\n",
 117 |        "      <td>9.4</td>\n",
 118 |        "      <td>713180</td>\n",
 119 |        "      <td>142</td>\n",
 120 |        "      <td>3351946.0</td>\n",
 121 |        "      <td>4.700</td>\n",
 122 |        "      <td>0.555</td>\n",
 123 |        "      <td>1994</td>\n",
 124 |        "    </tr>\n",
 125 |        "    <tr>\n",
 126 |        "      <th>4</th>\n",
 127 |        "      <td>罗伯托·贝尼尼</td>\n",
 128 |        "      <td>美丽人生</td>\n",
 129 |        "      <td>5</td>\n",
 130 |        "      <td>9.5</td>\n",
 131 |        "      <td>416064</td>\n",
 132 |        "      <td>116</td>\n",
 133 |        "      <td>1969230.0</td>\n",
 134 |        "      <td>4.733</td>\n",
 135 |        "      <td>0.525</td>\n",
 136 |        "      <td>1997</td>\n",
 137 |        "    </tr>\n",
 138 |        "  </tbody>\n",
 139 |        "</table>\n",
 140 |        "</div>"
 141 |       ],
 142 |       "text/plain": [
 143 |        "   director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
 144 |        "0  弗兰克·德拉邦特     肖申克的救赎     1    9.6     875072   142    4196845.0    4.796   \n",
 145 |        "1       陈凯歌       霸王别姬     2    9.5     629139   171    2990926.0    4.754   \n",
 146 |        "2     吕克·贝松    这个杀手不太冷     3    9.4     836619   110    3929599.0    4.697   \n",
 147 |        "3  罗伯特·泽米吉斯       阿甘正传     4    9.4     713180   142    3351946.0    4.700   \n",
 148 |        "4   罗伯托·贝尼尼       美丽人生     5    9.5     416064   116    1969230.0    4.733   \n",
 149 |        "\n",
 150 |        "   wa_star_std  year  \n",
 151 |        "0        0.469  1994  \n",
 152 |        "1        0.515  1993  \n",
 153 |        "2        0.551  1994  \n",
 154 |        "3        0.555  1994  \n",
 155 |        "4        0.525  1997  "
 156 |       ]
 157 |      },
 158 |      "execution_count": 3,
 159 |      "metadata": {},
 160 |      "output_type": "execute_result"
 161 |     }
 162 |    ],
 163 |    "source": [
 164 |     "# Let's see what information does the data contain?\n",
 165 |     "\n",
 166 |     "df = pd.read_excel('output.xls',encoding=\"UTF-8\")\n",
 167 |     "df.head()"
 168 |    ]
 169 |   },
 170 |   {
 171 |    "cell_type": "markdown",
 172 |    "metadata": {},
 173 |    "source": [
 174 |     "## 1.1 Directors Analysis"
 175 |    ]
 176 |   },
 177 |   {
 178 |    "cell_type": "markdown",
 179 |    "metadata": {},
 180 |    "source": [
 181 |     "### 1.1.1 number of movies the director shoot in the top250 list"
 182 |    ]
 183 |   },
 184 |   {
 185 |    "cell_type": "code",
 186 |    "execution_count": 152,
 187 |    "metadata": {},
 188 |    "outputs": [
 189 |     {
 190 |      "data": {
 191 |       "text/html": [
 192 |        "<div>\n",
 193 |        "<style>\n",
 194 |        "    .dataframe thead tr:only-child th {\n",
 195 |        "        text-align: right;\n",
 196 |        "    }\n",
 197 |        "\n",
 198 |        "    .dataframe thead th {\n",
 199 |        "        text-align: left;\n",
 200 |        "    }\n",
 201 |        "\n",
 202 |        "    .dataframe tbody tr th {\n",
 203 |        "        vertical-align: top;\n",
 204 |        "    }\n",
 205 |        "</style>\n",
 206 |        "<table border=\"1\" class=\"dataframe\">\n",
 207 |        "  <thead>\n",
 208 |        "    <tr style=\"text-align: right;\">\n",
 209 |        "      <th></th>\n",
 210 |        "      <th>count</th>\n",
 211 |        "      <th>pert</th>\n",
 212 |        "    </tr>\n",
 213 |        "  </thead>\n",
 214 |        "  <tbody>\n",
 215 |        "    <tr>\n",
 216 |        "      <th>克里斯托弗·诺兰</th>\n",
 217 |        "      <td>7</td>\n",
 218 |        "      <td>0.028</td>\n",
 219 |        "    </tr>\n",
 220 |        "    <tr>\n",
 221 |        "      <th>宫崎骏</th>\n",
 222 |        "      <td>7</td>\n",
 223 |        "      <td>0.028</td>\n",
 224 |        "    </tr>\n",
 225 |        "    <tr>\n",
 226 |        "      <th>史蒂文·斯皮尔伯格</th>\n",
 227 |        "      <td>6</td>\n",
 228 |        "      <td>0.024</td>\n",
 229 |        "    </tr>\n",
 230 |        "    <tr>\n",
 231 |        "      <th>王家卫</th>\n",
 232 |        "      <td>5</td>\n",
 233 |        "      <td>0.020</td>\n",
 234 |        "    </tr>\n",
 235 |        "    <tr>\n",
 236 |        "      <th>李安</th>\n",
 237 |        "      <td>4</td>\n",
 238 |        "      <td>0.016</td>\n",
 239 |        "    </tr>\n",
 240 |        "    <tr>\n",
 241 |        "      <th>大卫·芬奇</th>\n",
 242 |        "      <td>4</td>\n",
 243 |        "      <td>0.016</td>\n",
 244 |        "    </tr>\n",
 245 |        "    <tr>\n",
 246 |        "      <th>詹姆斯·卡梅隆</th>\n",
 247 |        "      <td>3</td>\n",
 248 |        "      <td>0.012</td>\n",
 249 |        "    </tr>\n",
 250 |        "    <tr>\n",
 251 |        "      <th>昆汀·塔伦蒂诺</th>\n",
 252 |        "      <td>3</td>\n",
 253 |        "      <td>0.012</td>\n",
 254 |        "    </tr>\n",
 255 |        "    <tr>\n",
 256 |        "      <th>彼得·杰克逊</th>\n",
 257 |        "      <td>3</td>\n",
 258 |        "      <td>0.012</td>\n",
 259 |        "    </tr>\n",
 260 |        "    <tr>\n",
 261 |        "      <th>刘镇伟</th>\n",
 262 |        "      <td>3</td>\n",
 263 |        "      <td>0.012</td>\n",
 264 |        "    </tr>\n",
 265 |        "    <tr>\n",
 266 |        "      <th>姜文</th>\n",
 267 |        "      <td>3</td>\n",
 268 |        "      <td>0.012</td>\n",
 269 |        "    </tr>\n",
 270 |        "    <tr>\n",
 271 |        "      <th>彼特·道格特</th>\n",
 272 |        "      <td>3</td>\n",
 273 |        "      <td>0.012</td>\n",
 274 |        "    </tr>\n",
 275 |        "    <tr>\n",
 276 |        "      <th>弗朗西斯·福特·科波拉</th>\n",
 277 |        "      <td>3</td>\n",
 278 |        "      <td>0.012</td>\n",
 279 |        "    </tr>\n",
 280 |        "    <tr>\n",
 281 |        "      <th>吴宇森</th>\n",
 282 |        "      <td>3</td>\n",
 283 |        "      <td>0.012</td>\n",
 284 |        "    </tr>\n",
 285 |        "    <tr>\n",
 286 |        "      <th>朱塞佩·托纳多雷</th>\n",
 287 |        "      <td>3</td>\n",
 288 |        "      <td>0.012</td>\n",
 289 |        "    </tr>\n",
 290 |        "    <tr>\n",
 291 |        "      <th>理查德·林克莱特</th>\n",
 292 |        "      <td>3</td>\n",
 293 |        "      <td>0.012</td>\n",
 294 |        "    </tr>\n",
 295 |        "    <tr>\n",
 296 |        "      <th>理查德·柯蒂斯</th>\n",
 297 |        "      <td>3</td>\n",
 298 |        "      <td>0.012</td>\n",
 299 |        "    </tr>\n",
 300 |        "    <tr>\n",
 301 |        "      <th>安德鲁·尼科尔</th>\n",
 302 |        "      <td>2</td>\n",
 303 |        "      <td>0.008</td>\n",
 304 |        "    </tr>\n",
 305 |        "    <tr>\n",
 306 |        "      <th>丹尼·博伊尔</th>\n",
 307 |        "      <td>2</td>\n",
 308 |        "      <td>0.008</td>\n",
 309 |        "    </tr>\n",
 310 |        "    <tr>\n",
 311 |        "      <th>吕克·贝松</th>\n",
 312 |        "      <td>2</td>\n",
 313 |        "      <td>0.008</td>\n",
 314 |        "    </tr>\n",
 315 |        "  </tbody>\n",
 316 |        "</table>\n",
 317 |        "</div>"
 318 |       ],
 319 |       "text/plain": [
 320 |        "             count   pert\n",
 321 |        "克里斯托弗·诺兰         7  0.028\n",
 322 |        "宫崎骏              7  0.028\n",
 323 |        "史蒂文·斯皮尔伯格        6  0.024\n",
 324 |        "王家卫              5  0.020\n",
 325 |        "李安               4  0.016\n",
 326 |        "大卫·芬奇            4  0.016\n",
 327 |        "詹姆斯·卡梅隆          3  0.012\n",
 328 |        "昆汀·塔伦蒂诺          3  0.012\n",
 329 |        "彼得·杰克逊           3  0.012\n",
 330 |        "刘镇伟              3  0.012\n",
 331 |        "姜文               3  0.012\n",
 332 |        "彼特·道格特           3  0.012\n",
 333 |        "弗朗西斯·福特·科波拉      3  0.012\n",
 334 |        "吴宇森              3  0.012\n",
 335 |        "朱塞佩·托纳多雷         3  0.012\n",
 336 |        "理查德·林克莱特         3  0.012\n",
 337 |        "理查德·柯蒂斯          3  0.012\n",
 338 |        "安德鲁·尼科尔          2  0.008\n",
 339 |        "丹尼·博伊尔           2  0.008\n",
 340 |        "吕克·贝松            2  0.008"
 341 |       ]
 342 |      },
 343 |      "execution_count": 152,
 344 |      "metadata": {},
 345 |      "output_type": "execute_result"
 346 |     }
 347 |    ],
 348 |    "source": [
 349 |     "directors = list(df['director'])\n",
 350 |     "dir_dict = {x:directors.count(x) for x in set(directors)}\n",
 351 |     "dir_df = pd.DataFrame.from_dict(dire,orient='index')\n",
 352 |     "dir_df.columns = ['count']\n",
 353 |     "dir_df.sort_values(['count'],axis=0,ascending=False,inplace=True)\n",
 354 |     "dir_df['pert'] = dir_df['count']/250\n",
 355 |     "dir_df[:20]"
 356 |    ]
 357 |   },
 358 |   {
 359 |    "cell_type": "code",
 360 |    "execution_count": 148,
 361 |    "metadata": {},
 362 |    "outputs": [
 363 |     {
 364 |      "data": {
 365 |       "text/html": [
 366 |        "<div>\n",
 367 |        "<style>\n",
 368 |        "    .dataframe thead tr:only-child th {\n",
 369 |        "        text-align: right;\n",
 370 |        "    }\n",
 371 |        "\n",
 372 |        "    .dataframe thead th {\n",
 373 |        "        text-align: left;\n",
 374 |        "    }\n",
 375 |        "\n",
 376 |        "    .dataframe tbody tr th {\n",
 377 |        "        vertical-align: top;\n",
 378 |        "    }\n",
 379 |        "</style>\n",
 380 |        "<table border=\"1\" class=\"dataframe\">\n",
 381 |        "  <thead>\n",
 382 |        "    <tr style=\"text-align: right;\">\n",
 383 |        "      <th></th>\n",
 384 |        "      <th>director</th>\n",
 385 |        "      <th>movie_name</th>\n",
 386 |        "      <th>rank</th>\n",
 387 |        "      <th>score</th>\n",
 388 |        "      <th>score_num</th>\n",
 389 |        "      <th>time</th>\n",
 390 |        "      <th>total_stars</th>\n",
 391 |        "      <th>wa_star</th>\n",
 392 |        "      <th>wa_star_std</th>\n",
 393 |        "      <th>year</th>\n",
 394 |        "      <th>order</th>\n",
 395 |        "      <th>diff</th>\n",
 396 |        "    </tr>\n",
 397 |        "  </thead>\n",
 398 |        "  <tbody>\n",
 399 |        "    <tr>\n",
 400 |        "      <th>8</th>\n",
 401 |        "      <td>克里斯托弗·诺兰</td>\n",
 402 |        "      <td>盗梦空间</td>\n",
 403 |        "      <td>9</td>\n",
 404 |        "      <td>9.2</td>\n",
 405 |        "      <td>764881</td>\n",
 406 |        "      <td>148</td>\n",
 407 |        "      <td>3539104.0</td>\n",
 408 |        "      <td>4.627</td>\n",
 409 |        "      <td>0.601</td>\n",
 410 |        "      <td>2010</td>\n",
 411 |        "      <td>11</td>\n",
 412 |        "      <td>-2</td>\n",
 413 |        "    </tr>\n",
 414 |        "    <tr>\n",
 415 |        "      <th>28</th>\n",
 416 |        "      <td>克里斯托弗·诺兰</td>\n",
 417 |        "      <td>星际穿越</td>\n",
 418 |        "      <td>29</td>\n",
 419 |        "      <td>9.1</td>\n",
 420 |        "      <td>484391</td>\n",
 421 |        "      <td>169</td>\n",
 422 |        "      <td>2211244.0</td>\n",
 423 |        "      <td>4.565</td>\n",
 424 |        "      <td>0.672</td>\n",
 425 |        "      <td>2014</td>\n",
 426 |        "      <td>25</td>\n",
 427 |        "      <td>4</td>\n",
 428 |        "    </tr>\n",
 429 |        "    <tr>\n",
 430 |        "      <th>33</th>\n",
 431 |        "      <td>克里斯托弗·诺兰</td>\n",
 432 |        "      <td>蝙蝠侠：黑暗骑士</td>\n",
 433 |        "      <td>34</td>\n",
 434 |        "      <td>9.0</td>\n",
 435 |        "      <td>319600</td>\n",
 436 |        "      <td>152</td>\n",
 437 |        "      <td>1441396.0</td>\n",
