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@ischurov
Created Mar 7, 2022
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Lesson14
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "## Наука о данных\n### Совместный бакалавриат ВШЭ-РЭШ, 2021-2022 учебный год\n_Илья Щуров_\n\n[Страница курса](http://math-info.hse.ru/s21/j)"
},
{
"metadata": {
"trusted": false
},
"id": "amended-entertainment",
"cell_type": "code",
"source": "import pandas as pd",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "printable-father",
"cell_type": "code",
"source": "df = pd.read_csv(\n \"https://github.com/Godoy/imdb-5000-movie-dataset/raw/master/data/movie_metadata.csv\"\n)",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "complete-slope",
"cell_type": "code",
"source": "df.columns",
"execution_count": 4,
"outputs": [
{
"data": {
"text/plain": "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n dtype='object')"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "historical-ensemble",
"cell_type": "code",
"source": "df.shape",
"execution_count": 5,
"outputs": [
{
"data": {
"text/plain": "(5043, 28)"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "united-singing",
"cell_type": "code",
"source": "df.dtypes",
"execution_count": 6,
"outputs": [
{
"data": {
"text/plain": "color object\ndirector_name object\nnum_critic_for_reviews float64\nduration float64\ndirector_facebook_likes float64\nactor_3_facebook_likes float64\nactor_2_name object\nactor_1_facebook_likes float64\ngross float64\ngenres object\nactor_1_name object\nmovie_title object\nnum_voted_users int64\ncast_total_facebook_likes int64\nactor_3_name object\nfacenumber_in_poster float64\nplot_keywords object\nmovie_imdb_link object\nnum_user_for_reviews float64\nlanguage object\ncountry object\ncontent_rating object\nbudget float64\ntitle_year float64\nactor_2_facebook_likes float64\nimdb_score float64\naspect_ratio float64\nmovie_facebook_likes int64\ndtype: object"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "controlled-monte",
"cell_type": "code",
"source": "df[\"color\"].unique()",
"execution_count": 8,
"outputs": [
{
"data": {
"text/plain": "array(['Color', nan, ' Black and White'], dtype=object)"
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "juvenile-happiness",
"cell_type": "code",
"source": "df[\"color\"].value_counts()",
"execution_count": 9,
"outputs": [
{
"data": {
"text/plain": "Color 4815\n Black and White 209\nName: color, dtype: int64"
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "coordinated-advisory",
"cell_type": "code",
"source": "df[\"color\"].value_counts(dropna=False)",
"execution_count": 10,
"outputs": [
{
"data": {
"text/plain": "Color 4815\n Black and White 209\nNaN 19\nName: color, dtype: int64"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "satisfactory-insight",
"cell_type": "code",
"source": "### Найти все чёрно-белые фильмы",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "static-hacker",
"cell_type": "code",
"source": "df",
"execution_count": 12,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>color</th>\n <th>director_name</th>\n <th>num_critic_for_reviews</th>\n <th>duration</th>\n <th>director_facebook_likes</th>\n <th>actor_3_facebook_likes</th>\n <th>actor_2_name</th>\n <th>actor_1_facebook_likes</th>\n <th>gross</th>\n <th>genres</th>\n <th>...</th>\n <th>num_user_for_reviews</th>\n <th>language</th>\n <th>country</th>\n <th>content_rating</th>\n <th>budget</th>\n <th>title_year</th>\n <th>actor_2_facebook_likes</th>\n <th>imdb_score</th>\n <th>aspect_ratio</th>\n <th>movie_facebook_likes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Color</td>\n <td>James Cameron</td>\n <td>723.0</td>\n <td>178.0</td>\n <td>0.0</td>\n <td>855.0</td>\n <td>Joel David Moore</td>\n <td>1000.0</td>\n <td>760505847.0</td>\n <td>Action|Adventure|Fantasy|Sci-Fi</td>\n <td>...</td>\n <td>3054.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>237000000.0</td>\n <td>2009.0</td>\n <td>936.0</td>\n <td>7.9</td>\n <td>1.78</td>\n <td>33000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Color</td>\n <td>Gore Verbinski</td>\n <td>302.0</td>\n <td>169.0</td>\n <td>563.0</td>\n <td>1000.0</td>\n <td>Orlando Bloom</td>\n <td>40000.0</td>\n <td>309404152.0</td>\n <td>Action|Adventure|Fantasy</td>\n <td>...</td>\n <td>1238.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>300000000.0</td>\n <td>2007.0</td>\n <td>5000.0</td>\n <td>7.1</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Color</td>\n <td>Sam Mendes</td>\n <td>602.0</td>\n <td>148.0</td>\n <td>0.0</td>\n <td>161.0</td>\n <td>Rory Kinnear</td>\n <td>11000.0</td>\n <td>200074175.0</td>\n <td>Action|Adventure|Thriller</td>\n <td>...</td>\n <td>994.0</td>\n <td>English</td>\n <td>UK</td>\n <td>PG-13</td>\n <td>245000000.0</td>\n <td>2015.0</td>\n <td>393.0</td>\n <td>6.8</td>\n <td>2.35</td>\n <td>85000</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Color</td>\n <td>Christopher Nolan</td>\n <td>813.0</td>\n <td>164.0</td>\n <td>22000.0</td>\n <td>23000.0</td>\n <td>Christian Bale</td>\n <td>27000.0</td>\n <td>448130642.0</td>\n <td>Action|Thriller</td>\n <td>...</td>\n <td>2701.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>250000000.0</td>\n <td>2012.0</td>\n <td>23000.0</td>\n <td>8.5</td>\n <td>2.35</td>\n <td>164000</td>\n </tr>\n <tr>\n <th>4</th>\n <td>NaN</td>\n <td>Doug Walker</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>131.0</td>\n <td>NaN</td>\n <td>Rob Walker</td>\n <td>131.0</td>\n <td>NaN</td>\n <td>Documentary</td>\n <td>...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>12.0</td>\n <td>7.1</td>\n <td>NaN</td>\n <td>0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>5038</th>\n <td>Color</td>\n <td>Scott Smith</td>\n <td>1.0</td>\n <td>87.0</td>\n <td>2.0</td>\n <td>318.0</td>\n <td>Daphne Zuniga</td>\n <td>637.0</td>\n <td>NaN</td>\n <td>Comedy|Drama</td>\n <td>...</td>\n <td>6.0</td>\n <td>English</td>\n <td>Canada</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>2013.0</td>\n <td>470.0</td>\n <td>7.7</td>\n <td>NaN</td>\n <td>84</td>\n </tr>\n <tr>\n <th>5039</th>\n <td>Color</td>\n <td>NaN</td>\n <td>43.0</td>\n <td>43.0</td>\n <td>NaN</td>\n <td>319.0</td>\n <td>Valorie Curry</td>\n <td>841.0</td>\n <td>NaN</td>\n <td>Crime|Drama|Mystery|Thriller</td>\n <td>...</td>\n <td>359.0</td>\n <td>English</td>\n <td>USA</td>\n <td>TV-14</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>593.0</td>\n <td>7.5</td>\n <td>16.00</td>\n <td>32000</td>\n </tr>\n <tr>\n <th>5040</th>\n <td>Color</td>\n <td>Benjamin Roberds</td>\n <td>13.0</td>\n <td>76.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>Maxwell Moody</td>\n <td>0.0</td>\n <td>NaN</td>\n <td>Drama|Horror|Thriller</td>\n <td>...</td>\n <td>3.0</td>\n <td>English</td>\n <td>USA</td>\n <td>NaN</td>\n <td>1400.0</td>\n <td>2013.0</td>\n <td>0.0</td>\n <td>6.3</td>\n <td>NaN</td>\n <td>16</td>\n </tr>\n <tr>\n <th>5041</th>\n <td>Color</td>\n <td>Daniel Hsia</td>\n <td>14.0</td>\n <td>100.0</td>\n <td>0.0</td>\n <td>489.0</td>\n <td>Daniel Henney</td>\n <td>946.0</td>\n <td>10443.0</td>\n <td>Comedy|Drama|Romance</td>\n <td>...</td>\n <td>9.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>NaN</td>\n <td>2012.0</td>\n <td>719.0</td>\n <td>6.3</td>\n <td>2.35</td>\n <td>660</td>\n </tr>\n <tr>\n <th>5042</th>\n <td>Color</td>\n <td>Jon Gunn</td>\n <td>43.0</td>\n <td>90.0</td>\n <td>16.0</td>\n <td>16.0</td>\n <td>Brian Herzlinger</td>\n <td>86.0</td>\n <td>85222.0</td>\n <td>Documentary</td>\n <td>...</td>\n <td>84.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG</td>\n <td>1100.0</td>\n <td>2004.0</td>\n <td>23.0</td>\n <td>6.6</td>\n <td>1.85</td>\n <td>456</td>\n </tr>\n </tbody>\n</table>\n<p>5043 rows × 28 columns</p>\n</div>",