 438 |        "      <td>4.510</td>\n",
 439 |        "      <td>0.693</td>\n",
 440 |        "      <td>2008</td>\n",
 441 |        "      <td>33</td>\n",
 442 |        "      <td>1</td>\n",
 443 |        "    </tr>\n",
 444 |        "    <tr>\n",
 445 |        "      <th>59</th>\n",
 446 |        "      <td>克里斯托弗·诺兰</td>\n",
 447 |        "      <td>致命魔术</td>\n",
 448 |        "      <td>60</td>\n",
 449 |        "      <td>8.8</td>\n",
 450 |        "      <td>338208</td>\n",
 451 |        "      <td>130</td>\n",
 452 |        "      <td>1485409.0</td>\n",
 453 |        "      <td>4.392</td>\n",
 454 |        "      <td>0.702</td>\n",
 455 |        "      <td>2006</td>\n",
 456 |        "      <td>69</td>\n",
 457 |        "      <td>-9</td>\n",
 458 |        "    </tr>\n",
 459 |        "    <tr>\n",
 460 |        "      <th>128</th>\n",
 461 |        "      <td>克里斯托弗·诺兰</td>\n",
 462 |        "      <td>记忆碎片</td>\n",
 463 |        "      <td>129</td>\n",
 464 |        "      <td>8.5</td>\n",
 465 |        "      <td>270198</td>\n",
 466 |        "      <td>113</td>\n",
 467 |        "      <td>1150503.0</td>\n",
 468 |        "      <td>4.258</td>\n",
 469 |        "      <td>0.749</td>\n",
 470 |        "      <td>2000</td>\n",
 471 |        "      <td>125</td>\n",
 472 |        "      <td>4</td>\n",
 473 |        "    </tr>\n",
 474 |        "    <tr>\n",
 475 |        "      <th>158</th>\n",
 476 |        "      <td>克里斯托弗·诺兰</td>\n",
 477 |        "      <td>追随</td>\n",
 478 |        "      <td>159</td>\n",
 479 |        "      <td>8.9</td>\n",
 480 |        "      <td>77960</td>\n",
 481 |        "      <td>70</td>\n",
 482 |        "      <td>348715.0</td>\n",
 483 |        "      <td>4.473</td>\n",
 484 |        "      <td>0.669</td>\n",
 485 |        "      <td>1998</td>\n",
 486 |        "      <td>184</td>\n",
 487 |        "      <td>-25</td>\n",
 488 |        "    </tr>\n",
 489 |        "    <tr>\n",
 490 |        "      <th>173</th>\n",
 491 |        "      <td>克里斯托弗·诺兰</td>\n",
 492 |        "      <td>蝙蝠侠：黑暗骑士崛起</td>\n",
 493 |        "      <td>174</td>\n",
 494 |        "      <td>8.5</td>\n",
 495 |        "      <td>282198</td>\n",
 496 |        "      <td>165</td>\n",
 497 |        "      <td>1204703.0</td>\n",
 498 |        "      <td>4.269</td>\n",
 499 |        "      <td>0.758</td>\n",
 500 |        "      <td>2012</td>\n",
 501 |        "      <td>180</td>\n",
 502 |        "      <td>-6</td>\n",
 503 |        "    </tr>\n",
 504 |        "  </tbody>\n",
 505 |        "</table>\n",
 506 |        "</div>"
 507 |       ],
 508 |       "text/plain": [
 509 |        "     director  movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
 510 |        "8    克里斯托弗·诺兰        盗梦空间     9    9.2     764881   148    3539104.0    4.627   \n",
 511 |        "28   克里斯托弗·诺兰        星际穿越    29    9.1     484391   169    2211244.0    4.565   \n",
 512 |        "33   克里斯托弗·诺兰    蝙蝠侠：黑暗骑士    34    9.0     319600   152    1441396.0    4.510   \n",
 513 |        "59   克里斯托弗·诺兰        致命魔术    60    8.8     338208   130    1485409.0    4.392   \n",
 514 |        "128  克里斯托弗·诺兰        记忆碎片   129    8.5     270198   113    1150503.0    4.258   \n",
 515 |        "158  克里斯托弗·诺兰          追随   159    8.9      77960    70     348715.0    4.473   \n",
 516 |        "173  克里斯托弗·诺兰  蝙蝠侠：黑暗骑士崛起   174    8.5     282198   165    1204703.0    4.269   \n",
 517 |        "\n",
 518 |        "     wa_star_std  year  order  diff  \n",
 519 |        "8          0.601  2010     11    -2  \n",
 520 |        "28         0.672  2014     25     4  \n",
 521 |        "33         0.693  2008     33     1  \n",
 522 |        "59         0.702  2006     69    -9  \n",
 523 |        "128        0.749  2000    125     4  \n",
 524 |        "158        0.669  1998    184   -25  \n",
 525 |        "173        0.758  2012    180    -6  "
 526 |       ]
 527 |      },
 528 |      "execution_count": 148,
 529 |      "metadata": {},
 530 |      "output_type": "execute_result"
 531 |     }
 532 |    ],
 533 |    "source": [
 534 |     "df[df.director=='克里斯托弗·诺兰']"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 149,
 540 |    "metadata": {},
 541 |    "outputs": [
 542 |     {
 543 |      "data": {
 544 |       "text/html": [
 545 |        "<div>\n",
 546 |        "<style>\n",
 547 |        "    .dataframe thead tr:only-child th {\n",
 548 |        "        text-align: right;\n",
 549 |        "    }\n",
 550 |        "\n",
 551 |        "    .dataframe thead th {\n",
 552 |        "        text-align: left;\n",
 553 |        "    }\n",
 554 |        "\n",
 555 |        "    .dataframe tbody tr th {\n",
 556 |        "        vertical-align: top;\n",
 557 |        "    }\n",
 558 |        "</style>\n",
 559 |        "<table border=\"1\" class=\"dataframe\">\n",
 560 |        "  <thead>\n",
 561 |        "    <tr style=\"text-align: right;\">\n",
 562 |        "      <th></th>\n",
 563 |        "      <th>director</th>\n",
 564 |        "      <th>movie_name</th>\n",
 565 |        "      <th>rank</th>\n",
 566 |        "      <th>score</th>\n",
 567 |        "      <th>score_num</th>\n",
 568 |        "      <th>time</th>\n",
 569 |        "      <th>total_stars</th>\n",
 570 |        "      <th>wa_star</th>\n",
 571 |        "      <th>wa_star_std</th>\n",
 572 |        "      <th>year</th>\n",
 573 |        "      <th>order</th>\n",
 574 |        "      <th>diff</th>\n",
 575 |        "    </tr>\n",
 576 |        "  </thead>\n",
 577 |        "  <tbody>\n",
 578 |        "    <tr>\n",
 579 |        "      <th>5</th>\n",
 580 |        "      <td>宫崎骏</td>\n",
 581 |        "      <td>千与千寻</td>\n",
 582 |        "      <td>6</td>\n",
 583 |        "      <td>9.2</td>\n",
 584 |        "      <td>664387</td>\n",
 585 |        "      <td>125</td>\n",
 586 |        "      <td>3064152.0</td>\n",
 587 |        "      <td>4.612</td>\n",
 588 |        "      <td>0.600</td>\n",
 589 |        "      <td>2001</td>\n",
 590 |        "      <td>6</td>\n",
 591 |        "      <td>0</td>\n",
 592 |        "    </tr>\n",
 593 |        "    <tr>\n",
 594 |        "      <th>15</th>\n",
 595 |        "      <td>宫崎骏</td>\n",
 596 |        "      <td>龙猫</td>\n",
 597 |        "      <td>16</td>\n",
 598 |        "      <td>9.1</td>\n",
 599 |        "      <td>419367</td>\n",
 600 |        "      <td>86</td>\n",
 601 |        "      <td>1913990.0</td>\n",
 602 |        "      <td>4.564</td>\n",
 603 |        "      <td>0.634</td>\n",
 604 |        "      <td>1988</td>\n",
 605 |        "      <td>65</td>\n",
 606 |        "      <td>-49</td>\n",
 607 |        "    </tr>\n",
 608 |        "    <tr>\n",
 609 |        "      <th>30</th>\n",
 610 |        "      <td>宫崎骏</td>\n",
 611 |        "      <td>天空之城</td>\n",
 612 |        "      <td>31</td>\n",
 613 |        "      <td>9.0</td>\n",
 614 |        "      <td>332484</td>\n",
 615 |        "      <td>124</td>\n",
 616 |        "      <td>1498172.0</td>\n",
 617 |        "      <td>4.506</td>\n",
 618 |        "      <td>0.653</td>\n",
 619 |        "      <td>1986</td>\n",
 620 |        "      <td>24</td>\n",
 621 |        "      <td>7</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>43</th>\n",
 625 |        "      <td>宫崎骏</td>\n",
 626 |        "      <td>哈尔的移动城堡</td>\n",
 627 |        "      <td>44</td>\n",
 628 |        "      <td>8.9</td>\n",
 629 |        "      <td>346323</td>\n",
 630 |        "      <td>119</td>\n",
 631 |        "      <td>1533864.0</td>\n",
 632 |        "      <td>4.429</td>\n",
 633 |        "      <td>0.695</td>\n",
 634 |        "      <td>2004</td>\n",
 635 |        "      <td>117</td>\n",
 636 |        "      <td>-73</td>\n",
 637 |        "    </tr>\n",
 638 |        "    <tr>\n",
 639 |        "      <th>77</th>\n",
 640 |        "      <td>宫崎骏</td>\n",
 641 |        "      <td>幽灵公主</td>\n",
 642 |        "      <td>78</td>\n",
 643 |        "      <td>8.8</td>\n",
 644 |        "      <td>230241</td>\n",
 645 |        "      <td>134</td>\n",
 646 |        "      <td>1010757.0</td>\n",
 647 |        "      <td>4.390</td>\n",
 648 |        "      <td>0.699</td>\n",
 649 |        "      <td>1997</td>\n",
 650 |        "      <td>59</td>\n",
 651 |        "      <td>19</td>\n",
 652 |        "    </tr>\n",
 653 |        "    <tr>\n",
 654 |        "      <th>102</th>\n",
 655 |        "      <td>宫崎骏</td>\n",
 656 |        "      <td>风之谷</td>\n",
 657 |        "      <td>103</td>\n",
 658 |        "      <td>8.8</td>\n",
 659 |        "      <td>171616</td>\n",
 660 |        "      <td>117</td>\n",
 661 |        "      <td>753051.0</td>\n",
 662 |        "      <td>4.388</td>\n",
 663 |        "      <td>0.704</td>\n",
 664 |        "      <td>1984</td>\n",
 665 |        "      <td>77</td>\n",
 666 |        "      <td>26</td>\n",
 667 |        "    </tr>\n",
 668 |        "    <tr>\n",
 669 |        "      <th>187</th>\n",
 670 |        "      <td>宫崎骏</td>\n",
 671 |        "      <td>魔女宅急便</td>\n",
 672 |        "      <td>188</td>\n",
 673 |        "      <td>8.4</td>\n",
 674 |        "      <td>184309</td>\n",
 675 |        "      <td>103</td>\n",
 676 |        "      <td>777783.0</td>\n",
 677 |        "      <td>4.220</td>\n",
 678 |        "      <td>0.708</td>\n",
 679 |        "      <td>1989</td>\n",
 680 |        "      <td>242</td>\n",
 681 |        "      <td>-54</td>\n",
 682 |        "    </tr>\n",
 683 |        "  </tbody>\n",
 684 |        "</table>\n",
 685 |        "</div>"
 686 |       ],
 687 |       "text/plain": [
 688 |        "    director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
 689 |        "5        宫崎骏       千与千寻     6    9.2     664387   125    3064152.0    4.612   \n",
 690 |        "15       宫崎骏         龙猫    16    9.1     419367    86    1913990.0    4.564   \n",
 691 |        "30       宫崎骏       天空之城    31    9.0     332484   124    1498172.0    4.506   \n",
 692 |        "43       宫崎骏    哈尔的移动城堡    44    8.9     346323   119    1533864.0    4.429   \n",
 693 |        "77       宫崎骏       幽灵公主    78    8.8     230241   134    1010757.0    4.390   \n",
 694 |        "102      宫崎骏        风之谷   103    8.8     171616   117     753051.0    4.388   \n",
 695 |        "187      宫崎骏      魔女宅急便   188    8.4     184309   103     777783.0    4.220   \n",
 696 |        "\n",
 697 |        "     wa_star_std  year  order  diff  \n",
 698 |        "5          0.600  2001      6     0  \n",
 699 |        "15         0.634  1988     65   -49  \n",
 700 |        "30         0.653  1986     24     7  \n",
 701 |        "43         0.695  2004    117   -73  \n",
 702 |        "77         0.699  1997     59    19  \n",
 703 |        "102        0.704  1984     77    26  \n",
 704 |        "187        0.708  1989    242   -54  "
 705 |       ]
 706 |      },
 707 |      "execution_count": 149,
 708 |      "metadata": {},
 709 |      "output_type": "execute_result"
 710 |     }
 711 |    ],
 712 |    "source": [
 713 |     "df[df.director=='宫崎骏']"
 714 |    ]
 715 |   },
 716 |   {
 717 |    "cell_type": "code",
 718 |    "execution_count": 156,
 719 |    "metadata": {},
 720 |    "outputs": [
 721 |     {
 722 |      "data": {
 723 |       "text/html": [
 724 |        "<div>\n",
 725 |        "<style>\n",
 726 |        "    .dataframe thead tr:only-child th {\n",
 727 |        "        text-align: right;\n",
 728 |        "    }\n",
 729 |        "\n",
 730 |        "    .dataframe thead th {\n",
 731 |        "        text-align: left;\n",
 732 |        "    }\n",