"text/plain": " color director_name num_critic_for_reviews duration \\\n0 Color James Cameron 723.0 178.0 \n1 Color Gore Verbinski 302.0 169.0 \n2 Color Sam Mendes 602.0 148.0 \n3 Color Christopher Nolan 813.0 164.0 \n4 NaN Doug Walker NaN NaN \n... ... ... ... ... \n5038 Color Scott Smith 1.0 87.0 \n5039 Color NaN 43.0 43.0 \n5040 Color Benjamin Roberds 13.0 76.0 \n5041 Color Daniel Hsia 14.0 100.0 \n5042 Color Jon Gunn 43.0 90.0 \n\n director_facebook_likes actor_3_facebook_likes actor_2_name \\\n0 0.0 855.0 Joel David Moore \n1 563.0 1000.0 Orlando Bloom \n2 0.0 161.0 Rory Kinnear \n3 22000.0 23000.0 Christian Bale \n4 131.0 NaN Rob Walker \n... ... ... ... \n5038 2.0 318.0 Daphne Zuniga \n5039 NaN 319.0 Valorie Curry \n5040 0.0 0.0 Maxwell Moody \n5041 0.0 489.0 Daniel Henney \n5042 16.0 16.0 Brian Herzlinger \n\n actor_1_facebook_likes gross genres \\\n0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n1 40000.0 309404152.0 Action|Adventure|Fantasy \n2 11000.0 200074175.0 Action|Adventure|Thriller \n3 27000.0 448130642.0 Action|Thriller \n4 131.0 NaN Documentary \n... ... ... ... \n5038 637.0 NaN Comedy|Drama \n5039 841.0 NaN Crime|Drama|Mystery|Thriller \n5040 0.0 NaN Drama|Horror|Thriller \n5041 946.0 10443.0 Comedy|Drama|Romance \n5042 86.0 85222.0 Documentary \n\n ... num_user_for_reviews language country content_rating budget \\\n0 ... 3054.0 English USA PG-13 237000000.0 \n1 ... 1238.0 English USA PG-13 300000000.0 \n2 ... 994.0 English UK PG-13 245000000.0 \n3 ... 2701.0 English USA PG-13 250000000.0 \n4 ... NaN NaN NaN NaN NaN \n... ... ... ... ... ... ... \n5038 ... 6.0 English Canada NaN NaN \n5039 ... 359.0 English USA TV-14 NaN \n5040 ... 3.0 English USA NaN 1400.0 \n5041 ... 9.0 English USA PG-13 NaN \n5042 ... 84.0 English USA PG 1100.0 \n\n title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n0 2009.0 936.0 7.9 1.78 \n1 2007.0 5000.0 7.1 2.35 \n2 2015.0 393.0 6.8 2.35 \n3 2012.0 23000.0 8.5 2.35 \n4 NaN 12.0 7.1 NaN \n... ... ... ... ... \n5038 2013.0 470.0 7.7 NaN \n5039 NaN 593.0 7.5 16.00 \n5040 2013.0 0.0 6.3 NaN \n5041 2012.0 719.0 6.3 2.35 \n5042 2004.0 23.0 6.6 1.85 \n\n movie_facebook_likes \n0 33000 \n1 0 \n2 85000 \n3 164000 \n4 0 \n... ... \n5038 84 \n5039 32000 \n5040 16 \n5041 660 \n5042 456 \n\n[5043 rows x 28 columns]"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "eight-iraqi",
"cell_type": "code",
"source": "df[df[\"color\"] == \" Black and White\"]",
"execution_count": 14,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>color</th>\n <th>director_name</th>\n <th>num_critic_for_reviews</th>\n <th>duration</th>\n <th>director_facebook_likes</th>\n <th>actor_3_facebook_likes</th>\n <th>actor_2_name</th>\n <th>actor_1_facebook_likes</th>\n <th>gross</th>\n <th>genres</th>\n <th>...</th>\n <th>num_user_for_reviews</th>\n <th>language</th>\n <th>country</th>\n <th>content_rating</th>\n <th>budget</th>\n <th>title_year</th>\n <th>actor_2_facebook_likes</th>\n <th>imdb_score</th>\n <th>aspect_ratio</th>\n <th>movie_facebook_likes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>111</th>\n <td>Black and White</td>\n <td>Michael Bay</td>\n <td>191.0</td>\n <td>184.0</td>\n <td>0.0</td>\n <td>691.0</td>\n <td>Jaime King</td>\n <td>3000.0</td>\n <td>198539855.0</td>\n <td>Action|Drama|History|Romance|War</td>\n <td>...</td>\n <td>1999.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>140000000.0</td>\n <td>2001.0</td>\n <td>961.0</td>\n <td>6.1</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>149</th>\n <td>Black and White</td>\n <td>Lee Tamahori</td>\n <td>264.0</td>\n <td>133.0</td>\n <td>93.0</td>\n <td>746.0</td>\n <td>Colin Salmon</td>\n <td>769.0</td>\n <td>160201106.0</td>\n <td>Action|Adventure|Thriller</td>\n <td>...</td>\n <td>1185.0</td>\n <td>English</td>\n <td>UK</td>\n <td>PG-13</td>\n <td>142000000.0</td>\n <td>2002.0</td>\n <td>766.0</td>\n <td>6.1</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>257</th>\n <td>Black and White</td>\n <td>Martin Scorsese</td>\n <td>267.0</td>\n <td>170.0</td>\n <td>17000.0</td>\n <td>827.0</td>\n <td>Adam Scott</td>\n <td>29000.0</td>\n <td>102608827.0</td>\n <td>Biography|Drama</td>\n <td>...</td>\n <td>799.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>110000000.0</td>\n <td>2004.0</td>\n <td>3000.0</td>\n <td>7.5</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>272</th>\n <td>Black and White</td>\n <td>Michael Mann</td>\n <td>174.0</td>\n <td>165.0</td>\n <td>0.0</td>\n <td>780.0</td>\n <td>Jada Pinkett Smith</td>\n <td>10000.0</td>\n <td>58183966.0</td>\n <td>Biography|Drama|Sport</td>\n <td>...</td>\n <td>386.0</td>\n <td>English</td>\n <td>USA</td>\n <td>R</td>\n <td>107000000.0</td>\n <td>2001.0</td>\n <td>851.0</td>\n <td>6.8</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>286</th>\n <td>Black and White</td>\n <td>Martin Campbell</td>\n <td>400.0</td>\n <td>144.0</td>\n <td>258.0</td>\n <td>834.0</td>\n <td>Tobias Menzies</td>\n <td>6000.0</td>\n <td>167007184.0</td>\n <td>Action|Adventure|Thriller</td>\n <td>...</td>\n <td>2301.0</td>\n <td>English</td>\n <td>UK</td>\n <td>PG-13</td>\n <td>150000000.0</td>\n <td>2006.0</td>\n <td>1000.0</td>\n <td>8.0</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>5005</th>\n <td>Black and White</td>\n <td>Andrew Bujalski</td>\n <td>52.0</td>\n <td>109.0</td>\n <td>26.0</td>\n <td>3.0</td>\n <td>Kate Dollenmayer</td>\n <td>26.0</td>\n <td>NaN</td>\n <td>Comedy</td>\n <td>...</td>\n <td>23.0</td>\n <td>English</td>\n <td>USA</td>\n <td>R</td>\n <td>NaN</td>\n <td>2005.0</td>\n <td>6.0</td>\n <td>6.9</td>\n <td>1.66</td>\n <td>91</td>\n </tr>\n <tr>\n <th>5008</th>\n <td>Black and White</td>\n <td>Kevin Smith</td>\n <td>136.0</td>\n <td>102.0</td>\n <td>0.0</td>\n <td>216.0</td>\n <td>Brian O'Halloran</td>\n <td>898.0</td>\n <td>3151130.0</td>\n <td>Comedy</td>\n <td>...</td>\n <td>615.0</td>\n <td>English</td>\n <td>USA</td>\n <td>R</td>\n <td>230000.0</td>\n <td>1994.0</td>\n <td>657.0</td>\n <td>7.8</td>\n <td>1.37</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5015</th>\n <td>Black and White</td>\n <td>Richard Linklater</td>\n <td>61.0</td>\n <td>100.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>Richard Linklater</td>\n <td>5.0</td>\n <td>1227508.0</td>\n <td>Comedy|Drama</td>\n <td>...</td>\n <td>80.0</td>\n <td>English</td>\n <td>USA</td>\n <td>R</td>\n <td>23000.0</td>\n <td>1991.0</td>\n <td>0.0</td>\n <td>7.1</td>\n <td>1.37</td>\n <td>2000</td>\n </tr>\n <tr>\n <th>5022</th>\n <td>Black and White</td>\n <td>Jim Chuchu</td>\n <td>6.0</td>\n <td>60.0</td>\n <td>0.0</td>\n <td>4.0</td>\n <td>Olwenya Maina</td>\n <td>147.0</td>\n <td>NaN</td>\n <td>Drama</td>\n <td>...</td>\n <td>1.0</td>\n <td>Swahili</td>\n <td>Kenya</td>\n <td>NaN</td>\n <td>15000.0</td>\n <td>2014.0</td>\n <td>19.0</td>\n <td>7.4</td>\n <td>NaN</td>\n <td>45</td>\n </tr>\n <tr>\n <th>5028</th>\n <td>Black and White</td>\n <td>Ivan Kavanagh</td>\n <td>12.0</td>\n <td>83.0</td>\n <td>18.0</td>\n <td>0.0</td>\n <td>Michael Parle</td>\n <td>10.0</td>\n <td>NaN</td>\n <td>Horror</td>\n <td>...</td>\n <td>1.0</td>\n <td>English</td>\n <td>Ireland</td>\n <td>NaN</td>\n <td>10000.0</td>\n <td>2007.0</td>\n <td>5.0</td>\n <td>6.7</td>\n <td>1.33</td>\n <td>105</td>\n </tr>\n </tbody>\n</table>\n<p>209 rows × 28 columns</p>\n</div>",
"text/plain": " color director_name num_critic_for_reviews duration \\\n111 Black and White Michael Bay 191.0 184.0 \n149 Black and White Lee Tamahori 264.0 133.0 \n257 Black and White Martin Scorsese 267.0 170.0 \n272 Black and White Michael Mann 174.0 165.0 \n286 Black and White Martin Campbell 400.0 144.0 \n... ... ... ... ... \n5005 Black and White Andrew Bujalski 52.0 109.0 \n5008 Black and White Kevin Smith 136.0 102.0 \n5015 Black and White Richard Linklater 61.0 100.0 \n5022 Black and White Jim Chuchu 6.0 60.0 \n5028 Black and White Ivan Kavanagh 12.0 83.0 \n\n director_facebook_likes actor_3_facebook_likes actor_2_name \\\n111 0.0 691.0 Jaime King \n149 93.0 746.0 Colin Salmon \n257 17000.0 827.0 Adam Scott \n272 0.0 780.0 Jada Pinkett Smith \n286 258.0 834.0 Tobias Menzies \n... ... ... ... \n5005 26.0 3.0 Kate Dollenmayer \n5008 0.0 216.0 Brian O'Halloran \n5015 0.0 0.0 Richard Linklater \n5022 0.0 4.0 Olwenya Maina \n5028 18.0 0.0 Michael Parle \n\n actor_1_facebook_likes gross genres \\\n111 3000.0 198539855.0 Action|Drama|History|Romance|War \n149 769.0 160201106.0 Action|Adventure|Thriller \n257 29000.0 102608827.0 Biography|Drama \n272 10000.0 58183966.0 Biography|Drama|Sport \n286 6000.0 167007184.0 Action|Adventure|Thriller \n... ... ... ... \n5005 26.0 NaN Comedy \n5008 898.0 3151130.0 Comedy \n5015 5.0 1227508.0 Comedy|Drama \n5022 147.0 NaN Drama \n5028 10.0 NaN Horror \n\n ... num_user_for_reviews language country content_rating budget \\\n111 ... 1999.0 English USA PG-13 140000000.0 \n149 ... 1185.0 English UK PG-13 142000000.0 \n257 ... 799.0 English USA PG-13 110000000.0 \n272 ... 386.0 English USA R 107000000.0 \n286 ... 2301.0 English UK PG-13 150000000.0 \n... ... ... ... ... ... ... \n5005 ... 23.0 English USA R NaN \n5008 ... 615.0 English USA R 230000.0 \n5015 ... 80.0 English USA R 23000.0 \n5022 ... 1.0 Swahili Kenya NaN 15000.0 \n5028 ... 1.0 English Ireland NaN 10000.0 \n\n title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n111 2001.0 961.0 6.1 2.35 \n149 2002.0 766.0 6.1 2.35 \n257 2004.0 3000.0 7.5 2.35 \n272 2001.0 851.0 6.8 2.35 \n286 2006.0 1000.0 8.0 2.35 \n... ... ... ... ... \n5005 2005.0 6.0 6.9 1.66 \n5008 1994.0 657.0 7.8 1.37 \n5015 1991.0 0.0 7.1 1.37 \n5022 2014.0 19.0 7.4 NaN \n5028 2007.0 5.0 6.7 1.33 \n\n movie_facebook_likes \n111 0 \n149 0 \n257 0 \n272 0 \n286 0 \n... ... \n5005 91 \n5008 0 \n5015 2000 \n5022 45 \n5028 105 \n\n[209 rows x 28 columns]"
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "handy-background",
"cell_type": "code",
"source": "df[\"color\"].str.strip().unique()",
"execution_count": 19,
"outputs": [
{
"data": {
"text/plain": "array(['Color', nan, 'Black and White'], dtype=object)"
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "timely-bermuda",
"cell_type": "code",
"source": "df.dtypes[df.dtypes == object].index",
"execution_count": 35,
"outputs": [
{
"data": {
"text/plain": "Index(['color', 'director_name', 'actor_2_name', 'genres', 'actor_1_name',\n 'movie_title', 'actor_3_name', 'plot_keywords', 'movie_imdb_link',\n 'language', 'country', 'content_rating'],\n dtype='object')"
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "interested-socket",
"cell_type": "code",
"source": "for column in df.dtypes[df.dtypes == object].index:\n df[column] = df[column].str.strip()",
"execution_count": 29,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "baking-small",
"cell_type": "code",
"source": "df[\"color\"].unique()",
"execution_count": 31,
"outputs": [
{
"data": {
"text/plain": "array(['Color', nan, 'Black and White'], dtype=object)"
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "bored-rwanda",
"cell_type": "code",
"source": "df.columns",
"execution_count": 42,
"outputs": [
{
"data": {
"text/plain": "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n dtype='object')"
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "collective-microphone",
"cell_type": "code",
"source": "df[df.duplicated(keep=False)].sort_values(\"movie_title\")",
"execution_count": 45,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>color</th>\n <th>director_name</th>\n <th>num_critic_for_reviews</th>\n <th>duration</th>\n <th>director_facebook_likes</th>\n <th>actor_3_facebook_likes</th>\n <th>actor_2_name</th>\n <th>actor_1_facebook_likes</th>\n <th>gross</th>\n <th>genres</th>\n <th>...</th>\n <th>num_user_for_reviews</th>\n <th>language</th>\n <th>country</th>\n <th>content_rating</th>\n <th>budget</th>\n <th>title_year</th>\n <th>actor_2_facebook_likes</th>\n <th>imdb_score</th>\n <th>aspect_ratio</th>\n <th>movie_facebook_likes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>4950</th>\n <td>Color</td>\n <td>David Hewlett</td>\n <td>8.0</td>\n <td>88.0</td>\n <td>686.0</td>\n <td>405.0</td>\n <td>David Hewlett</td>\n <td>847.0</td>\n <td>NaN</td>\n <td>Comedy</td>\n <td>...</td>\n <td>46.0</td>\n <td>English</td>\n <td>Canada</td>\n <td>NaN</td>\n <td>120000.0</td>\n <td>2007.0</td>\n <td>686.0</td>\n <td>7.0</td>\n <td>1.78</td>\n <td>377</td>\n </tr>\n <tr>\n <th>4949</th>\n <td>Color</td>\n <td>David Hewlett</td>\n <td>8.0</td>\n <td>88.0</td>\n <td>686.0</td>\n <td>405.0</td>\n <td>David Hewlett</td>\n <td>847.0</td>\n <td>NaN</td>\n <td>Comedy</td>\n <td>...</td>\n <td>46.0</td>\n <td>English</td>\n <td>Canada</td>\n <td>NaN</td>\n <td>120000.0</td>\n <td>2007.0</td>\n <td>686.0</td>\n <td>7.0</td>\n <td>1.78</td>\n <td>377</td>\n </tr>\n <tr>\n <th>4408</th>\n <td>Color</td>\n <td>Yimou Zhang</td>\n <td>101.0</td>\n <td>95.0</td>\n <td>611.0</td>\n <td>3.0</td>\n <td>Ni Yan</td>\n <td>9.0</td>\n <td>190666.0</td>\n <td>Comedy|Drama</td>\n <td>...</td>\n <td>20.0</td>\n <td>Mandarin</td>\n <td>China</td>\n <td>R</td>\n <td>NaN</td>\n <td>2009.0</td>\n <td>4.0</td>\n <td>5.7</td>\n <td>2.35</td>\n <td>784</td>\n </tr>\n <tr>\n <th>3007</th>\n <td>Color</td>\n <td>Yimou Zhang</td>\n <td>101.0</td>\n <td>95.0</td>\n <td>611.0</td>\n <td>3.0</td>\n <td>Ni Yan</td>\n <td>9.0</td>\n <td>190666.0</td>\n <td>Comedy|Drama</td>\n <td>...</td>\n <td>20.0</td>\n <td>Mandarin</td>\n <td>China</td>\n <td>R</td>\n <td>NaN</td>\n <td>2009.0</td>\n <td>4.0</td>\n <td>5.7</td>\n <td>2.35</td>\n <td>784</td>\n </tr>\n <tr>\n <th>2181</th>\n <td>Color</td>\n <td>Jon Lucas</td>\n <td>81.0</td>\n <td>100.0</td>\n <td>24.0</td>\n <td>851.0</td>\n <td>Jay Hernandez</td>\n <td>15000.0</td>\n <td>55461307.0</td>\n <td>Comedy</td>\n <td>...</td>\n <td>46.0</td>\n <td>English</td>\n <td>USA</td>\n <td>R</td>\n <td>20000000.0</td>\n <td>2016.0</td>\n <td>1000.0</td>\n <td>6.7</td>\n <td>NaN</td>\n <td>18000</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>663</th>\n <td>Color</td>\n <td>Angelina Jolie Pitt</td>\n <td>322.0</td>\n <td>137.0</td>\n <td>11000.0</td>\n <td>465.0</td>\n <td>Jack O'Connell</td>\n <td>769.0</td>\n <td>115603980.0</td>\n <td>Biography|Drama|Sport|War</td>\n <td>...</td>\n <td>351.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>65000000.0</td>\n <td>2014.0</td>\n <td>698.0</td>\n <td>7.2</td>\n <td>2.35</td>\n <td>35000</td>\n </tr>\n <tr>\n <th>1146</th>\n <td>Color</td>\n <td>Paul McGuigan</td>\n <td>159.0</td>\n <td>110.0</td>\n <td>118.0</td>\n <td>287.0</td>\n <td>Spencer Wilding</td>\n <td>11000.0</td>\n <td>5773519.0</td>\n <td>Drama|Horror|Sci-Fi|Thriller</td>\n <td>...</td>\n <td>91.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>40000000.0</td>\n <td>2015.0</td>\n <td>1000.0</td>\n <td>6.0</td>\n <td>2.35</td>\n <td>11000</td>\n </tr>\n <tr>\n <th>1305</th>\n <td>Color</td>\n <td>Paul McGuigan</td>\n <td>159.0</td>\n <td>110.0</td>\n <td>118.0</td>\n <td>287.0</td>\n <td>Spencer Wilding</td>\n <td>11000.0</td>\n <td>5773519.0</td>\n <td>Drama|Horror|Sci-Fi|Thriller</td>\n <td>...</td>\n <td>91.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>40000000.0</td>\n <td>2015.0</td>\n <td>1000.0</td>\n <td>6.0</td>\n <td>2.35</td>\n <td>11000</td>\n </tr>\n <tr>\n <th>1697</th>\n <td>Color</td>\n <td>Paul McGuigan</td>\n <td>98.0</td>\n <td>114.0</td>\n <td>118.0</td>\n <td>40.0</td>\n <td>Christopher Cousins</td>\n <td>489.0</td>\n <td>12831121.0</td>\n <td>Drama|Mystery|Romance|Thriller</td>\n <td>...</td>\n <td>298.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>30000000.0</td>\n <td>2004.0</td>\n <td>93.0</td>\n <td>7.0</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2169</th>\n <td>Color</td>\n <td>Paul McGuigan</td>\n <td>98.0</td>\n <td>114.0</td>\n <td>118.0</td>\n <td>40.0</td>\n <td>Christopher Cousins</td>\n <td>489.0</td>\n <td>12831121.0</td>\n <td>Drama|Mystery|Romance|Thriller</td>\n <td>...</td>\n <td>298.0</td>\n <td>English</td>\n <td>USA</td>\n <td>PG-13</td>\n <td>30000000.0</td>\n <td>2004.0</td>\n <td>93.0</td>\n <td>7.0</td>\n <td>2.35</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>90 rows × 28 columns</p>\n</div>",