 733 |        "\n",
 734 |        "    .dataframe tbody tr th {\n",
 735 |        "        vertical-align: top;\n",
 736 |        "    }\n",
 737 |        "</style>\n",
 738 |        "<table border=\"1\" class=\"dataframe\">\n",
 739 |        "  <thead>\n",
 740 |        "    <tr style=\"text-align: right;\">\n",
 741 |        "      <th></th>\n",
 742 |        "      <th>director</th>\n",
 743 |        "      <th>movie_name</th>\n",
 744 |        "      <th>rank</th>\n",
 745 |        "      <th>score</th>\n",
 746 |        "      <th>score_num</th>\n",
 747 |        "      <th>time</th>\n",
 748 |        "      <th>total_stars</th>\n",
 749 |        "      <th>wa_star</th>\n",
 750 |        "      <th>wa_star_std</th>\n",
 751 |        "      <th>year</th>\n",
 752 |        "      <th>order</th>\n",
 753 |        "      <th>diff</th>\n",
 754 |        "    </tr>\n",
 755 |        "  </thead>\n",
 756 |        "  <tbody>\n",
 757 |        "    <tr>\n",
 758 |        "      <th>6</th>\n",
 759 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 760 |        "      <td>辛德勒的名单</td>\n",
 761 |        "      <td>7</td>\n",
 762 |        "      <td>9.4</td>\n",
 763 |        "      <td>383622</td>\n",
 764 |        "      <td>195</td>\n",
 765 |        "      <td>1805325.0</td>\n",
 766 |        "      <td>4.706</td>\n",
 767 |        "      <td>0.547</td>\n",
 768 |        "      <td>1993</td>\n",
 769 |        "      <td>8</td>\n",
 770 |        "      <td>-1</td>\n",
 771 |        "    </tr>\n",
 772 |        "    <tr>\n",
 773 |        "      <th>71</th>\n",
 774 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 775 |        "      <td>拯救大兵瑞恩</td>\n",
 776 |        "      <td>72</td>\n",
 777 |        "      <td>8.8</td>\n",
 778 |        "      <td>232684</td>\n",
 779 |        "      <td>169</td>\n",
 780 |        "      <td>1029394.0</td>\n",
 781 |        "      <td>4.424</td>\n",
 782 |        "      <td>0.681</td>\n",
 783 |        "      <td>1998</td>\n",
 784 |        "      <td>179</td>\n",
 785 |        "      <td>-107</td>\n",
 786 |        "    </tr>\n",
 787 |        "    <tr>\n",
 788 |        "      <th>100</th>\n",
 789 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 790 |        "      <td>猫鼠游戏</td>\n",
 791 |        "      <td>101</td>\n",
 792 |        "      <td>8.7</td>\n",
 793 |        "      <td>214691</td>\n",
 794 |        "      <td>141</td>\n",
 795 |        "      <td>934549.0</td>\n",
 796 |        "      <td>4.353</td>\n",
 797 |        "      <td>0.681</td>\n",
 798 |        "      <td>2002</td>\n",
 799 |        "      <td>192</td>\n",
 800 |        "      <td>-91</td>\n",
 801 |        "    </tr>\n",
 802 |        "    <tr>\n",
 803 |        "      <th>110</th>\n",
 804 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 805 |        "      <td>幸福终点站</td>\n",
 806 |        "      <td>111</td>\n",
 807 |        "      <td>8.6</td>\n",
 808 |        "      <td>219675</td>\n",
 809 |        "      <td>128</td>\n",
 810 |        "      <td>947238.0</td>\n",
 811 |        "      <td>4.312</td>\n",
 812 |        "      <td>0.706</td>\n",
 813 |        "      <td>2004</td>\n",
 814 |        "      <td>64</td>\n",
 815 |        "      <td>47</td>\n",
 816 |        "    </tr>\n",
 817 |        "    <tr>\n",
 818 |        "      <th>152</th>\n",
 819 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 820 |        "      <td>人工智能</td>\n",
 821 |        "      <td>153</td>\n",
 822 |        "      <td>8.6</td>\n",
 823 |        "      <td>183252</td>\n",
 824 |        "      <td>146</td>\n",
 825 |        "      <td>783585.0</td>\n",
 826 |        "      <td>4.276</td>\n",
 827 |        "      <td>0.774</td>\n",
 828 |        "      <td>2001</td>\n",
 829 |        "      <td>224</td>\n",
 830 |        "      <td>-71</td>\n",
 831 |        "    </tr>\n",
 832 |        "    <tr>\n",
 833 |        "      <th>206</th>\n",
 834 |        "      <td>史蒂文·斯皮尔伯格</td>\n",
 835 |        "      <td>E.T.</td>\n",
 836 |        "      <td>207</td>\n",
 837 |        "      <td>8.5</td>\n",
 838 |        "      <td>145344</td>\n",
 839 |        "      <td>115</td>\n",
 840 |        "      <td>616258.0</td>\n",
 841 |        "      <td>4.240</td>\n",
 842 |        "      <td>0.723</td>\n",
 843 |        "      <td>1982</td>\n",
 844 |        "      <td>216</td>\n",
 845 |        "      <td>-9</td>\n",
 846 |        "    </tr>\n",
 847 |        "  </tbody>\n",
 848 |        "</table>\n",
 849 |        "</div>"
 850 |       ],
 851 |       "text/plain": [
 852 |        "      director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
 853 |        "6    史蒂文·斯皮尔伯格     辛德勒的名单     7    9.4     383622   195    1805325.0    4.706   \n",
 854 |        "71   史蒂文·斯皮尔伯格     拯救大兵瑞恩    72    8.8     232684   169    1029394.0    4.424   \n",
 855 |        "100  史蒂文·斯皮尔伯格       猫鼠游戏   101    8.7     214691   141     934549.0    4.353   \n",
 856 |        "110  史蒂文·斯皮尔伯格      幸福终点站   111    8.6     219675   128     947238.0    4.312   \n",
 857 |        "152  史蒂文·斯皮尔伯格       人工智能   153    8.6     183252   146     783585.0    4.276   \n",
 858 |        "206  史蒂文·斯皮尔伯格       E.T.   207    8.5     145344   115     616258.0    4.240   \n",
 859 |        "\n",
 860 |        "     wa_star_std  year  order  diff  \n",
 861 |        "6          0.547  1993      8    -1  \n",
 862 |        "71         0.681  1998    179  -107  \n",
 863 |        "100        0.681  2002    192   -91  \n",
 864 |        "110        0.706  2004     64    47  \n",
 865 |        "152        0.774  2001    224   -71  \n",
 866 |        "206        0.723  1982    216    -9  "
 867 |       ]
 868 |      },
 869 |      "execution_count": 156,
 870 |      "metadata": {},
 871 |      "output_type": "execute_result"
 872 |     }
 873 |    ],
 874 |    "source": [
 875 |     "df[df.director=='史蒂文·斯皮尔伯格']"
 876 |    ]
 877 |   },
 878 |   {
 879 |    "cell_type": "code",
 880 |    "execution_count": 150,
 881 |    "metadata": {},
 882 |    "outputs": [
 883 |     {
 884 |      "data": {
 885 |       "text/html": [
 886 |        "<div>\n",
 887 |        "<style>\n",
 888 |        "    .dataframe thead tr:only-child th {\n",
 889 |        "        text-align: right;\n",
 890 |        "    }\n",
 891 |        "\n",
 892 |        "    .dataframe thead th {\n",
 893 |        "        text-align: left;\n",
 894 |        "    }\n",
 895 |        "\n",
 896 |        "    .dataframe tbody tr th {\n",
 897 |        "        vertical-align: top;\n",
 898 |        "    }\n",
 899 |        "</style>\n",
 900 |        "<table border=\"1\" class=\"dataframe\">\n",
 901 |        "  <thead>\n",
 902 |        "    <tr style=\"text-align: right;\">\n",
 903 |        "      <th></th>\n",
 904 |        "      <th>director</th>\n",
 905 |        "      <th>movie_name</th>\n",
 906 |        "      <th>rank</th>\n",
 907 |        "      <th>score</th>\n",
 908 |        "      <th>score_num</th>\n",
 909 |        "      <th>time</th>\n",
 910 |        "      <th>total_stars</th>\n",
 911 |        "      <th>wa_star</th>\n",
 912 |        "      <th>wa_star_std</th>\n",
 913 |        "      <th>year</th>\n",
 914 |        "      <th>order</th>\n",
 915 |        "      <th>diff</th>\n",
 916 |        "    </tr>\n",
 917 |        "  </thead>\n",
 918 |        "  <tbody>\n",
 919 |        "    <tr>\n",
 920 |        "      <th>76</th>\n",
 921 |        "      <td>王家卫</td>\n",
 922 |        "      <td>春光乍泄</td>\n",
 923 |        "      <td>77</td>\n",
 924 |        "      <td>8.8</td>\n",
 925 |        "      <td>240053</td>\n",
 926 |        "      <td>96</td>\n",
 927 |        "      <td>1052872.0</td>\n",
 928 |        "      <td>4.386</td>\n",
 929 |        "      <td>0.714</td>\n",
 930 |        "      <td>1997</td>\n",
 931 |        "      <td>37</td>\n",
 932 |        "      <td>40</td>\n",
 933 |        "    </tr>\n",
 934 |        "    <tr>\n",
 935 |        "      <th>83</th>\n",
 936 |        "      <td>王家卫</td>\n",
 937 |        "      <td>重庆森林</td>\n",
 938 |        "      <td>84</td>\n",
 939 |        "      <td>8.6</td>\n",
 940 |        "      <td>321698</td>\n",
 941 |        "      <td>102</td>\n",
 942 |        "      <td>1389735.0</td>\n",
 943 |        "      <td>4.320</td>\n",
 944 |        "      <td>0.739</td>\n",
 945 |        "      <td>1994</td>\n",
 946 |        "      <td>36</td>\n",
 947 |        "      <td>48</td>\n",
 948 |        "    </tr>\n",
 949 |        "    <tr>\n",
 950 |        "      <th>127</th>\n",
 951 |        "      <td>王家卫</td>\n",
 952 |        "      <td>东邪西毒</td>\n",
 953 |        "      <td>128</td>\n",
 954 |        "      <td>8.6</td>\n",
 955 |        "      <td>241668</td>\n",
 956 |        "      <td>100</td>\n",
 957 |        "      <td>1034339.0</td>\n",
 958 |        "      <td>4.280</td>\n",
 959 |        "      <td>0.790</td>\n",
 960 |        "      <td>1994</td>\n",
 961 |        "      <td>243</td>\n",
 962 |        "      <td>-115</td>\n",
 963 |        "    </tr>\n",
 964 |        "    <tr>\n",
 965 |        "      <th>154</th>\n",
 966 |        "      <td>王家卫</td>\n",
 967 |        "      <td>花样年华</td>\n",
 968 |        "      <td>155</td>\n",
 969 |        "      <td>8.5</td>\n",
 970 |        "      <td>235920</td>\n",
 971 |        "      <td>98</td>\n",
 972 |        "      <td>999593.0</td>\n",
 973 |        "      <td>4.237</td>\n",
 974 |        "      <td>0.731</td>\n",
 975 |        "      <td>2000</td>\n",
 976 |        "      <td>210</td>\n",
 977 |        "      <td>-55</td>\n",
 978 |        "    </tr>\n",
 979 |        "    <tr>\n",
 980 |        "      <th>183</th>\n",
 981 |        "      <td>王家卫</td>\n",
 982 |        "      <td>阿飞正传</td>\n",
 983 |        "      <td>184</td>\n",
 984 |        "      <td>8.5</td>\n",
 985 |        "      <td>171350</td>\n",
 986 |        "      <td>94</td>\n",
 987 |        "      <td>726695.0</td>\n",
 988 |        "      <td>4.241</td>\n",
 989 |        "      <td>0.725</td>\n",
 990 |        "      <td>1990</td>\n",
 991 |        "      <td>147</td>\n",
 992 |        "      <td>37</td>\n",
 993 |        "    </tr>\n",
 994 |        "  </tbody>\n",
 995 |        "</table>\n",
 996 |        "</div>"
 997 |       ],
 998 |       "text/plain": [
 999 |        "    director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
1000 |        "76       王家卫       春光乍泄    77    8.8     240053    96    1052872.0    4.386   \n",
1001 |        "83       王家卫       重庆森林    84    8.6     321698   102    1389735.0    4.320   \n",
1002 |        "127      王家卫       东邪西毒   128    8.6     241668   100    1034339.0    4.280   \n",
1003 |        "154      王家卫       花样年华   155    8.5     235920    98     999593.0    4.237   \n",
1004 |        "183      王家卫       阿飞正传   184    8.5     171350    94     726695.0    4.241   \n",
1005 |        "\n",
1006 |        "     wa_star_std  year  order  diff  \n",
1007 |        "76         0.714  1997     37    40  \n",
1008 |        "83         0.739  1994     36    48  \n",
1009 |        "127        0.790  1994    243  -115  \n",
1010 |        "154        0.731  2000    210   -55  \n",
1011 |        "183        0.725  1990    147    37  "
1012 |       ]
1013 |      },
1014 |      "execution_count": 150,
1015 |      "metadata": {},
1016 |      "output_type": "execute_result"
1017 |     }
1018 |    ],
1019 |    "source": [
1020 |     "df[df.director=='王家卫']"
1021 |    ]
1022 |   },
1023 |   {
1024 |    "cell_type": "code",
1025 |    "execution_count": 157,
1026 |    "metadata": {},
1027 |    "outputs": [
1028 |     {
1029 |      "data": {
1030 |       "text/html": [
1031 |        "<div>\n",
1032 |        "<style>\n",
1033 |        "    .dataframe thead tr:only-child th {\n",
1034 |        "        text-align: right;\n",
1035 |        "    }\n",
1036 |        "\n",
1037 |        "    .dataframe thead th {\n",
1038 |        "        text-align: left;\n",
1039 |        "    }\n",
1040 |        "\n",
1041 |        "    .dataframe tbody tr th {\n",
1042 |        "        vertical-align: top;\n",
1043 |        "    }\n",
1044 |        "</style>\n",
1045 |        "<table border=\"1\" class=\"dataframe\">\n",
1046 |        "  <thead>\n",
1047 |        "    <tr style=\"text-align: right;\">\n",
1048 |        "      <th></th>\n",
1049 |        "      <th>director</th>\n",
1050 |        "      <th>movie_name</th>\n",
1051 |        "      <th>rank</th>\n",
1052 |        "      <th>score</th>\n",
1053 |        "      <th>score_num</th>\n",
1054 |        "      <th>time</th>\n",
1055 |        "      <th>total_stars</th>\n",
1056 |        "      <th>wa_star</th>\n",
1057 |        "      <th>wa_star_std</th>\n",
1058 |        "      <th>year</th>\n",
1059 |        "      <th>order</th>\n",
1060 |        "      <th>diff</th>\n",
1061 |        "    </tr>\n",
1062 |        "  </thead>\n",
1063 |        "  <tbody>\n",
1064 |        "    <tr>\n",
1065 |        "      <th>29</th>\n",
1066 |        "      <td>李安</td>\n",
1067 |        "      <td>少年派的奇幻漂流</td>\n",
1068 |        "      <td>30</td>\n",
1069 |        "      <td>9.0</td>\n",
1070 |        "      <td>578974</td>\n",
1071 |        "      <td>127</td>\n",
1072 |        "      <td>2615225.0</td>\n",
1073 |        "      <td>4.517</td>\n",
1074 |        "      <td>0.666</td>\n",
1075 |        "      <td>2012</td>\n",
1076 |        "      <td>7</td>\n",
1077 |        "      <td>23</td>\n",
1078 |        "    </tr>\n",
1079 |        "    <tr>\n",
1080 |        "      <th>55</th>\n",
1081 |        "      <td>李安</td>\n",
1082 |        "      <td>饮食男女</td>\n",
1083 |        "      <td>56</td>\n",
1084 |        "      <td>9.0</td>\n",
1085 |        "      <td>193303</td>\n",
1086 |        "      <td>124</td>\n",
1087 |        "      <td>873922.0</td>\n",
1088 |        "      <td>4.521</td>\n",
1089 |        "      <td>0.632</td>\n",
1090 |        "      <td>1994</td>\n",
1091 |        "      <td>95</td>\n",
1092 |        "      <td>-39</td>\n",
1093 |        "    </tr>\n",
1094 |        "    <tr>\n",
1095 |        "      <th>96</th>\n",
1096 |        "      <td>李安</td>\n",
1097 |        "      <td>断背山</td>\n",
1098 |        "      <td>97</td>\n",
1099 |        "      <td>8.6</td>\n",
1100 |        "      <td>310668</td>\n",
1101 |        "      <td>134</td>\n",
1102 |        "      <td>1335251.0</td>\n",
1103 |        "      <td>4.298</td>\n",
1104 |        "      <td>0.749</td>\n",
1105 |        "      <td>2005</td>\n",
1106 |        "      <td>157</td>\n",
1107 |        "      <td>-60</td>\n",
1108 |        "    </tr>\n",
1109 |        "    <tr>\n",
1110 |        "      <th>133</th>\n",
1111 |        "      <td>李安</td>\n",
1112 |        "      <td>喜宴</td>\n",
1113 |        "      <td>134</td>\n",
1114 |        "      <td>8.8</td>\n",
1115 |        "      <td>133146</td>\n",
1116 |        "      <td>108</td>\n",
1117 |        "      <td>584910.0</td>\n",
1118 |        "      <td>4.393</td>\n",
1119 |        "      <td>0.650</td>\n",
1120 |        "      <td>1993</td>\n",
1121 |        "      <td>106</td>\n",
1122 |        "      <td>28</td>\n",
1123 |        "    </tr>\n",
1124 |        "  </tbody>\n",
1125 |        "</table>\n",
1126 |        "</div>"
1127 |       ],
1128 |       "text/plain": [
1129 |        "    director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
1130 |        "29        李安   少年派的奇幻漂流    30    9.0     578974   127    2615225.0    4.517   \n",
1131 |        "55        李安       饮食男女    56    9.0     193303   124     873922.0    4.521   \n",
1132 |        "96        李安        断背山    97    8.6     310668   134    1335251.0    4.298   \n",
1133 |        "133       李安         喜宴   134    8.8     133146   108     584910.0    4.393   \n",
1134 |        "\n",
1135 |        "     wa_star_std  year  order  diff  \n",
1136 |        "29         0.666  2012      7    23  \n",
1137 |        "55         0.632  1994     95   -39  \n",
1138 |        "96         0.749  2005    157   -60  \n",
1139 |        "133        0.650  1993    106    28  "
1140 |       ]
1141 |      },
1142 |      "execution_count": 157,
1143 |      "metadata": {},
1144 |      "output_type": "execute_result"
1145 |     }
1146 |    ],
1147 |    "source": [
1148 |     "df[df.director=='李安']"
1149 |    ]
1150 |   },
1151 |   {
1152 |    "cell_type": "markdown",
1153 |    "metadata": {},
1154 |    "source": [
1155 |     "### 1.1.2 The highest average score director"
1156 |    ]
1157 |   },
1158 |   {
1159 |    "cell_type": "code",
1160 |    "execution_count": 169,
1161 |    "metadata": {},
1162 |    "outputs": [
1163 |     {
1164 |      "data": {
1165 |       "text/html": [
1166 |        "<div>\n",
1167 |        "<style>\n",
1168 |        "    .dataframe thead tr:only-child th {\n",
1169 |        "        text-align: right;\n",
1170 |        "    }\n",
1171 |        "\n",
1172 |        "    .dataframe thead th {\n",
1173 |        "        text-align: left;\n",
1174 |        "    }\n",
1175 |        "\n",
1176 |        "    .dataframe tbody tr th {\n",
1177 |        "        vertical-align: top;\n",
1178 |        "    }\n",
1179 |        "</style>\n",
1180 |        "<table border=\"1\" class=\"dataframe\">\n",
1181 |        "  <thead>\n",
1182 |        "    <tr style=\"text-align: right;\">\n",
1183 |        "      <th></th>\n",
1184 |        "      <th>av_score</th>\n",
1185 |        "    </tr>\n",
1186 |        "  </thead>\n",
1187 |        "  <tbody>\n",
1188 |        "    <tr>\n",
1189 |        "      <th>比利·怀尔德</th>\n",
1190 |        "      <td>9.60</td>\n",
1191 |        "    </tr>\n",
1192 |        "    <tr>\n",
1193 |        "      <th>陈凯歌</th>\n",
1194 |        "      <td>9.50</td>\n",
1195 |        "    </tr>\n",
1196 |        "    <tr>\n",
1197 |        "      <th>罗伯托·贝尼尼</th>\n",
1198 |        "      <td>9.50</td>\n",
1199 |        "    </tr>\n",
1200 |        "    <tr>\n",
1201 |        "      <th>西德尼·吕美特</th>\n",
1202 |        "      <td>9.40</td>\n",
1203 |        "    </tr>\n",
1204 |        "    <tr>\n",
1205 |        "      <th>安德鲁·斯坦顿</th>\n",
1206 |        "      <td>9.30</td>\n",
1207 |        "    </tr>\n",
1208 |        "    <tr>\n",
1209 |        "      <th>路易·西霍尤斯</th>\n",
1210 |        "      <td>9.30</td>\n",
1211 |        "    </tr>\n",
1212 |        "    <tr>\n",
1213 |        "      <th>克里斯托夫·巴拉蒂</th>\n",
1214 |        "      <td>9.20</td>\n",
1215 |        "    </tr>\n",
1216 |        "    <tr>\n",
1217 |        "      <th>拉斯·霍尔斯道姆</th>\n",
1218 |        "      <td>9.20</td>\n",
1219 |        "    </tr>\n",
1220 |        "    <tr>\n",
1221 |        "      <th>马基德·马基迪</th>\n",
1222 |        "      <td>9.20</td>\n",
1223 |        "    </tr>\n",
1224 |        "    <tr>\n",
1225 |        "      <th>小津安二郎</th>\n",
1226 |        "      <td>9.20</td>\n",
1227 |        "    </tr>\n",
1228 |        "    <tr>\n",
1229 |        "      <th>拜伦·霍华德</th>\n",
1230 |        "      <td>9.20</td>\n",
1231 |        "    </tr>\n",
1232 |        "    <tr>\n",
1233 |        "      <th>万籁鸣</th>\n",
1234 |        "      <td>9.20</td>\n",
1235 |        "    </tr>\n",
1236 |        "    <tr>\n",
1237 |        "      <th>查理·卓别林</th>\n",
1238 |        "      <td>9.20</td>\n",
1239 |        "    </tr>\n",
1240 |        "    <tr>\n",
1241 |        "      <th>黄东赫</th>\n",
1242 |        "      <td>9.20</td>\n",
1243 |        "    </tr>\n",
1244 |        "    <tr>\n",
1245 |        "      <th>维克多·弗莱明</th>\n",
1246 |        "      <td>9.20</td>\n",
1247 |        "    </tr>\n",
1248 |        "    <tr>\n",
1249 |        "      <th>弗兰克·德拉邦特</th>\n",
1250 |        "      <td>9.15</td>\n",
1251 |        "    </tr>\n",
1252 |        "    <tr>\n",
1253 |        "      <th>弗洛里安·亨克尔·冯·多纳斯马尔克</th>\n",
1254 |        "      <td>9.10</td>\n",
1255 |        "    </tr>\n",
1256 |        "    <tr>\n",
1257 |        "      <th>赛尔乔·莱翁内</th>\n",
1258 |        "      <td>9.10</td>\n",
1259 |        "    </tr>\n",
1260 |        "    <tr>\n",
1261 |        "      <th>杨宇硕</th>\n",
1262 |        "      <td>9.10</td>\n",
1263 |        "    </tr>\n",
1264 |        "    <tr>\n",
1265 |        "      <th>张艺谋</th>\n",
1266 |        "      <td>9.10</td>\n",
1267 |        "    </tr>\n",
1268 |        "  </tbody>\n",
1269 |        "</table>\n",
1270 |        "</div>"
1271 |       ],
1272 |       "text/plain": [
1273 |        "                   av_score\n",
1274 |        "比利·怀尔德                 9.60\n",
1275 |        "陈凯歌                    9.50\n",
1276 |        "罗伯托·贝尼尼                9.50\n",
1277 |        "西德尼·吕美特                9.40\n",
1278 |        "安德鲁·斯坦顿                9.30\n",
1279 |        "路易·西霍尤斯                9.30\n",
1280 |        "克里斯托夫·巴拉蒂              9.20\n",
1281 |        "拉斯·霍尔斯道姆               9.20\n",
1282 |        "马基德·马基迪                9.20\n",
1283 |        "小津安二郎                  9.20\n",
1284 |        "拜伦·霍华德                 9.20\n",
1285 |        "万籁鸣                    9.20\n",
1286 |        "查理·卓别林                 9.20\n",
1287 |        "黄东赫                    9.20\n",
1288 |        "维克多·弗莱明                9.20\n",
1289 |        "弗兰克·德拉邦特               9.15\n",
1290 |        "弗洛里安·亨克尔·冯·多纳斯马尔克      9.10\n",
1291 |        "赛尔乔·莱翁内                9.10\n",
1292 |        "杨宇硕                    9.10\n",
1293 |        "张艺谋                    9.10"
1294 |       ]
1295 |      },
1296 |      "execution_count": 169,
1297 |      "metadata": {},
1298 |      "output_type": "execute_result"
1299 |     }
1300 |    ],
1301 |    "source": [
1302 |     "director_av_score = {x:df[df.director==x]['score'].mean() for x in set(directors)}\n",
1303 |     "dire_score_df = pd.DataFrame.from_dict(director_av_score,orient='index')\n",
1304 |     "dire_score_df.columns = ['av_score']\n",
1305 |     "dire_score_df.sort_values(['av_score'],axis=0,ascending=False,inplace=True)\n",
1306 |     "dire_score_df[:20]"
1307 |    ]
1308 |   },
1309 |   {
1310 |    "cell_type": "markdown",
1311 |    "metadata": {},
1312 |    "source": [
1313 |     "## 1.2 Year Distribution"
1314 |    ]
1315 |   },
1316 |   {
1317 |    "cell_type": "code",
1318 |    "execution_count": 228,
1319 |    "metadata": {},
1320 |    "outputs": [
1321 |     {
1322 |      "data": {
1323 |       "text/html": [
1324 |        "<div>\n",
1325 |        "<style>\n",
1326 |        "    .dataframe thead tr:only-child th {\n",
1327 |        "        text-align: right;\n",
1328 |        "    }\n",
1329 |        "\n",
1330 |        "    .dataframe thead th {\n",
1331 |        "        text-align: left;\n",
1332 |        "    }\n",
1333 |        "\n",
1334 |        "    .dataframe tbody tr th {\n",
1335 |        "        vertical-align: top;\n",
1336 |        "    }\n",
1337 |        "</style>\n",
1338 |        "<table border=\"1\" class=\"dataframe\">\n",
1339 |        "  <thead>\n",
1340 |        "    <tr style=\"text-align: right;\">\n",
1341 |        "      <th></th>\n",
1342 |        "      <th>year_count</th>\n",
1343 |        "      <th>pert</th>\n",
1344 |        "    </tr>\n",
1345 |        "  </thead>\n",
1346 |        "  <tbody>\n",
1347 |        "    <tr>\n",
1348 |        "      <th>2010</th>\n",