"text/plain": " color director_name num_critic_for_reviews duration \\\n4950 Color David Hewlett 8.0 88.0 \n4949 Color David Hewlett 8.0 88.0 \n4408 Color Yimou Zhang 101.0 95.0 \n3007 Color Yimou Zhang 101.0 95.0 \n2181 Color Jon Lucas 81.0 100.0 \n... ... ... ... ... \n663 Color Angelina Jolie Pitt 322.0 137.0 \n1146 Color Paul McGuigan 159.0 110.0 \n1305 Color Paul McGuigan 159.0 110.0 \n1697 Color Paul McGuigan 98.0 114.0 \n2169 Color Paul McGuigan 98.0 114.0 \n\n director_facebook_likes actor_3_facebook_likes actor_2_name \\\n4950 686.0 405.0 David Hewlett \n4949 686.0 405.0 David Hewlett \n4408 611.0 3.0 Ni Yan \n3007 611.0 3.0 Ni Yan \n2181 24.0 851.0 Jay Hernandez \n... ... ... ... \n663 11000.0 465.0 Jack O'Connell \n1146 118.0 287.0 Spencer Wilding \n1305 118.0 287.0 Spencer Wilding \n1697 118.0 40.0 Christopher Cousins \n2169 118.0 40.0 Christopher Cousins \n\n actor_1_facebook_likes gross genres \\\n4950 847.0 NaN Comedy \n4949 847.0 NaN Comedy \n4408 9.0 190666.0 Comedy|Drama \n3007 9.0 190666.0 Comedy|Drama \n2181 15000.0 55461307.0 Comedy \n... ... ... ... \n663 769.0 115603980.0 Biography|Drama|Sport|War \n1146 11000.0 5773519.0 Drama|Horror|Sci-Fi|Thriller \n1305 11000.0 5773519.0 Drama|Horror|Sci-Fi|Thriller \n1697 489.0 12831121.0 Drama|Mystery|Romance|Thriller \n2169 489.0 12831121.0 Drama|Mystery|Romance|Thriller \n\n ... num_user_for_reviews language country content_rating budget \\\n4950 ... 46.0 English Canada NaN 120000.0 \n4949 ... 46.0 English Canada NaN 120000.0 \n4408 ... 20.0 Mandarin China R NaN \n3007 ... 20.0 Mandarin China R NaN \n2181 ... 46.0 English USA R 20000000.0 \n... ... ... ... ... ... ... \n663 ... 351.0 English USA PG-13 65000000.0 \n1146 ... 91.0 English USA PG-13 40000000.0 \n1305 ... 91.0 English USA PG-13 40000000.0 \n1697 ... 298.0 English USA PG-13 30000000.0 \n2169 ... 298.0 English USA PG-13 30000000.0 \n\n title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n4950 2007.0 686.0 7.0 1.78 \n4949 2007.0 686.0 7.0 1.78 \n4408 2009.0 4.0 5.7 2.35 \n3007 2009.0 4.0 5.7 2.35 \n2181 2016.0 1000.0 6.7 NaN \n... ... ... ... ... \n663 2014.0 698.0 7.2 2.35 \n1146 2015.0 1000.0 6.0 2.35 \n1305 2015.0 1000.0 6.0 2.35 \n1697 2004.0 93.0 7.0 2.35 \n2169 2004.0 93.0 7.0 2.35 \n\n movie_facebook_likes \n4950 377 \n4949 377 \n4408 784 \n3007 784 \n2181 18000 \n... ... \n663 35000 \n1146 11000 \n1305 11000 \n1697 0 \n2169 0 \n\n[90 rows x 28 columns]"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "asian-danish",
"cell_type": "code",
"source": "df = pd.read_csv(\n \"https://github.com/Godoy/imdb-5000-movie-dataset/raw/master/data/movie_metadata.csv\"\n)\ndf = df.drop_duplicates()",
"execution_count": 53,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "weird-silence",
"cell_type": "code",
"source": "df.shape",
"execution_count": 54,
"outputs": [
{
"data": {
"text/plain": "(4998, 28)"
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "unlike-florida",
"cell_type": "code",
"source": "pd.set_option(\"display.max_rows\", None)",
"execution_count": 55,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"id": "sacred-commonwealth",
"cell_type": "code",
"source": "(\n df[df.duplicated(subset=\"movie_title\", keep=False)].sort_values(\"movie_title\")[\n [\"director_name\", \"movie_title\", \"title_year\"]\n ]\n)",
"execution_count": 56,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>director_name</th>\n <th>movie_title</th>\n <th>title_year</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3711</th>\n <td>Richard Fleischer</td>\n <td>20,000 Leagues Under the Sea</td>\n <td>1954.0</td>\n </tr>\n <tr>\n <th>4894</th>\n <td>Richard Fleischer</td>\n <td>20,000 Leagues Under the Sea</td>\n <td>1954.0</td>\n </tr>\n <tr>\n <th>4352</th>\n <td>Wes Craven</td>\n <td>A Nightmare on Elm Street</td>\n <td>1984.0</td>\n </tr>\n <tr>\n <th>1420</th>\n <td>Wes Craven</td>\n <td>A Nightmare on Elm Street</td>\n <td>1984.0</td>\n </tr>\n <tr>\n <th>1113</th>\n <td>Julie Taymor</td>\n <td>Across the Universe</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>4842</th>\n <td>Julie Taymor</td>\n <td>Across the Universe</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>4128</th>\n <td>Tim Burton</td>\n <td>Alice in Wonderland</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Tim Burton</td>\n <td>Alice in Wonderland</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>2639</th>\n <td>Cameron Crowe</td>\n <td>Aloha</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>1389</th>\n <td>Cameron Crowe</td>\n <td>Aloha</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>271</th>\n <td>Frank Coraci</td>\n <td>Around the World in 80 Days</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>3587</th>\n <td>Frank Coraci</td>\n <td>Around the World in 80 Days</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>367</th>\n <td>Timur Bekmambetov</td>\n <td>Ben-Hur</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>2613</th>\n <td>Timur Bekmambetov</td>\n <td>Ben-Hur</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>3967</th>\n <td>Timur Bekmambetov</td>\n <td>Ben-Hur</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>1852</th>\n <td>Jim Sheridan</td>\n <td>Brothers</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>2882</th>\n <td>Jim Sheridan</td>\n <td>Brothers</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>1662</th>\n <td>Kimberly Peirce</td>\n <td>Carrie</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>4350</th>\n <td>Kimberly Peirce</td>\n <td>Carrie</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>2944</th>\n <td>Martin Campbell</td>\n <td>Casino Royale</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>286</th>\n <td>Martin Campbell</td>\n <td>Casino Royale</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>2063</th>\n <td>Andy Cadiff</td>\n <td>Chasing Liberty</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>1705</th>\n <td>Andy Cadiff</td>\n <td>Chasing Liberty</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>336</th>\n <td>Kenneth Branagh</td>\n <td>Cinderella</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>4141</th>\n <td>Kenneth Branagh</td>\n <td>Cinderella</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>213</th>\n <td>Louis Leterrier</td>\n <td>Clash of the Titans</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>2650</th>\n <td>Louis Leterrier</td>\n <td>Clash of the Titans</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>2193</th>\n <td>John Milius</td>\n <td>Conan the Barbarian</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>390</th>\n <td>John Milius</td>\n <td>Conan the Barbarian</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>430</th>\n <td>George A. Romero</td>\n <td>Creepshow</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>3370</th>\n <td>George A. Romero</td>\n <td>Creepshow</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>1759</th>\n <td>Zack Snyder</td>\n <td>Dawn of the Dead</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>4401</th>\n <td>Zack Snyder</td>\n <td>Dawn of the Dead</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>George