1349 |        "      <td>13</td>\n",
1350 |        "      <td>0.052</td>\n",
1351 |        "    </tr>\n",
1352 |        "    <tr>\n",
1353 |        "      <th>1994</th>\n",
1354 |        "      <td>12</td>\n",
1355 |        "      <td>0.048</td>\n",
1356 |        "    </tr>\n",
1357 |        "    <tr>\n",
1358 |        "      <th>2009</th>\n",
1359 |        "      <td>12</td>\n",
1360 |        "      <td>0.048</td>\n",
1361 |        "    </tr>\n",
1362 |        "    <tr>\n",
1363 |        "      <th>2004</th>\n",
1364 |        "      <td>12</td>\n",
1365 |        "      <td>0.048</td>\n",
1366 |        "    </tr>\n",
1367 |        "    <tr>\n",
1368 |        "      <th>2011</th>\n",
1369 |        "      <td>11</td>\n",
1370 |        "      <td>0.044</td>\n",
1371 |        "    </tr>\n",
1372 |        "    <tr>\n",
1373 |        "      <th>2001</th>\n",
1374 |        "      <td>11</td>\n",
1375 |        "      <td>0.044</td>\n",
1376 |        "    </tr>\n",
1377 |        "    <tr>\n",
1378 |        "      <th>2002</th>\n",
1379 |        "      <td>10</td>\n",
1380 |        "      <td>0.040</td>\n",
1381 |        "    </tr>\n",
1382 |        "    <tr>\n",
1383 |        "      <th>2013</th>\n",
1384 |        "      <td>10</td>\n",
1385 |        "      <td>0.040</td>\n",
1386 |        "    </tr>\n",
1387 |        "    <tr>\n",
1388 |        "      <th>1997</th>\n",
1389 |        "      <td>9</td>\n",
1390 |        "      <td>0.036</td>\n",
1391 |        "    </tr>\n",
1392 |        "    <tr>\n",
1393 |        "      <th>2014</th>\n",
1394 |        "      <td>9</td>\n",
1395 |        "      <td>0.036</td>\n",
1396 |        "    </tr>\n",
1397 |        "    <tr>\n",
1398 |        "      <th>2008</th>\n",
1399 |        "      <td>9</td>\n",
1400 |        "      <td>0.036</td>\n",
1401 |        "    </tr>\n",
1402 |        "    <tr>\n",
1403 |        "      <th>2006</th>\n",
1404 |        "      <td>9</td>\n",
1405 |        "      <td>0.036</td>\n",
1406 |        "    </tr>\n",
1407 |        "    <tr>\n",
1408 |        "      <th>1995</th>\n",
1409 |        "      <td>9</td>\n",
1410 |        "      <td>0.036</td>\n",
1411 |        "    </tr>\n",
1412 |        "    <tr>\n",
1413 |        "      <th>1999</th>\n",
1414 |        "      <td>8</td>\n",
1415 |        "      <td>0.032</td>\n",
1416 |        "    </tr>\n",
1417 |        "    <tr>\n",
1418 |        "      <th>2000</th>\n",
1419 |        "      <td>8</td>\n",
1420 |        "      <td>0.032</td>\n",
1421 |        "    </tr>\n",
1422 |        "    <tr>\n",
1423 |        "      <th>2003</th>\n",
1424 |        "      <td>8</td>\n",
1425 |        "      <td>0.032</td>\n",
1426 |        "    </tr>\n",
1427 |        "    <tr>\n",
1428 |        "      <th>2007</th>\n",
1429 |        "      <td>8</td>\n",
1430 |        "      <td>0.032</td>\n",
1431 |        "    </tr>\n",
1432 |        "    <tr>\n",
1433 |        "      <th>1993</th>\n",
1434 |        "      <td>7</td>\n",
1435 |        "      <td>0.028</td>\n",
1436 |        "    </tr>\n",
1437 |        "    <tr>\n",
1438 |        "      <th>1998</th>\n",
1439 |        "      <td>6</td>\n",
1440 |        "      <td>0.024</td>\n",
1441 |        "    </tr>\n",
1442 |        "    <tr>\n",
1443 |        "      <th>1988</th>\n",
1444 |        "      <td>5</td>\n",
1445 |        "      <td>0.020</td>\n",
1446 |        "    </tr>\n",
1447 |        "  </tbody>\n",
1448 |        "</table>\n",
1449 |        "</div>"
1450 |       ],
1451 |       "text/plain": [
1452 |        "      year_count   pert\n",
1453 |        "2010          13  0.052\n",
1454 |        "1994          12  0.048\n",
1455 |        "2009          12  0.048\n",
1456 |        "2004          12  0.048\n",
1457 |        "2011          11  0.044\n",
1458 |        "2001          11  0.044\n",
1459 |        "2002          10  0.040\n",
1460 |        "2013          10  0.040\n",
1461 |        "1997           9  0.036\n",
1462 |        "2014           9  0.036\n",
1463 |        "2008           9  0.036\n",
1464 |        "2006           9  0.036\n",
1465 |        "1995           9  0.036\n",
1466 |        "1999           8  0.032\n",
1467 |        "2000           8  0.032\n",
1468 |        "2003           8  0.032\n",
1469 |        "2007           8  0.032\n",
1470 |        "1993           7  0.028\n",
1471 |        "1998           6  0.024\n",
1472 |        "1988           5  0.020"
1473 |       ]
1474 |      },
1475 |      "execution_count": 228,
1476 |      "metadata": {},
1477 |      "output_type": "execute_result"
1478 |     }
1479 |    ],
1480 |    "source": [
1481 |     "year = list(df['year'])\n",
1482 |     "year_dict = {x:year.count(x) for x in set(year)}\n",
1483 |     "year_df = pd.DataFrame.from_dict(year_dict,orient='index')\n",
1484 |     "year_df.columns = ['year_count']\n",
1485 |     "new_year_df = year_df.sort_values(['year_count'],axis=0,ascending=False,inplace=False)\n",
1486 |     "new_year_df['pert'] = year_df['year_count']/250\n",
1487 |     "new_year_df[:20]"
1488 |    ]
1489 |   },
1490 |   {
1491 |    "cell_type": "code",
1492 |    "execution_count": 146,
1493 |    "metadata": {},
1494 |    "outputs": [
1495 |     {
1496 |      "data": {
1497 |       "text/html": [
1498 |        "<div>\n",
1499 |        "<style>\n",
1500 |        "    .dataframe thead tr:only-child th {\n",
1501 |        "        text-align: right;\n",
1502 |        "    }\n",
1503 |        "\n",
1504 |        "    .dataframe thead th {\n",
1505 |        "        text-align: left;\n",
1506 |        "    }\n",
1507 |        "\n",
1508 |        "    .dataframe tbody tr th {\n",
1509 |        "        vertical-align: top;\n",
1510 |        "    }\n",
1511 |        "</style>\n",
1512 |        "<table border=\"1\" class=\"dataframe\">\n",
1513 |        "  <thead>\n",
1514 |        "    <tr style=\"text-align: right;\">\n",
1515 |        "      <th></th>\n",
1516 |        "      <th>director</th>\n",
1517 |        "      <th>movie_name</th>\n",
1518 |        "      <th>rank</th>\n",
1519 |        "      <th>score</th>\n",
1520 |        "      <th>score_num</th>\n",
1521 |        "      <th>time</th>\n",
1522 |        "      <th>total_stars</th>\n",
1523 |        "      <th>wa_star</th>\n",
1524 |        "      <th>wa_star_std</th>\n",
1525 |        "      <th>year</th>\n",
1526 |        "      <th>order</th>\n",
1527 |        "      <th>diff</th>\n",
1528 |        "    </tr>\n",
1529 |        "  </thead>\n",
1530 |        "  <tbody>\n",
1531 |        "    <tr>\n",
1532 |        "      <th>8</th>\n",
1533 |        "      <td>克里斯托弗·诺兰</td>\n",
1534 |        "      <td>盗梦空间</td>\n",
1535 |        "      <td>9</td>\n",
1536 |        "      <td>9.2</td>\n",
1537 |        "      <td>764881</td>\n",
1538 |        "      <td>148</td>\n",
1539 |        "      <td>3539104.0</td>\n",
1540 |        "      <td>4.627</td>\n",
1541 |        "      <td>0.601</td>\n",
1542 |        "      <td>2010</td>\n",
1543 |        "      <td>11</td>\n",
1544 |        "      <td>-2</td>\n",
1545 |        "    </tr>\n",
1546 |        "    <tr>\n",
1547 |        "      <th>26</th>\n",
1548 |        "      <td>罗伯·莱纳</td>\n",
1549 |        "      <td>怦然心动</td>\n",
1550 |        "      <td>27</td>\n",
1551 |        "      <td>8.9</td>\n",
1552 |        "      <td>546851</td>\n",
1553 |        "      <td>90</td>\n",
1554 |        "      <td>2435127.0</td>\n",
1555 |        "      <td>4.453</td>\n",
1556 |        "      <td>0.685</td>\n",
1557 |        "      <td>2010</td>\n",
1558 |        "      <td>57</td>\n",
1559 |        "      <td>-30</td>\n",
1560 |        "    </tr>\n",
1561 |        "    <tr>\n",
1562 |        "      <th>78</th>\n",
1563 |        "      <td>姜文</td>\n",
1564 |        "      <td>让子弹飞</td>\n",
1565 |        "      <td>79</td>\n",
1566 |        "      <td>8.7</td>\n",
1567 |        "      <td>611213</td>\n",
1568 |        "      <td>132</td>\n",
1569 |        "      <td>2648997.0</td>\n",
1570 |        "      <td>4.334</td>\n",
1571 |        "      <td>0.769</td>\n",
1572 |        "      <td>2010</td>\n",
1573 |        "      <td>49</td>\n",
1574 |        "      <td>30</td>\n",
1575 |        "    </tr>\n",
1576 |        "    <tr>\n",
1577 |        "      <th>85</th>\n",
1578 |        "      <td>马丁·斯科塞斯</td>\n",
1579 |        "      <td>禁闭岛</td>\n",
1580 |        "      <td>86</td>\n",
1581 |        "      <td>8.6</td>\n",
1582 |        "      <td>350277</td>\n",
1583 |        "      <td>138</td>\n",
1584 |        "      <td>1510744.0</td>\n",
1585 |        "      <td>4.313</td>\n",
1586 |        "      <td>0.704</td>\n",
1587 |        "      <td>2010</td>\n",
1588 |        "      <td>108</td>\n",
1589 |        "      <td>-22</td>\n",
1590 |        "    </tr>\n",
1591 |        "    <tr>\n",
1592 |        "      <th>91</th>\n",
1593 |        "      <td>中岛哲也</td>\n",
1594 |        "      <td>告白</td>\n",
1595 |        "      <td>92</td>\n",
1596 |        "      <td>8.7</td>\n",
1597 |        "      <td>333704</td>\n",
1598 |        "      <td>106</td>\n",
1599 |        "      <td>1444270.0</td>\n",
1600 |        "      <td>4.328</td>\n",
1601 |        "      <td>0.776</td>\n",
1602 |        "      <td>2010</td>\n",
1603 |        "      <td>129</td>\n",
1604 |        "      <td>-37</td>\n",
1605 |        "    </tr>\n",
1606 |        "    <tr>\n",
1607 |        "      <th>107</th>\n",
1608 |        "      <td>迪恩·德布洛斯</td>\n",
1609 |        "      <td>驯龙高手</td>\n",
1610 |        "      <td>108</td>\n",
1611 |        "      <td>8.7</td>\n",
1612 |        "      <td>276513</td>\n",
1613 |        "      <td>98</td>\n",
1614 |        "      <td>1197024.0</td>\n",
1615 |        "      <td>4.329</td>\n",
1616 |        "      <td>0.705</td>\n",
1617 |        "      <td>2010</td>\n",
1618 |        "      <td>72</td>\n",
1619 |        "      <td>36</td>\n",
1620 |        "    </tr>\n",
1621 |        "    <tr>\n",
1622 |        "      <th>116</th>\n",
1623 |        "      <td>皮埃尔·科凡</td>\n",
1624 |        "      <td>神偷奶爸</td>\n",
1625 |        "      <td>117</td>\n",
1626 |        "      <td>8.5</td>\n",
1627 |        "      <td>362011</td>\n",
1628 |        "      <td>95</td>\n",
1629 |        "      <td>1541080.0</td>\n",
1630 |        "      <td>4.257</td>\n",
1631 |        "      <td>0.718</td>\n",
1632 |        "      <td>2010</td>\n",
1633 |        "      <td>132</td>\n",
1634 |        "      <td>-15</td>\n",
1635 |        "    </tr>\n",
1636 |        "    <tr>\n",
1637 |        "      <th>117</th>\n",
1638 |        "      <td>罗启锐</td>\n",
1639 |        "      <td>岁月神偷</td>\n",
1640 |        "      <td>118</td>\n",
1641 |        "      <td>8.6</td>\n",
1642 |        "      <td>299518</td>\n",
1643 |        "      <td>117</td>\n",
1644 |        "      <td>1290323.0</td>\n",
1645 |        "      <td>4.308</td>\n",
1646 |        "      <td>0.739</td>\n",
1647 |        "      <td>2010</td>\n",
1648 |        "      <td>181</td>\n",
1649 |        "      <td>-63</td>\n",
1650 |        "    </tr>\n",
1651 |        "    <tr>\n",
1652 |        "      <th>120</th>\n",
1653 |        "      <td>米林宏昌</td>\n",
1654 |        "      <td>借东西的小人阿莉埃蒂</td>\n",
1655 |        "      <td>121</td>\n",
1656 |        "      <td>8.7</td>\n",
1657 |        "      <td>209713</td>\n",
1658 |        "      <td>94</td>\n",
1659 |        "      <td>909525.0</td>\n",
1660 |        "      <td>4.337</td>\n",
1661 |        "      <td>0.706</td>\n",
1662 |        "      <td>2010</td>\n",
1663 |        "      <td>98</td>\n",
1664 |        "      <td>23</td>\n",
1665 |        "    </tr>\n",