A. Romero</td>\n <td>Day of the Dead</td>\n <td>1985.0</td>\n </tr>\n <tr>\n <th>4001</th>\n <td>George A. Romero</td>\n <td>Day of the Dead</td>\n <td>1985.0</td>\n </tr>\n <tr>\n <th>3207</th>\n <td>NaN</td>\n <td>Dekalog</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2824</th>\n <td>NaN</td>\n <td>Dekalog</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2166</th>\n <td>D.J. Caruso</td>\n <td>Disturbia</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>1319</th>\n <td>D.J. Caruso</td>\n <td>Disturbia</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>4202</th>\n <td>Rawson Marshall Thurber</td>\n <td>Dodgeball: A True Underdog Story</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>1595</th>\n <td>Rawson Marshall Thurber</td>\n <td>Dodgeball: A True Underdog Story</td>\n <td>2004.0</td>\n </tr>\n <tr>\n <th>428</th>\n <td>Pete Travis</td>\n <td>Dredd</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>1122</th>\n <td>Pete Travis</td>\n <td>Dredd</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>1944</th>\n <td>Dexter Fletcher</td>\n <td>Eddie the Eagle</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>2059</th>\n <td>Dexter Fletcher</td>\n <td>Eddie the Eagle</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>159</th>\n <td>Ridley Scott</td>\n <td>Exodus: Gods and Kings</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>3891</th>\n <td>Ridley Scott</td>\n <td>Exodus: Gods and Kings</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>2772</th>\n <td>Ted Kotcheff</td>\n <td>First Blood</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>1060</th>\n <td>Ted Kotcheff</td>\n <td>First Blood</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>1582</th>\n <td>Paul Feig</td>\n <td>Ghostbusters</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>150</th>\n <td>Paul Feig</td>\n <td>Ghostbusters</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>1713</th>\n <td>Edward Zwick</td>\n <td>Glory</td>\n <td>1989.0</td>\n </tr>\n <tr>\n <th>2418</th>\n <td>Edward Zwick</td>\n <td>Glory</td>\n <td>1989.0</td>\n </tr>\n <tr>\n <th>4569</th>\n <td>Rob Letterman</td>\n <td>Goosebumps</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>815</th>\n <td>Rob Letterman</td>\n <td>Goosebumps</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>2492</th>\n <td>John Carpenter</td>\n <td>Halloween</td>\n <td>1978.0</td>\n </tr>\n <tr>\n <th>4821</th>\n <td>John Carpenter</td>\n <td>Halloween</td>\n <td>1978.0</td>\n </tr>\n <tr>\n <th>1463</th>\n <td>Scott Mann</td>\n <td>Heist</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>3317</th>\n <td>Scott Mann</td>\n <td>Heist</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>188</th>\n <td>Tim Johnson</td>\n <td>Home</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>3010</th>\n <td>Tim Johnson</td>\n <td>Home</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>4752</th>\n <td>Tim Johnson</td>\n <td>Home</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>1451</th>\n <td>Jaume Collet-Serra</td>\n <td>House of Wax</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>4695</th>\n <td>Jaume Collet-Serra</td>\n <td>House of Wax</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>4446</th>\n <td>Christopher McQuarrie</td>\n <td>Jack Reacher</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>739</th>\n <td>Christopher McQuarrie</td>\n <td>Jack Reacher</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>3463</th>\n <td>Jason Reitman</td>\n <td>Juno</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>4557</th>\n <td>Jason Reitman</td>\n <td>Juno</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>4694</th>\n <td>Peter Jackson</td>\n <td>King Kong</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Peter Jackson</td>\n <td>King Kong</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>2049</th>\n <td>Peter Jackson</td>\n <td>King Kong</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>4256</th>\n <td>Stanley Kubrick</td>\n <td>Lolita</td>\n <td>1962.0</td>\n </tr>\n <tr>\n <th>890</th>\n <td>Stanley Kubrick</td>\n <td>Lolita</td>\n <td>1962.0</td>\n </tr>\n <tr>\n <th>1812</th>\n <td>Paul McGuigan</td>\n <td>Lucky Number Slevin</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>4493</th>\n <td>Paul McGuigan</td>\n <td>Lucky Number Slevin</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>785</th>\n <td>Harold Becker</td>\n <td>Mercury Rising</td>\n <td>1998.0</td>\n </tr>\n <tr>\n <th>4466</th>\n <td>Harold Becker</td>\n <td>Mercury Rising</td>\n <td>1998.0</td>\n </tr>\n <tr>\n <th>998</th>\n <td>Barbet Schroeder</td>\n <td>Murder by Numbers</td>\n <td>2002.0</td>\n </tr>\n <tr>\n <th>3654</th>\n <td>Barbet Schroeder</td>\n <td>Murder by Numbers</td>\n <td>2002.0</td>\n </tr>\n <tr>\n <th>3791</th>\n <td>Dennis Hopper</td>\n <td>Out of the Blue</td>\n <td>1980.0</td>\n </tr>\n <tr>\n <th>3840</th>\n <td>Robert Sarkies</td>\n <td>Out of the Blue</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>4778</th>\n <td>Sam Raimi</td>\n <td>Oz the Great and Powerful</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Sam Raimi</td>\n <td>Oz the Great and Powerful</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>3879</th>\n <td>Joe Wright</td>\n <td>Pan</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>145</th>\n <td>Joe Wright</td>\n <td>Pan</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>287</th>\n <td>Tim Burton</td>\n <td>Planet of the Apes</td>\n <td>2001.0</td>\n </tr>\n <tr>\n <th>3908</th>\n <td>Tim Burton</td>\n <td>Planet of the Apes</td>\n <td>2001.0</td>\n </tr>\n <tr>\n <th>318</th>\n <td>Ericson Core</td>\n <td>Point Break</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>2020</th>\n <td>Ericson Core</td>\n <td>Point Break</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>1438</th>\n <td>Tobe Hooper</td>\n <td>Poltergeist</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>3069</th>\n <td>Tobe Hooper</td>\n <td>Poltergeist</td>\n <td>1982.0</td>\n </tr>\n <tr>\n <th>1357</th>\n <td>Lee Daniels</td>\n <td>Precious</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>3096</th>\n <td>Lee Daniels</td>\n <td>Precious</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>231</th>\n <td>José Padilha</td>\n <td>RoboCop</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>2836</th>\n <td>José Padilha</td>\n <td>RoboCop</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>1492</th>\n <td>David Ayer</td>\n <td>Sabotage</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>5012</th>\n <td>David Ayer</td>\n <td>Sabotage</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>1668</th>\n <td>Steven Soderbergh</td>\n <td>Side Effects</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>4905</th>\n <td>Steven Soderbergh</td>\n <td>Side Effects</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Sam Mendes</td>\n <td>Skyfall</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>3493</th>\n <td>Sam Mendes</td>\n <td>Skyfall</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>1530</th>\n <td>David R. Ellis</td>\n <td>Snakes on a Plane</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>1994</th>\n <td>David R. Ellis</td>\n <td>Snakes on a Plane</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>2632</th>\n <td>Ric Roman Waugh</td>\n <td>Snitch</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>4649</th>\n <td>Ric Roman Waugh</td>\n <td>Snitch</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>3461</th>\n <td>Sam Raimi</td>\n <td>Spider-Man 3</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Sam Raimi</td>\n <td>Spider-Man 3</td>\n <td>2007.0</td>\n </tr>\n <tr>\n <th>969</th>\n <td>Stephen Gaghan</td>\n <td>Syriana</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>3382</th>\n <td>Stephen Gaghan</td>\n <td>Syriana</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>2499</th>\n <td>Joseph Kosinski</td>\n <td>TRON: Legacy</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Joseph Kosinski</td>\n <td>TRON: Legacy</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>245</th>\n <td>Jonathan Liebesman</td>\n <td>Teenage Mutant Ninja Turtles</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>2820</th>\n <td>Jonathan Liebesman</td>\n <td>Teenage Mutant Ninja Turtles</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>1519</th>\n <td>Rand Ravich</td>\n <td>The Astronaut's Wife</td>\n <td>1999.0</td>\n </tr>\n <tr>\n <th>4173</th>\n <td>Rand Ravich</td>\n <td>The Astronaut's Wife</td>\n <td>1999.0</td>\n </tr>\n <tr>\n <th>468</th>\n <td>Scott Derrickson</td>\n <td>The Day the Earth Stood Still</td>\n <td>2008.0</td>\n </tr>\n <tr>\n <th>4489</th>\n <td>Scott Derrickson</td>\n <td>The Day the Earth Stood Still</td>\n <td>2008.0</td>\n </tr>\n <tr>\n <th>100</th>\n <td>Rob Cohen</td>\n <td>The Fast and the Furious</td>\n <td>2001.0</td>\n </tr>\n <tr>\n <th>1332</th>\n <td>Rob Cohen</td>\n <td>The Fast and the Furious</td>\n <td>2001.0</td>\n </tr>\n <tr>\n <th>2420</th>\n <td>John Carpenter</td>\n <td>The Fog</td>\n <td>1980.0</td>\n </tr>\n <tr>\n <th>4532</th>\n <td>John Carpenter</td>\n <td>The Fog</td>\n <td>1980.0</td>\n </tr>\n <tr>\n <th>4118</th>\n <td>Rupert Wyatt</td>\n <td>The Gambler</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>1940</th>\n <td>Rupert Wyatt</td>\n <td>The Gambler</td>\n <td>2014.0</td>\n </tr>\n <tr>\n <th>3704</th>\n <td>Joel Edgerton</td>\n <td>The Gift</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>3158</th>\n <td>Joel Edgerton</td>\n <td>The Gift</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>50</th>\n <td>Baz Luhrmann</td>\n <td>The Great Gatsby</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>3476</th>\n <td>Baz Luhrmann</td>\n <td>The Great Gatsby</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>1002</th>\n <td>Andrew Niccol</td>\n <td>The Host</td>\n <td>2013.0</td>\n </tr>\n <tr>\n <th>2988</th>\n <td>Joon-ho Bong</td>\n <td>The Host</td>\n <td>2006.0</td>\n </tr>\n <tr>\n <th>2105</th>\n <td>Michael Bay</td>\n <td>The Island</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>280</th>\n <td>Michael Bay</td>\n <td>The Island</td>\n <td>2005.0</td>\n </tr>\n <tr>\n <th>1805</th>\n <td>Jon Favreau</td>\n <td>The Jungle Book</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>79</th>\n <td>Jon Favreau</td>\n <td>The Jungle Book</td>\n <td>2016.0</td>\n </tr>\n <tr>\n <th>1184</th>\n <td>John G. Avildsen</td>\n <td>The Karate Kid</td>\n <td>1984.0</td>\n </tr>\n <tr>\n <th>3351</th>\n <td>John G. Avildsen</td>\n <td>The Karate Kid</td>\n <td>1984.0</td>\n </tr>\n <tr>\n <th>4971</th>\n <td>Dennis Iliadis</td>\n <td>The Last House on the Left</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>2647</th>\n <td>Dennis Iliadis</td>\n <td>The Last House on the Left</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>337</th>\n <td>Peter Jackson</td>\n <td>The Lovely Bones</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>4584</th>\n <td>Peter Jackson</td>\n <td>The Lovely Bones</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>84</th>\n <td>Roland Joffé</td>\n <td>The Lovers</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>1508</th>\n <td>Roland Joffé</td>\n <td>The Lovers</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>4150</th>\n <td>Richard Donner</td>\n <td>The Omen</td>\n <td>1976.0</td>\n </tr>\n <tr>\n <th>1894</th>\n <td>Richard Donner</td>\n <td>The Omen</td>\n <td>1976.0</td>\n </tr>\n <tr>\n <th>3896</th>\n <td>Dan O'Bannon</td>\n <td>The Return of the Living Dead</td>\n <td>1985.0</td>\n </tr>\n <tr>\n <th>3578</th>\n <td>Dan O'Bannon</td>\n <td>The Return of the Living Dead</td>\n <td>1985.0</td>\n </tr>\n <tr>\n <th>4936</th>\n <td>Tobe Hooper</td>\n <td>The Texas Chain Saw Massacre</td>\n <td>1974.0</td>\n </tr>\n <tr>\n <th>3278</th>\n <td>Tobe Hooper</td>\n <td>The Texas Chain Saw Massacre</td>\n <td>1974.0</td>\n </tr>\n <tr>\n <th>305</th>\n <td>Florian Henckel von Donnersmarck</td>\n <td>The Tourist</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>3170</th>\n <td>Florian Henckel von Donnersmarck</td>\n <td>The Tourist</td>\n <td>2010.0</td>\n </tr>\n <tr>\n <th>4681</th>\n <td>David S. Goyer</td>\n <td>The Unborn</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>2546</th>\n <td>David S. Goyer</td>\n <td>The Unborn</td>\n <td>2009.0</td>\n </tr>\n <tr>\n <th>4009</th>\n <td>Akiva Schaffer</td>\n <td>The Watch</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>871</th>\n <td>Akiva Schaffer</td>\n <td>The Watch</td>\n <td>2012.0</td>\n </tr>\n <tr>\n <th>1378</th>\n <td>Catherine Hardwicke</td>\n <td>Twilight</td>\n <td>2008.0</td>\n </tr>\n <tr>\n <th>2262</th>\n <td>Catherine Hardwicke</td>\n <td>Twilight</td>\n <td>2008.0</td>\n </tr>\n <tr>\n <th>1232</th>\n <td>Jaume Collet-Serra</td>\n <td>Unknown</td>\n <td>2011.0</td>\n </tr>\n <tr>\n <th>3981</th>\n <td>Jaume Collet-Serra</td>\n <td>Unknown</td>\n <td>2011.0</td>\n </tr>\n <tr>\n <th>1146</th>\n <td>Paul McGuigan</td>\n <td>Victor Frankenstein</td>\n <td>2015.0</td>\n </tr>\n <tr>\n <th>2099</th>\n <td>Paul McGuigan</td>\n <td>Victor Frankenstein</td>\n <td>2015.0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " director_name movie_title \\\n3711 Richard Fleischer 20,000 Leagues Under the Sea  \n4894 Richard Fleischer 20,000 Leagues Under the Sea  \n4352 Wes Craven A Nightmare on Elm Street  \n1420 Wes Craven A Nightmare on Elm Street  \n1113 Julie Taymor Across the Universe  \n4842 Julie Taymor Across the Universe  \n4128 Tim Burton Alice in Wonderland  \n33 Tim Burton Alice in Wonderland  \n2639 Cameron Crowe Aloha  \n1389 Cameron Crowe Aloha  \n271 Frank Coraci Around the World in 80 Days  \n3587 Frank Coraci Around the World in 80 Days  \n367 Timur Bekmambetov Ben-Hur  \n2613 Timur Bekmambetov Ben-Hur  \n3967 Timur Bekmambetov Ben-Hur  \n1852 Jim Sheridan Brothers  \n2882 Jim Sheridan Brothers  \n1662 Kimberly Peirce Carrie  \n4350 Kimberly Peirce Carrie  \n2944 Martin Campbell Casino Royale  \n286 Martin Campbell Casino Royale  \n2063 Andy Cadiff Chasing Liberty  \n1705 Andy Cadiff Chasing Liberty  \n336 Kenneth Branagh Cinderella  \n4141 Kenneth Branagh Cinderella  \n213 Louis Leterrier Clash of the Titans  \n2650 Louis Leterrier Clash of the Titans  \n2193 John Milius Conan the Barbarian  \n390 John Milius Conan the Barbarian  \n430 George A. Romero Creepshow  \n3370 George A. Romero Creepshow  \n1759 Zack Snyder Dawn of the Dead  \n4401 Zack Snyder Dawn of the Dead  \n2469 George A. Romero Day of the Dead  \n4001 George A. Romero Day of the Dead  \n3207 NaN Dekalog  \n2824 NaN Dekalog  \n2166 D.J. Caruso Disturbia  \n1319 D.J. Caruso Disturbia  \n4202 Rawson Marshall Thurber Dodgeball: A True Underdog Story  \n1595 Rawson Marshall Thurber Dodgeball: A True Underdog Story  \n428 Pete Travis Dredd  \n1122 Pete Travis Dredd  \n1944 Dexter Fletcher Eddie the Eagle  \n2059 Dexter Fletcher Eddie the Eagle  \n159 Ridley Scott Exodus: Gods and Kings  \n3891 Ridley Scott Exodus: Gods and Kings  \n2772 Ted Kotcheff First Blood  \n1060 Ted Kotcheff First Blood  \n1582 Paul Feig Ghostbusters  \n150 Paul Feig Ghostbusters  \n1713 Edward Zwick Glory  \n2418 Edward Zwick Glory  \n4569 Rob Letterman Goosebumps  \n815 Rob Letterman Goosebumps  \n2492 John Carpenter Halloween  \n4821 John Carpenter Halloween  \n1463 Scott Mann Heist  \n3317 Scott Mann Heist  \n188 Tim Johnson Home  \n3010 Tim Johnson Home  \n4752 Tim Johnson Home  \n1451 Jaume Collet-Serra House