1666 |        "    <tr>\n",
1667 |        "      <th>134</th>\n",
1668 |        "      <td>达伦·阿伦诺夫斯基</td>\n",
1669 |        "      <td>黑天鹅</td>\n",
1670 |        "      <td>135</td>\n",
1671 |        "      <td>8.5</td>\n",
1672 |        "      <td>404017</td>\n",
1673 |        "      <td>108</td>\n",
1674 |        "      <td>1715860.0</td>\n",
1675 |        "      <td>4.247</td>\n",
1676 |        "      <td>0.725</td>\n",
1677 |        "      <td>2010</td>\n",
1678 |        "      <td>204</td>\n",
1679 |        "      <td>-69</td>\n",
1680 |        "    </tr>\n",
1681 |        "    <tr>\n",
1682 |        "      <th>148</th>\n",
1683 |        "      <td>李·昂克里奇</td>\n",
1684 |        "      <td>玩具总动员3</td>\n",
1685 |        "      <td>149</td>\n",
1686 |        "      <td>8.7</td>\n",
1687 |        "      <td>186117</td>\n",
1688 |        "      <td>103</td>\n",
1689 |        "      <td>814075.0</td>\n",
1690 |        "      <td>4.374</td>\n",
1691 |        "      <td>0.718</td>\n",
1692 |        "      <td>2010</td>\n",
1693 |        "      <td>188</td>\n",
1694 |        "      <td>-39</td>\n",
1695 |        "    </tr>\n",
1696 |        "    <tr>\n",
1697 |        "      <th>217</th>\n",
1698 |        "      <td>普特鹏·普罗萨卡·那·萨克那卡林</td>\n",
1699 |        "      <td>初恋这件小事</td>\n",
1700 |        "      <td>218</td>\n",
1701 |        "      <td>8.3</td>\n",
1702 |        "      <td>435895</td>\n",
1703 |        "      <td>118</td>\n",
1704 |        "      <td>1798502.0</td>\n",
1705 |        "      <td>4.126</td>\n",
1706 |        "      <td>0.816</td>\n",
1707 |        "      <td>2010</td>\n",
1708 |        "      <td>186</td>\n",
1709 |        "      <td>32</td>\n",
1710 |        "    </tr>\n",
1711 |        "    <tr>\n",
1712 |        "      <th>228</th>\n",
1713 |        "      <td>汤姆·霍珀</td>\n",
1714 |        "      <td>国王的演讲</td>\n",
1715 |        "      <td>229</td>\n",
1716 |        "      <td>8.3</td>\n",
1717 |        "      <td>321773</td>\n",
1718 |        "      <td>118</td>\n",
1719 |        "      <td>1334392.0</td>\n",
1720 |        "      <td>4.147</td>\n",
1721 |        "      <td>0.712</td>\n",
1722 |        "      <td>2010</td>\n",
1723 |        "      <td>178</td>\n",
1724 |        "      <td>51</td>\n",
1725 |        "    </tr>\n",
1726 |        "  </tbody>\n",
1727 |        "</table>\n",
1728 |        "</div>"
1729 |       ],
1730 |       "text/plain": [
1731 |        "             director  movie_name  rank  score  score_num  time  total_stars  \\\n",
1732 |        "8            克里斯托弗·诺兰        盗梦空间     9    9.2     764881   148    3539104.0   \n",
1733 |        "26              罗伯·莱纳        怦然心动    27    8.9     546851    90    2435127.0   \n",
1734 |        "78                 姜文        让子弹飞    79    8.7     611213   132    2648997.0   \n",
1735 |        "85            马丁·斯科塞斯         禁闭岛    86    8.6     350277   138    1510744.0   \n",
1736 |        "91               中岛哲也          告白    92    8.7     333704   106    1444270.0   \n",
1737 |        "107           迪恩·德布洛斯        驯龙高手   108    8.7     276513    98    1197024.0   \n",
1738 |        "116            皮埃尔·科凡        神偷奶爸   117    8.5     362011    95    1541080.0   \n",
1739 |        "117               罗启锐        岁月神偷   118    8.6     299518   117    1290323.0   \n",
1740 |        "120              米林宏昌  借东西的小人阿莉埃蒂   121    8.7     209713    94     909525.0   \n",
1741 |        "134         达伦·阿伦诺夫斯基         黑天鹅   135    8.5     404017   108    1715860.0   \n",
1742 |        "148            李·昂克里奇      玩具总动员3   149    8.7     186117   103     814075.0   \n",
1743 |        "217  普特鹏·普罗萨卡·那·萨克那卡林      初恋这件小事   218    8.3     435895   118    1798502.0   \n",
1744 |        "228             汤姆·霍珀       国王的演讲   229    8.3     321773   118    1334392.0   \n",
1745 |        "\n",
1746 |        "     wa_star  wa_star_std  year  order  diff  \n",
1747 |        "8      4.627        0.601  2010     11    -2  \n",
1748 |        "26     4.453        0.685  2010     57   -30  \n",
1749 |        "78     4.334        0.769  2010     49    30  \n",
1750 |        "85     4.313        0.704  2010    108   -22  \n",
1751 |        "91     4.328        0.776  2010    129   -37  \n",
1752 |        "107    4.329        0.705  2010     72    36  \n",
1753 |        "116    4.257        0.718  2010    132   -15  \n",
1754 |        "117    4.308        0.739  2010    181   -63  \n",
1755 |        "120    4.337        0.706  2010     98    23  \n",
1756 |        "134    4.247        0.725  2010    204   -69  \n",
1757 |        "148    4.374        0.718  2010    188   -39  \n",
1758 |        "217    4.126        0.816  2010    186    32  \n",
1759 |        "228    4.147        0.712  2010    178    51  "
1760 |       ]
1761 |      },
1762 |      "execution_count": 146,
1763 |      "metadata": {},
1764 |      "output_type": "execute_result"
1765 |     }
1766 |    ],
1767 |    "source": [
1768 |     "df[df.year==2010]"
1769 |    ]
1770 |   },
1771 |   {
1772 |    "cell_type": "code",
1773 |    "execution_count": 147,
1774 |    "metadata": {},
1775 |    "outputs": [
1776 |     {
1777 |      "data": {
1778 |       "text/html": [
1779 |        "<div>\n",
1780 |        "<style>\n",
1781 |        "    .dataframe thead tr:only-child th {\n",
1782 |        "        text-align: right;\n",
1783 |        "    }\n",
1784 |        "\n",
1785 |        "    .dataframe thead th {\n",
1786 |        "        text-align: left;\n",
1787 |        "    }\n",
1788 |        "\n",
1789 |        "    .dataframe tbody tr th {\n",
1790 |        "        vertical-align: top;\n",
1791 |        "    }\n",
1792 |        "</style>\n",
1793 |        "<table border=\"1\" class=\"dataframe\">\n",
1794 |        "  <thead>\n",
1795 |        "    <tr style=\"text-align: right;\">\n",
1796 |        "      <th></th>\n",
1797 |        "      <th>director</th>\n",
1798 |        "      <th>movie_name</th>\n",
1799 |        "      <th>rank</th>\n",
1800 |        "      <th>score</th>\n",
1801 |        "      <th>score_num</th>\n",
1802 |        "      <th>time</th>\n",
1803 |        "      <th>total_stars</th>\n",
1804 |        "      <th>wa_star</th>\n",
1805 |        "      <th>wa_star_std</th>\n",
1806 |        "      <th>year</th>\n",
1807 |        "      <th>order</th>\n",
1808 |        "      <th>diff</th>\n",
1809 |        "    </tr>\n",
1810 |        "  </thead>\n",
1811 |        "  <tbody>\n",
1812 |        "    <tr>\n",
1813 |        "      <th>0</th>\n",
1814 |        "      <td>弗兰克·德拉邦特</td>\n",
1815 |        "      <td>肖申克的救赎</td>\n",
1816 |        "      <td>1</td>\n",
1817 |        "      <td>9.6</td>\n",
1818 |        "      <td>875072</td>\n",
1819 |        "      <td>142</td>\n",
1820 |        "      <td>4196845.0</td>\n",
1821 |        "      <td>4.796</td>\n",
1822 |        "      <td>0.469</td>\n",
1823 |        "      <td>1994</td>\n",
1824 |        "      <td>1</td>\n",
1825 |        "      <td>0</td>\n",
1826 |        "    </tr>\n",
1827 |        "    <tr>\n",
1828 |        "      <th>2</th>\n",
1829 |        "      <td>吕克·贝松</td>\n",
1830 |        "      <td>这个杀手不太冷</td>\n",
1831 |        "      <td>3</td>\n",
1832 |        "      <td>9.4</td>\n",
1833 |        "      <td>836619</td>\n",
1834 |        "      <td>110</td>\n",
1835 |        "      <td>3929599.0</td>\n",
1836 |        "      <td>4.697</td>\n",
1837 |        "      <td>0.551</td>\n",
1838 |        "      <td>1994</td>\n",
1839 |        "      <td>9</td>\n",
1840 |        "      <td>-6</td>\n",
1841 |        "    </tr>\n",
1842 |        "    <tr>\n",
1843 |        "      <th>3</th>\n",
1844 |        "      <td>罗伯特·泽米吉斯</td>\n",
1845 |        "      <td>阿甘正传</td>\n",
1846 |        "      <td>4</td>\n",
1847 |        "      <td>9.4</td>\n",
1848 |        "      <td>713180</td>\n",
1849 |        "      <td>142</td>\n",
1850 |        "      <td>3351946.0</td>\n",
1851 |        "      <td>4.700</td>\n",
1852 |        "      <td>0.555</td>\n",
1853 |        "      <td>1994</td>\n",
1854 |        "      <td>4</td>\n",
1855 |        "      <td>0</td>\n",
1856 |        "    </tr>\n",
1857 |        "    <tr>\n",
1858 |        "      <th>35</th>\n",
1859 |        "      <td>张艺谋</td>\n",
1860 |        "      <td>活着</td>\n",
1861 |        "      <td>36</td>\n",
1862 |        "      <td>9.1</td>\n",
1863 |        "      <td>265809</td>\n",
1864 |        "      <td>132</td>\n",
1865 |        "      <td>1203317.0</td>\n",
1866 |        "      <td>4.527</td>\n",
1867 |        "      <td>0.642</td>\n",
1868 |        "      <td>1994</td>\n",
1869 |        "      <td>68</td>\n",
1870 |        "      <td>-32</td>\n",
1871 |        "    </tr>\n",
1872 |        "    <tr>\n",
1873 |        "      <th>55</th>\n",
1874 |        "      <td>李安</td>\n",
1875 |        "      <td>饮食男女</td>\n",
1876 |        "      <td>56</td>\n",
1877 |        "      <td>9.0</td>\n",
1878 |        "      <td>193303</td>\n",
1879 |        "      <td>124</td>\n",
1880 |        "      <td>873922.0</td>\n",
1881 |        "      <td>4.521</td>\n",
1882 |        "      <td>0.632</td>\n",
1883 |        "      <td>1994</td>\n",
1884 |        "      <td>95</td>\n",
1885 |        "      <td>-39</td>\n",
1886 |        "    </tr>\n",
1887 |        "    <tr>\n",
1888 |        "      <th>57</th>\n",
1889 |        "      <td>罗杰·阿勒斯</td>\n",
1890 |        "      <td>狮子王</td>\n",
1891 |        "      <td>58</td>\n",
1892 |        "      <td>8.9</td>\n",
1893 |        "      <td>278222</td>\n",
1894 |        "      <td>89</td>\n",
1895 |        "      <td>1234749.0</td>\n",
1896 |        "      <td>4.438</td>\n",
1897 |        "      <td>0.678</td>\n",
1898 |        "      <td>1994</td>\n",
1899 |        "      <td>20</td>\n",
1900 |        "      <td>38</td>\n",
1901 |        "    </tr>\n",
1902 |        "    <tr>\n",
1903 |        "      <th>65</th>\n",
1904 |        "      <td>昆汀·塔伦蒂诺</td>\n",
1905 |        "      <td>低俗小说</td>\n",
1906 |        "      <td>66</td>\n",
1907 |        "      <td>8.7</td>\n",
1908 |        "      <td>329102</td>\n",
1909 |        "      <td>154</td>\n",
1910 |        "      <td>1438175.0</td>\n",
1911 |        "      <td>4.370</td>\n",
1912 |        "      <td>0.739</td>\n",
1913 |        "      <td>1994</td>\n",
1914 |        "      <td>94</td>\n",
1915 |        "      <td>-28</td>\n",
1916 |        "    </tr>\n",
1917 |        "    <tr>\n",
1918 |        "      <th>80</th>\n",
1919 |        "      <td>姜文</td>\n",
1920 |        "      <td>阳光灿烂的日子</td>\n",
1921 |        "      <td>81</td>\n",
1922 |        "      <td>8.7</td>\n",
1923 |        "      <td>264797</td>\n",
1924 |        "      <td>134</td>\n",
1925 |        "      <td>1155838.0</td>\n",
1926 |        "      <td>4.365</td>\n",
1927 |        "      <td>0.696</td>\n",
1928 |        "      <td>1994</td>\n",
1929 |        "      <td>71</td>\n",
1930 |        "      <td>10</td>\n",
1931 |        "    </tr>\n",
1932 |        "    <tr>\n",
1933 |        "      <th>83</th>\n",
1934 |        "      <td>王家卫</td>\n",
1935 |        "      <td>重庆森林</td>\n",
1936 |        "      <td>84</td>\n",
1937 |        "      <td>8.6</td>\n",
1938 |        "      <td>321698</td>\n",
1939 |        "      <td>102</td>\n",
1940 |        "      <td>1389735.0</td>\n",
1941 |        "      <td>4.320</td>\n",
1942 |        "      <td>0.739</td>\n",
1943 |        "      <td>1994</td>\n",
1944 |        "      <td>36</td>\n",
1945 |        "      <td>48</td>\n",
1946 |        "    </tr>\n",
1947 |        "    <tr>\n",
1948 |        "      <th>121</th>\n",