of Wax  \n4695 Jaume Collet-Serra House of Wax  \n4446 Christopher McQuarrie Jack Reacher  \n739 Christopher McQuarrie Jack Reacher  \n3463 Jason Reitman Juno  \n4557 Jason Reitman Juno  \n4694 Peter Jackson King Kong  \n25 Peter Jackson King Kong  \n2049 Peter Jackson King Kong  \n4256 Stanley Kubrick Lolita  \n890 Stanley Kubrick Lolita  \n1812 Paul McGuigan Lucky Number Slevin  \n4493 Paul McGuigan Lucky Number Slevin  \n785 Harold Becker Mercury Rising  \n4466 Harold Becker Mercury Rising  \n998 Barbet Schroeder Murder by Numbers  \n3654 Barbet Schroeder Murder by Numbers  \n3791 Dennis Hopper Out of the Blue  \n3840 Robert Sarkies Out of the Blue  \n4778 Sam Raimi Oz the Great and Powerful  \n38 Sam Raimi Oz the Great and Powerful  \n3879 Joe Wright Pan  \n145 Joe Wright Pan  \n287 Tim Burton Planet of the Apes  \n3908 Tim Burton Planet of the Apes  \n318 Ericson Core Point Break  \n2020 Ericson Core Point Break  \n1438 Tobe Hooper Poltergeist  \n3069 Tobe Hooper Poltergeist  \n1357 Lee Daniels Precious  \n3096 Lee Daniels Precious  \n231 José Padilha RoboCop  \n2836 José Padilha RoboCop  \n1492 David Ayer Sabotage  \n5012 David Ayer Sabotage  \n1668 Steven Soderbergh Side Effects  \n4905 Steven Soderbergh Side Effects  \n30 Sam Mendes Skyfall  \n3493 Sam Mendes Skyfall  \n1530 David R. Ellis Snakes on a Plane  \n1994 David R. Ellis Snakes on a Plane  \n2632 Ric Roman Waugh Snitch  \n4649 Ric Roman Waugh Snitch  \n3461 Sam Raimi Spider-Man 3  \n6 Sam Raimi Spider-Man 3  \n969 Stephen Gaghan Syriana  \n3382 Stephen Gaghan Syriana  \n2499 Joseph Kosinski TRON: Legacy  \n40 Joseph Kosinski TRON: Legacy  \n245 Jonathan Liebesman Teenage Mutant Ninja Turtles  \n2820 Jonathan Liebesman Teenage Mutant Ninja Turtles  \n1519 Rand Ravich The Astronaut's Wife  \n4173 Rand Ravich The Astronaut's Wife  \n468 Scott Derrickson The Day the Earth Stood Still  \n4489 Scott Derrickson The Day the Earth Stood Still  \n100 Rob Cohen The Fast and the Furious  \n1332 Rob Cohen The Fast and the Furious  \n2420 John Carpenter The Fog  \n4532 John Carpenter The Fog  \n4118 Rupert Wyatt The Gambler  \n1940 Rupert Wyatt The Gambler  \n3704 Joel Edgerton The Gift  \n3158 Joel Edgerton The Gift  \n50 Baz Luhrmann The Great Gatsby  \n3476 Baz Luhrmann The Great Gatsby  \n1002 Andrew Niccol The Host  \n2988 Joon-ho Bong The Host  \n2105 Michael Bay The Island  \n280 Michael Bay The Island  \n1805 Jon Favreau The Jungle Book  \n79 Jon Favreau The Jungle Book  \n1184 John G. Avildsen The Karate Kid  \n3351 John G. Avildsen The Karate Kid  \n4971 Dennis Iliadis The Last House on the Left  \n2647 Dennis Iliadis The Last House on the Left  \n337 Peter Jackson The Lovely Bones  \n4584 Peter Jackson The Lovely Bones  \n84 Roland Joffé The Lovers  \n1508 Roland Joffé The Lovers  \n4150 Richard Donner The Omen  \n1894 Richard Donner The Omen  \n3896 Dan O'Bannon The Return of the Living Dead  \n3578 Dan O'Bannon The Return of the Living Dead  \n4936 Tobe Hooper The Texas Chain Saw Massacre  \n3278 Tobe Hooper The Texas Chain Saw Massacre  \n305 Florian Henckel von Donnersmarck The Tourist  \n3170 Florian Henckel von Donnersmarck The Tourist  \n4681 David S. Goyer The Unborn  \n2546 David S. Goyer The Unborn  \n4009 Akiva Schaffer The Watch  \n871 Akiva Schaffer The Watch  \n1378 Catherine Hardwicke Twilight  \n2262 Catherine Hardwicke Twilight  \n1232 Jaume Collet-Serra Unknown  \n3981 Jaume Collet-Serra Unknown  \n1146 Paul McGuigan Victor Frankenstein  \n2099 Paul McGuigan Victor Frankenstein  \n\n title_year \n3711 1954.0 \n4894 1954.0 \n4352 1984.0 \n1420 1984.0 \n1113 2007.0 \n4842 2007.0 \n4128 2010.0 \n33 2010.0 \n2639 2015.0 \n1389 2015.0 \n271 2004.0 \n3587 2004.0 \n367 2016.0 \n2613 2016.0 \n3967 2016.0 \n1852 2009.0 \n2882 2009.0 \n1662 2013.0 \n4350 2013.0 \n2944 2006.0 \n286 2006.0 \n2063 2004.0 \n1705 2004.0 \n336 2015.0 \n4141 2015.0 \n213 2010.0 \n2650 2010.0 \n2193 1982.0 \n390 1982.0 \n430 1982.0 \n3370 1982.0 \n1759 2004.0 \n4401 2004.0 \n2469 1985.0 \n4001 1985.0 \n3207 NaN \n2824 NaN \n2166 2007.0 \n1319 2007.0 \n4202 2004.0 \n1595 2004.0 \n428 2012.0 \n1122 2012.0 \n1944 2016.0 \n2059 2016.0 \n159 2014.0 \n3891 2014.0 \n2772 1982.0 \n1060 1982.0 \n1582 2016.0 \n150 2016.0 \n1713 1989.0 \n2418 1989.0 \n4569 2015.0 \n815 2015.0 \n2492 1978.0 \n4821 1978.0 \n1463 2015.0 \n3317 2015.0 \n188 2015.0 \n3010 2015.0 \n4752 2015.0 \n1451 2005.0 \n4695 2005.0 \n4446 2012.0 \n739 2012.0 \n3463 2007.0 \n4557 2007.0 \n4694 2005.0 \n25 2005.0 \n2049 2005.0 \n4256 1962.0 \n890 1962.0 \n1812 2006.0 \n4493 2006.0 \n785 1998.0 \n4466 1998.0 \n998 2002.0 \n3654 2002.0 \n3791 1980.0 \n3840 2006.0 \n4778 2013.0 \n38 2013.0 \n3879 2015.0 \n145 2015.0 \n287 2001.0 \n3908 2001.0 \n318 2015.0 \n2020 2015.0 \n1438 1982.0 \n3069 1982.0 \n1357 2009.0 \n3096 2009.0 \n231 2014.0 \n2836 2014.0 \n1492 2014.0 \n5012 2014.0 \n1668 2013.0 \n4905 2013.0 \n30 2012.0 \n3493 2012.0 \n1530 2006.0 \n1994 2006.0 \n2632 2013.0 \n4649 2013.0 \n3461 2007.0 \n6 2007.0 \n969 2005.0 \n3382 2005.0 \n2499 2010.0 \n40 2010.0 \n245 2014.0 \n2820 2014.0 \n1519 1999.0 \n4173 1999.0 \n468 2008.0 \n4489 2008.0 \n100 2001.0 \n1332 2001.0 \n2420 1980.0 \n4532 1980.0 \n4118 2014.0 \n1940 2014.0 \n3704 2015.0 \n3158 2015.0 \n50 2013.0 \n3476 2013.0 \n1002 2013.0 \n2988 2006.0 \n2105 2005.0 \n280 2005.0 \n1805 2016.0 \n79 2016.0 \n1184 1984.0 \n3351 1984.0 \n4971 2009.0 \n2647 2009.0 \n337 2009.0 \n4584 2009.0 \n84 2015.0 \n1508 2015.0 \n4150 1976.0 \n1894 1976.0 \n3896 1985.0 \n3578 1985.0 \n4936 1974.0 \n3278 1974.0 \n305 2010.0 \n3170 2010.0 \n4681 2009.0 \n2546 2009.0 \n4009 2012.0 \n871 2012.0 \n1378 2008.0 \n2262 2008.0 \n1232 2011.0 \n3981 2011.0 \n1146 2015.0 \n2099 2015.0 "
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "interpreted-certificate",
"cell_type": "code",
"source": "df = pd.read_csv(\n \"https://github.com/Godoy/imdb-5000-movie-dataset/raw/master/data/movie_metadata.csv\"\n)\nfor column in df.dtypes[df.dtypes == object].index:\n df[column] = df[column].str.strip()\ndf.drop_duplicates(subset=[\"director_name\", \n \"movie_title\", \n \"title_year\"], \n inplace=True)",
"execution_count": 80,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "ongoing-probability",
"cell_type": "code",
"source": "pd.set_option(\"display.max_rows\", 50)",
"execution_count": 65,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "single-progressive",
"cell_type": "code",
"source": "df.shape",
"execution_count": 71,
"outputs": [
{
"data": {
"text/plain": "(4919, 28)"
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "resistant-appliance",
"cell_type": "code",
"source": "df.drop('color', axis=1, inplace=True)",
"execution_count": 77,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "artificial-alias",
"cell_type": "code",
"source": "df = pd.read_csv(\n \"https://github.com/Godoy/imdb-5000-movie-dataset/raw/master/data/movie_metadata.csv\"\n)\nfor column in df.dtypes[df.dtypes == object].index:\n df[column] = df[column].str.strip()\ndf.drop_duplicates(subset=[\"director_name\", \n \"movie_title\", \n \"title_year\"], \n inplace=True)",
"execution_count": 81,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "statewide-switch",
"cell_type": "code",
"source": "df.columns",
"execution_count": 83,
"outputs": [
{
"data": {
"text/plain": "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n dtype='object')"
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "inner-joseph",
"cell_type": "code",
"source": "(df.groupby('country')\n ['imdb_score']\n .mean()\n .sort_values(ascending=False)\n [:10])",
"execution_count": 88,
"outputs": [
{
"data": {
"text/plain": "country\nKyrgyzstan 8.700000\nLibya 8.400000\nUnited Arab Emirates 8.200000\nSoviet Union 8.100000\nEgypt 8.100000\nIran 7.725000\nIndonesia 7.600000\nIsrael 7.525000\nSweden 7.516667\nCameroon 7.500000\nName: imdb_score, dtype: float64"