1949 |        "      <td>爱德华·兹威克</td>\n",
1950 |        "      <td>燃情岁月</td>\n",
1951 |        "      <td>122</td>\n",
1952 |        "      <td>8.8</td>\n",
1953 |        "      <td>145827</td>\n",
1954 |        "      <td>133</td>\n",
1955 |        "      <td>637993.0</td>\n",
1956 |        "      <td>4.375</td>\n",
1957 |        "      <td>0.742</td>\n",
1958 |        "      <td>1994</td>\n",
1959 |        "      <td>215</td>\n",
1960 |        "      <td>-93</td>\n",
1961 |        "    </tr>\n",
1962 |        "    <tr>\n",
1963 |        "      <th>127</th>\n",
1964 |        "      <td>王家卫</td>\n",
1965 |        "      <td>东邪西毒</td>\n",
1966 |        "      <td>128</td>\n",
1967 |        "      <td>8.6</td>\n",
1968 |        "      <td>241668</td>\n",
1969 |        "      <td>100</td>\n",
1970 |        "      <td>1034339.0</td>\n",
1971 |        "      <td>4.280</td>\n",
1972 |        "      <td>0.790</td>\n",
1973 |        "      <td>1994</td>\n",
1974 |        "      <td>243</td>\n",
1975 |        "      <td>-115</td>\n",
1976 |        "    </tr>\n",
1977 |        "    <tr>\n",
1978 |        "      <th>244</th>\n",
1979 |        "      <td>尼尔·乔丹</td>\n",
1980 |        "      <td>夜访吸血鬼</td>\n",
1981 |        "      <td>245</td>\n",
1982 |        "      <td>8.3</td>\n",
1983 |        "      <td>189233</td>\n",
1984 |        "      <td>123</td>\n",
1985 |        "      <td>784370.0</td>\n",
1986 |        "      <td>4.145</td>\n",
1987 |        "      <td>0.778</td>\n",
1988 |        "      <td>1994</td>\n",
1989 |        "      <td>244</td>\n",
1990 |        "      <td>1</td>\n",
1991 |        "    </tr>\n",
1992 |        "  </tbody>\n",
1993 |        "</table>\n",
1994 |        "</div>"
1995 |       ],
1996 |       "text/plain": [
1997 |        "     director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
1998 |        "0    弗兰克·德拉邦特     肖申克的救赎     1    9.6     875072   142    4196845.0    4.796   \n",
1999 |        "2       吕克·贝松    这个杀手不太冷     3    9.4     836619   110    3929599.0    4.697   \n",
2000 |        "3    罗伯特·泽米吉斯       阿甘正传     4    9.4     713180   142    3351946.0    4.700   \n",
2001 |        "35        张艺谋         活着    36    9.1     265809   132    1203317.0    4.527   \n",
2002 |        "55         李安       饮食男女    56    9.0     193303   124     873922.0    4.521   \n",
2003 |        "57     罗杰·阿勒斯        狮子王    58    8.9     278222    89    1234749.0    4.438   \n",
2004 |        "65    昆汀·塔伦蒂诺       低俗小说    66    8.7     329102   154    1438175.0    4.370   \n",
2005 |        "80         姜文    阳光灿烂的日子    81    8.7     264797   134    1155838.0    4.365   \n",
2006 |        "83        王家卫       重庆森林    84    8.6     321698   102    1389735.0    4.320   \n",
2007 |        "121   爱德华·兹威克       燃情岁月   122    8.8     145827   133     637993.0    4.375   \n",
2008 |        "127       王家卫       东邪西毒   128    8.6     241668   100    1034339.0    4.280   \n",
2009 |        "244     尼尔·乔丹      夜访吸血鬼   245    8.3     189233   123     784370.0    4.145   \n",
2010 |        "\n",
2011 |        "     wa_star_std  year  order  diff  \n",
2012 |        "0          0.469  1994      1     0  \n",
2013 |        "2          0.551  1994      9    -6  \n",
2014 |        "3          0.555  1994      4     0  \n",
2015 |        "35         0.642  1994     68   -32  \n",
2016 |        "55         0.632  1994     95   -39  \n",
2017 |        "57         0.678  1994     20    38  \n",
2018 |        "65         0.739  1994     94   -28  \n",
2019 |        "80         0.696  1994     71    10  \n",
2020 |        "83         0.739  1994     36    48  \n",
2021 |        "121        0.742  1994    215   -93  \n",
2022 |        "127        0.790  1994    243  -115  \n",
2023 |        "244        0.778  1994    244     1  "
2024 |       ]
2025 |      },
2026 |      "execution_count": 147,
2027 |      "metadata": {},
2028 |      "output_type": "execute_result"
2029 |     }
2030 |    ],
2031 |    "source": [
2032 |     "df[df.year==1994]"
2033 |    ]
2034 |   },
2035 |   {
2036 |    "cell_type": "code",
2037 |    "execution_count": 155,
2038 |    "metadata": {
2039 |     "scrolled": false
2040 |    },
2041 |    "outputs": [
2042 |     {
2043 |      "data": {
2044 |       "text/html": [
2045 |        "<div>\n",
2046 |        "<style>\n",
2047 |        "    .dataframe thead tr:only-child th {\n",
2048 |        "        text-align: right;\n",
2049 |        "    }\n",
2050 |        "\n",
2051 |        "    .dataframe thead th {\n",
2052 |        "        text-align: left;\n",
2053 |        "    }\n",
2054 |        "\n",
2055 |        "    .dataframe tbody tr th {\n",
2056 |        "        vertical-align: top;\n",
2057 |        "    }\n",
2058 |        "</style>\n",
2059 |        "<table border=\"1\" class=\"dataframe\">\n",
2060 |        "  <thead>\n",
2061 |        "    <tr style=\"text-align: right;\">\n",
2062 |        "      <th></th>\n",
2063 |        "      <th>director</th>\n",
2064 |        "      <th>movie_name</th>\n",
2065 |        "      <th>rank</th>\n",
2066 |        "      <th>score</th>\n",
2067 |        "      <th>score_num</th>\n",
2068 |        "      <th>time</th>\n",
2069 |        "      <th>total_stars</th>\n",
2070 |        "      <th>wa_star</th>\n",
2071 |        "      <th>wa_star_std</th>\n",
2072 |        "      <th>year</th>\n",
2073 |        "      <th>order</th>\n",
2074 |        "      <th>diff</th>\n",
2075 |        "    </tr>\n",
2076 |        "  </thead>\n",
2077 |        "  <tbody>\n",
2078 |        "    <tr>\n",
2079 |        "      <th>11</th>\n",
2080 |        "      <td>拉吉库马尔·希拉尼</td>\n",
2081 |        "      <td>三傻大闹宝莱坞</td>\n",
2082 |        "      <td>12</td>\n",
2083 |        "      <td>9.1</td>\n",
2084 |        "      <td>675424</td>\n",
2085 |        "      <td>171</td>\n",
2086 |        "      <td>3085336.0</td>\n",
2087 |        "      <td>4.568</td>\n",
2088 |        "      <td>0.687</td>\n",
2089 |        "      <td>2009</td>\n",
2090 |        "      <td>27</td>\n",
2091 |        "      <td>-15</td>\n",
2092 |        "    </tr>\n",
2093 |        "    <tr>\n",
2094 |        "      <th>12</th>\n",
2095 |        "      <td>拉斯·霍尔斯道姆</td>\n",
2096 |        "      <td>忠犬八公的故事</td>\n",
2097 |        "      <td>13</td>\n",
2098 |        "      <td>9.2</td>\n",
2099 |        "      <td>456513</td>\n",
2100 |        "      <td>93</td>\n",
2101 |        "      <td>2103611.0</td>\n",
2102 |        "      <td>4.608</td>\n",
2103 |        "      <td>0.625</td>\n",
2104 |        "      <td>2009</td>\n",
2105 |        "      <td>22</td>\n",
2106 |        "      <td>-9</td>\n",
2107 |        "    </tr>\n",
2108 |        "    <tr>\n",
2109 |        "      <th>37</th>\n",
2110 |        "      <td>彼特·道格特</td>\n",
2111 |        "      <td>飞屋环游记</td>\n",
2112 |        "      <td>38</td>\n",
2113 |        "      <td>8.9</td>\n",
2114 |        "      <td>496926</td>\n",
2115 |        "      <td>96</td>\n",
2116 |        "      <td>2208836.0</td>\n",
2117 |        "      <td>4.445</td>\n",
2118 |        "      <td>0.677</td>\n",
2119 |        "      <td>2009</td>\n",
2120 |        "      <td>126</td>\n",
2121 |        "      <td>-88</td>\n",
2122 |        "    </tr>\n",
2123 |        "    <tr>\n",
2124 |        "      <th>40</th>\n",
2125 |        "      <td>路易·西霍尤斯</td>\n",
2126 |        "      <td>海豚湾</td>\n",
2127 |        "      <td>41</td>\n",
2128 |        "      <td>9.3</td>\n",
2129 |        "      <td>181260</td>\n",
2130 |        "      <td>92</td>\n",
2131 |        "      <td>839415.0</td>\n",
2132 |        "      <td>4.631</td>\n",
2133 |        "      <td>0.650</td>\n",
2134 |        "      <td>2009</td>\n",
2135 |        "      <td>206</td>\n",
2136 |        "      <td>-165</td>\n",
2137 |        "    </tr>\n",
2138 |        "    <tr>\n",
2139 |        "      <th>74</th>\n",
2140 |        "      <td>亚当·艾略特</td>\n",
2141 |        "      <td>玛丽和马克思</td>\n",
2142 |        "      <td>75</td>\n",
2143 |        "      <td>8.9</td>\n",
2144 |        "      <td>230061</td>\n",
2145 |        "      <td>80</td>\n",
2146 |        "      <td>1023541.0</td>\n",
2147 |        "      <td>4.449</td>\n",
2148 |        "      <td>0.705</td>\n",
2149 |        "      <td>2009</td>\n",
2150 |        "      <td>40</td>\n",
2151 |        "      <td>35</td>\n",
2152 |        "    </tr>\n",
2153 |        "    <tr>\n",
2154 |        "      <th>98</th>\n",
2155 |        "      <td>詹姆斯·卡梅隆</td>\n",
2156 |        "      <td>阿凡达</td>\n",
2157 |        "      <td>99</td>\n",
2158 |        "      <td>8.6</td>\n",
2159 |        "      <td>516658</td>\n",
2160 |        "      <td>162</td>\n",
2161 |        "      <td>2221112.0</td>\n",
2162 |        "      <td>4.299</td>\n",
2163 |        "      <td>0.785</td>\n",
2164 |        "      <td>2009</td>\n",
2165 |        "      <td>222</td>\n",
2166 |        "      <td>-123</td>\n",
2167 |        "    </tr>\n",
2168 |        "    <tr>\n",
2169 |        "      <th>111</th>\n",
2170 |        "      <td>雅克·贝汉</td>\n",
2171 |        "      <td>海洋</td>\n",
2172 |        "      <td>112</td>\n",
2173 |        "      <td>9.0</td>\n",
2174 |        "      <td>97371</td>\n",
2175 |        "      <td>104</td>\n",
2176 |        "      <td>438656.0</td>\n",
2177 |        "      <td>4.505</td>\n",
2178 |        "      <td>0.671</td>\n",
2179 |        "      <td>2009</td>\n",
2180 |        "      <td>39</td>\n",
2181 |        "      <td>73</td>\n",
2182 |        "    </tr>\n",
2183 |        "    <tr>\n",
2184 |        "      <th>178</th>\n",
2185 |        "      <td>克里斯托弗·史密斯</td>\n",
2186 |        "      <td>恐怖游轮</td>\n",
2187 |        "      <td>179</td>\n",
2188 |        "      <td>8.3</td>\n",
2189 |        "      <td>303107</td>\n",
2190 |        "      <td>99</td>\n",
2191 |        "      <td>1261228.0</td>\n",
2192 |        "      <td>4.161</td>\n",
2193 |        "      <td>0.786</td>\n",
2194 |        "      <td>2009</td>\n",
2195 |        "      <td>211</td>\n",
2196 |        "      <td>-32</td>\n",
2197 |        "    </tr>\n",
2198 |        "    <tr>\n",
2199 |        "      <th>182</th>\n",
2200 |        "      <td>理查德·柯蒂斯</td>\n",
2201 |        "      <td>海盗电台</td>\n",
2202 |        "      <td>183</td>\n",
2203 |        "      <td>8.6</td>\n",
2204 |        "      <td>160607</td>\n",
2205 |        "      <td>116</td>\n",
2206 |        "      <td>692376.0</td>\n",
2207 |        "      <td>4.311</td>\n",
2208 |        "      <td>0.796</td>\n",
2209 |        "      <td>2009</td>\n",
2210 |        "      <td>240</td>\n",
2211 |        "      <td>-57</td>\n",
2212 |        "    </tr>\n",
2213 |        "    <tr>\n",
2214 |        "      <th>216</th>\n",
2215 |        "      <td>昆汀·塔伦蒂诺</td>\n",
2216 |        "      <td>无耻混蛋</td>\n",
2217 |        "      <td>217</td>\n",
2218 |        "      <td>8.4</td>\n",
2219 |        "      <td>213168</td>\n",
2220 |        "      <td>153</td>\n",
2221 |        "      <td>896158.0</td>\n",
2222 |        "      <td>4.204</td>\n",
2223 |        "      <td>0.759</td>\n",
2224 |        "      <td>2009</td>\n",
2225 |        "      <td>82</td>\n",
2226 |        "      <td>135</td>\n",
2227 |        "    </tr>\n",
2228 |        "    <tr>\n",
2229 |        "      <th>236</th>\n",
2230 |        "      <td>洛朗·蒂拉尔</td>\n",
2231 |        "      <td>巴黎淘气帮</td>\n",
2232 |        "      <td>237</td>\n",
2233 |        "      <td>8.6</td>\n",
2234 |        "      <td>101307</td>\n",
2235 |        "      <td>92</td>\n",
2236 |        "      <td>436835.0</td>\n",
2237 |        "      <td>4.312</td>\n",
2238 |        "      <td>0.707</td>\n",
2239 |        "      <td>2009</td>\n",
2240 |        "      <td>236</td>\n",
2241 |        "      <td>1</td>\n",
2242 |        "    </tr>\n",
2243 |        "    <tr>\n",
2244 |        "      <th>241</th>\n",
2245 |        "      <td>邓肯·琼斯</td>\n",
2246 |        "      <td>月球</td>\n",
2247 |        "      <td>242</td>\n",
2248 |        "      <td>8.5</td>\n",
2249 |        "      <td>135878</td>\n",
2250 |        "      <td>97</td>\n",
2251 |        "      <td>576122.0</td>\n",
2252 |        "      <td>4.240</td>\n",
2253 |        "      <td>0.719</td>\n",
2254 |        "      <td>2009</td>\n",
2255 |        "      <td>250</td>\n",
2256 |        "      <td>-8</td>\n",
2257 |        "    </tr>\n",
2258 |        "  </tbody>\n",
2259 |        "</table>\n",
2260 |        "</div>"
2261 |       ],
2262 |       "text/plain": [
2263 |        "      director movie_name  rank  score  score_num  time  total_stars  wa_star  \\\n",
2264 |        "11   拉吉库马尔·希拉尼    三傻大闹宝莱坞    12    9.1     675424   171    3085336.0    4.568   \n",
2265 |        "12    拉斯·霍尔斯道姆    忠犬八公的故事    13    9.2     456513    93    2103611.0    4.608   \n",
2266 |        "37      彼特·道格特      飞屋环游记    38    8.9     496926    96    2208836.0    4.445   \n",
2267 |        "40     路易·西霍尤斯        海豚湾    41    9.3     181260    92     839415.0    4.631   \n",
2268 |        "74      亚当·艾略特     玛丽和马克思    75    8.9     230061    80    1023541.0    4.449   \n",
2269 |        "98     詹姆斯·卡梅隆        阿凡达    99    8.6     516658   162    2221112.0    4.299   \n",
2270 |        "111      雅克·贝汉         海洋   112    9.0      97371   104     438656.0    4.505   \n",
2271 |        "178  克里斯托弗·史密斯       恐怖游轮   179    8.3     303107    99    1261228.0    4.161   \n",
2272 |        "182    理查德·柯蒂斯       海盗电台   183    8.6     160607   116     692376.0    4.311   \n",
2273 |        "216    昆汀·塔伦蒂诺       无耻混蛋   217    8.4     213168   153     896158.0    4.204   \n",
2274 |        "236     洛朗·蒂拉尔      巴黎淘气帮   237    8.6     101307    92     436835.0    4.312   \n",
2275 |        "241      邓肯·琼斯         月球   242    8.5     135878    97     576122.0    4.240   \n",
2276 |        "\n",
2277 |        "     wa_star_std  year  order  diff  \n",
2278 |        "11         0.687  2009     27   -15  \n",
2279 |        "12         0.625  2009     22    -9  \n",
2280 |        "37         0.677  2009    126   -88  \n",
2281 |        "40         0.650  2009    206  -165  \n",
2282 |        "74         0.705  2009     40    35  \n",
2283 |        "98         0.785  2009    222  -123  \n",
2284 |        "111        0.671  2009     39    73  \n",
2285 |        "178        0.786  2009    211   -32  \n",
2286 |        "182        0.796  2009    240   -57  \n",
2287 |        "216        0.759  2009     82   135  \n",
2288 |        "236        0.707  2009    236     1  \n",
2289 |        "241        0.719  2009    250    -8  "
2290 |       ]
2291 |      },
2292 |      "execution_count": 155,
2293 |      "metadata": {},
2294 |      "output_type": "execute_result"
2295 |     }
2296 |    ],
2297 |    "source": [
2298 |     "df[df.year==2009]"
2299 |    ]
2300 |   },
2301 |   {
2302 |    "cell_type": "code",
2303 |    "execution_count": 231,
2304 |    "metadata": {},
2305 |    "outputs": [
2306 |     {
2307 |      "data": {
2308 |       "text/plain": [
2309 |        "<matplotlib.text.Text at 0x19df44a8>"
2310 |       ]
2311 |      },
2312 |      "execution_count": 231,
2313 |      "metadata": {},
2314 |      "output_type": "execute_result"
2315 |     },
2316 |     {
2317 |      "data": {
2318 |       "image/png": 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4vKPTQ8Xr0ltwTmnnrVX3mSwf9OZm6OJ6/PCToY6bDVc/PdzeO72L5pdba6csuf9Ph3G3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\ncrlcLpfL5XINmf4fi/E6UXjhz0AAAAAASUVORK5CYII=\n",
2319 |       "text/plain": [
2320 |        "<matplotlib.figure.Figure at 0x19e3aa20>"
2321 |       ]
2322 |      },
2323 |      "metadata": {},
2324 |      "output_type": "display_data"
2325 |     }
2326 |    ],
2327 |    "source": [
2328 |     "year_df.plot(kind='bar',legend=False)\n",
2329 |     "plt.ylabel('number of movies in this year',size=16)\n",
2330 |     "plt.title('numer of movies from year 1931 to 2016',size=20)"
2331 |    ]
2332 |   },
2333 |   {
2334 |    "cell_type": "code",
2335 |    "execution_count": 226,
2336 |    "metadata": {},
2337 |    "outputs": [
2338 |     {
2339 |      "data": {
2340 |       "text/plain": [
2341 |        "<matplotlib.text.Text at 0x1a18ea20>"
2342 |       ]
2343 |      },
2344 |      "execution_count": 226,
2345 |      "metadata": {},
2346 |      "output_type": "execute_result"
2347 |     },
2348 |     {
2349 |      "data": {
2350 |       "image/png": 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tLgY+BmwIrN+R6iRJkiRJXVF3gpvVga9l5nHAOcB8mXliZn4SOA+Y2KkCJUmS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\nklTzPzQBQX0oG9+bAAAAAElFTkSuQmCC\n",
2351 |       "text/plain": [
2352 |        "<matplotlib.figure.Figure at 0x1c2a1eb8>"
2353 |       ]
2354 |      },
2355 |      "metadata": {},
2356 |      "output_type": "display_data"
2357 |     }
2358 |    ],
2359 |    "source": [
2360 |     "year_av_score = {x:df[df.year==x]['score'].mean() for x in set(year)}\n",
2361 |     "year_score_df = pd.DataFrame.from_dict(year_av_score,orient='index')\n",
2362 |     "year_score_df.columns = ['av_score']\n",
2363 |     "year_score_df.plot(kind='bar',legend=False)\n",
2364 |     "plt.ylim(8,10)\n",
2365 |     "plt.ylabel('Average score of movies in the year',size=16)\n",
2366 |     "plt.title('The average number of movie scores each year',size=20)"
2367 |    ]
2368 |   },
2369 |   {
2370 |    "cell_type": "markdown",
2371 |    "metadata": {},
2372 |    "source": [
2373 |     "# 2.Regression Analysis"
2374 |    ]
2375 |   },
2376 |   {
2377 |    "cell_type": "code",
2378 |    "execution_count": 9,
2379 |    "metadata": {},
2380 |    "outputs": [
2381 |     {
2382 |      "data": {
2383 |       "text/plain": [
2384 |        "<matplotlib.text.Text at 0xb6e3630>"
2385 |       ]
2386 |      },
2387 |      "execution_count": 9,
2388 |      "metadata": {},
2389 |      "output_type": "execute_result"
2390 |     },
2391 |     {
2392 |      "data": {
2393 |       "image/png": 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jIiKJ3Hfffaxdu5YFCxZE5hEZYzj++OP505/+xBe/+MUBH2PHjh1ce+21XHbZ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\n",
2394 |       "text/plain": [
2395 |        "<matplotlib.figure.Figure at 0xb6e3588>"
2396 |       ]
2397 |      },
2398 |      "metadata": {},
2399 |      "output_type": "display_data"
2400 |     }
2401 |    ],
2402 |    "source": [
2403 |     "# First, let's take a look at the relationship between the average star rating of the movie and the standard deviation of the star rating\n",
2404 |     "\n",
2405 |     "was = np.array(df['wa_star'])\n",
2406 |     "wa_std = np.array(df['wa_star_std'])\n",
2407 |     "\n",
2408 |     "sns.regplot(was,wa_std,order=2)\n",
2409 |     "plt.xlabel('the weighted average of star ratings',size=16)\n",
2410 |     "plt.ylabel('the standard deviation of star rating',size=16)\n",
2411 |     "plt.title(' relationship between wa_star and wa_star_std',size=20)\n",
2412 |     "plt.text(4.45,0.85,r'$y=-0.491x^2+3.975x-7.274,\\ R^2=0.741$',size=16)"
2413 |    ]
2414 |   },
2415 |   {
2416 |    "cell_type": "code",
2417 |    "execution_count": 170,
2418 |    "metadata": {},
2419 |    "outputs": [
2420 |     {
2421 |      "name": "stdout",
2422 |      "output_type": "stream",
2423 |      "text": [
2424 |       "                            OLS Regression Results                            \n",
2425 |       "==============================================================================\n",
2426 |       "Dep. Variable:            wa_star_std   R-squared:                       0.741\n",
2427 |       "Model:                            OLS   Adj. R-squared:                  0.739\n",
2428 |       "Method:                 Least Squares   F-statistic:                     353.4\n",
2429 |       "Date:                Tue, 19 Sep 2017   Prob (F-statistic):           3.42e-73\n",
2430 |       "Time:                        17:01:24   Log-Likelihood:                 520.03\n",
2431 |       "No. Observations:                 250   AIC:                            -1034.\n",
2432 |       "Df Residuals:                     247   BIC:                            -1023.\n",
2433 |       "Df Model:                           2                                         \n",
2434 |       "Covariance Type:            nonrobust                                         \n",
2435 |       "==============================================================================\n",
2436 |       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
2437 |       "------------------------------------------------------------------------------\n",
2438 |       "cosnt         -7.2736      1.575     -4.617      0.000     -10.376      -4.171\n",
2439 |       "was            3.9747      0.713      5.574      0.000       2.570       5.379\n",
2440 |       "was_sq        -0.4910      0.081     -6.088      0.000      -0.650      -0.332\n",
2441 |       "==============================================================================\n",
2442 |       "Omnibus:                       32.946   Durbin-Watson:                   1.948\n",
2443 |       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               47.397\n",
2444 |       "Skew:                           0.817   Prob(JB):                     5.10e-11\n",
2445 |       "Kurtosis:                       4.371   Cond. No.                     1.78e+04\n",
2446 |       "==============================================================================\n",
2447 |       "\n",
2448 |       "Warnings:\n",
2449 |       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
2450 |       "[2] The condition number is large, 1.78e+04. This might indicate that there are\n",
2451 |       "strong multicollinearity or other numerical problems.\n"
2452 |      ]
2453 |     }
2454 |    ],
2455 |    "source": [
2456 |     "def reg(y,yname,xname,*args):\n",
2457 |     "    import statsmodels.api as sm\n",
2458 |     "    x = np.vstack((args)).T\n",
2459 |     "    mat_x = sm.add_constant(x)\n",
2460 |     "    res = sm.OLS(y,mat_x).fit()\n",
2461 |     "    print(res.summary(yname=yname,xname=['cosnt']+xname))\n",
2462 |     "\n",
2463 |     "# was_sq is the squre of the weighted average star rating\n",
2464 |     "\n",
2465 |     "was_sq = was**2\n",
2466 |     "reg(wa_std,'wa_star_std',['was','was_sq'],was,was_sq)"
2467 |    ]
2468 |   },
2469 |   {
2470 |    "cell_type": "code",
2471 |    "execution_count": null,
2472 |    "metadata": {
2473 |     "collapsed": true
2474 |    },
2475 |    "outputs": [],
2476 |    "source": []
2477 |   }
2478 |  ],
2479 |  "metadata": {
2480 |   "anaconda-cloud": {},
2481 |   "kernelspec": {
2482 |    "display_name": "Python 3",
2483 |    "language": "python",
2484 |    "name": "python3"
2485 |   },
2486 |   "language_info": {
2487 |    "codemirror_mode": {
2488 |     "name": "ipython",
2489 |     "version": 3
2490 |    },
2491 |    "file_extension": ".py",
2492 |    "mimetype": "text/x-python",
2493 |    "name": "python",
2494 |    "nbconvert_exporter": "python",
2495 |    "pygments_lexer": "ipython3",
2496 |    "version": "3.6.1"
2497 |   }
2498 |  },
2499 |  "nbformat": 4,
2500 |  "nbformat_minor": 1
2501 | }
2502 | 


--------------------------------------------------------------------------------