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "honest-massage",
"cell_type": "code",
"source": "(df.groupby('country')\n .agg({'imdb_score': 'mean',\n 'movie_title': 'count'})\n .sort_values('imdb_score', ascending=False)\n .rename(columns={'movie_title': 'count'})\n .query('count > 10')\n [:10]\n)",
"execution_count": 99,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>imdb_score</th>\n <th>count</th>\n </tr>\n <tr>\n <th>country</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>New Zealand</th>\n <td>7.292308</td>\n <td>13</td>\n </tr>\n <tr>\n <th>Denmark</th>\n <td>7.172727</td>\n <td>11</td>\n </tr>\n <tr>\n <th>Japan</th>\n <td>6.895455</td>\n <td>22</td>\n </tr>\n <tr>\n <th>Italy</th>\n <td>6.873913</td>\n <td>23</td>\n </tr>\n <tr>\n <th>Spain</th>\n <td>6.824242</td>\n <td>33</td>\n </tr>\n <tr>\n <th>UK</th>\n <td>6.800230</td>\n <td>434</td>\n </tr>\n <tr>\n <th>Ireland</th>\n <td>6.783333</td>\n <td>12</td>\n </tr>\n <tr>\n <th>Mexico</th>\n <td>6.776471</td>\n <td>17</td>\n </tr>\n <tr>\n <th>Hong Kong</th>\n <td>6.741176</td>\n <td>17</td>\n </tr>\n <tr>\n <th>France</th>\n <td>6.678571</td>\n <td>154</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " imdb_score count\ncountry \nNew Zealand 7.292308 13\nDenmark 7.172727 11\nJapan 6.895455 22\nItaly 6.873913 23\nSpain 6.824242 33\nUK 6.800230 434\nIreland 6.783333 12\nMexico 6.776471 17\nHong Kong 6.741176 17\nFrance 6.678571 154"
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "southern-kingdom",
"cell_type": "code",
"source": "(df.groupby('country')\n .agg({'imdb_score': 'mean',\n 'movie_title': 'count'})\n .sort_values('imdb_score', ascending=False)\n .rename(columns={'movie_title': 'count'})\n [lambda x: x['count'] > 10]\n [:10]\n)",
"execution_count": 100,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>imdb_score</th>\n <th>count</th>\n </tr>\n <tr>\n <th>country</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>New Zealand</th>\n <td>7.292308</td>\n <td>13</td>\n </tr>\n <tr>\n <th>Denmark</th>\n <td>7.172727</td>\n <td>11</td>\n </tr>\n <tr>\n <th>Japan</th>\n <td>6.895455</td>\n <td>22</td>\n </tr>\n <tr>\n <th>Italy</th>\n <td>6.873913</td>\n <td>23</td>\n </tr>\n <tr>\n <th>Spain</th>\n <td>6.824242</td>\n <td>33</td>\n </tr>\n <tr>\n <th>UK</th>\n <td>6.800230</td>\n <td>434</td>\n </tr>\n <tr>\n <th>Ireland</th>\n <td>6.783333</td>\n <td>12</td>\n </tr>\n <tr>\n <th>Mexico</th>\n <td>6.776471</td>\n <td>17</td>\n </tr>\n <tr>\n <th>Hong Kong</th>\n <td>6.741176</td>\n <td>17</td>\n </tr>\n <tr>\n <th>France</th>\n <td>6.678571</td>\n <td>154</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " imdb_score count\ncountry \nNew Zealand 7.292308 13\nDenmark 7.172727 11\nJapan 6.895455 22\nItaly 6.873913 23\nSpain 6.824242 33\nUK 6.800230 434\nIreland 6.783333 12\nMexico 6.776471 17\nHong Kong 6.741176 17\nFrance 6.678571 154"
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "beautiful-burning",
"cell_type": "code",
"source": "%matplotlib inline",
"execution_count": 104,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "prepared-cradle",
"cell_type": "code",
"source": "(df[['director_facebook_likes', 'imdb_score']]\n .plot\n .scatter(x='director_facebook_likes', y='imdb_score'))",
"execution_count": 105,
"outputs": [
{
"data": {
"text/plain": "<AxesSubplot:xlabel='director_facebook_likes', ylabel='imdb_score'>"
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "chicken-paintball",
"cell_type": "code",
"source": "df.columns",
"execution_count": 107,
"outputs": [
{
"data": {
"text/plain": "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n dtype='object')"
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "amber-alignment",
"cell_type": "code",
"source": "df[['director_facebook_likes', 'actor_1_facebook_likes', \n 'imdb_score']].corr()",
"execution_count": 108,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>director_facebook_likes</th>\n <th>actor_1_facebook_likes</th>\n <th>imdb_score</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>director_facebook_likes</th>\n <td>1.000000</td>\n <td>0.09104</td>\n <td>0.173862</td>\n </tr>\n <tr>\n <th>actor_1_facebook_likes</th>\n <td>0.091040</td>\n <td>1.00000</td>\n <td>0.076900</td>\n </tr>\n <tr>\n <th>imdb_score</th>\n <td>0.173862</td>\n <td>0.07690</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " director_facebook_likes actor_1_facebook_likes \\\ndirector_facebook_likes 1.000000 0.09104 \nactor_1_facebook_likes 0.091040 1.00000 \nimdb_score 0.173862 0.07690 \n\n imdb_score \ndirector_facebook_likes 0.173862 \nactor_1_facebook_likes 0.076900 \nimdb_score 1.000000 "
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "sticky-picnic",
"cell_type": "code",
"source": "import matplotlib.pyplot as plt",
"execution_count": 110,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "alien-elephant",
"cell_type": "code",
"source": "plt.matshow(df.corr(), cmap='seismic')\nplt.colorbar()\nplt.yticks(\n range(df.corr().shape[0]),\n df.corr().index);",
"execution_count": 119,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 288x288 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "powered-going",
"cell_type": "code",
"source": "import numpy as np",
"execution_count": 125,
"outputs": []
},
{
"metadata": {
"trusted": false
},
"id": "false-jungle",
"cell_type": "code",
"source": "df.groupby('director_name').size()",
"execution_count": 129,
"outputs": [
{
"data": {
"text/plain": "director_name\nA. Raven Cruz 1\nAaron Hann 1\nAaron Schneider 1\nAaron Seltzer 1\nAbel Ferrara 1\n ..\nZoran Lisinac 1\nÁlex de la Iglesia 1\nÉmile Gaudreault 1\nÉric Tessier 1\nÉtienne Faure 1\nLength: 2398, dtype: int64"
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "nutritional-algebra",
"cell_type": "code",
"source": "df.plot.scatter(x='title_year', y='duration')",
"execution_count": 133,
"outputs": [
{
"data": {
"text/plain": "<AxesSubplot:xlabel='title_year', ylabel='duration'>"
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "expressed-governor",
"cell_type": "code",
"source": "(df.groupby(['title_year', 'color'])\n .size().to_frame().reset_index()\n .query('color == \"Black and White\"')\n .sort_values('title_year')\n)",
"execution_count": 139,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>title_year</th>\n <th>color</th>\n <th>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1916.0</td>\n <td>Black and White</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1920.0</td>\n <td>Black and White</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1925.0</td>\n <td>Black and White</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1927.0</td>\n <td>Black and White</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1929.0</td>\n <td>Black and White</td>\n <td>2</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>139</th>\n <td>2011.0</td>\n <td>Black and White</td>\n <td>2</td>\n </tr>\n <tr>\n <th>141</th>\n <td>2012.0</td>\n <td>Black and White</td>\n <td>5</td>\n </tr>\n <tr>\n <th>143</th>\n <td>2013.0</td>\n <td>Black and White</td>\n <td>5</td>\n </tr>\n <tr>\n <th>145</th>\n <td>2014.0</td>\n <td>Black and White</td>\n <td>3</td>\n </tr>\n <tr>\n <th>147</th>\n <td>2015.0</td>\n <td>Black and White</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n<p>73 rows × 3 columns</p>\n</div>",
"text/plain": " title_year color 0\n0 1916.0 Black and White 1\n1 1920.0 Black and White 1\n2 1925.0 Black and White 1\n3 1927.0 Black and White 1\n4 1929.0 Black and White 2\n.. ... ... ..\n139 2011.0 Black and White 2\n141 2012.0 Black and White 5\n143 2013.0 Black and White 5\n145 2014.0 Black and White 3\n147 2015.0 Black and White 1\n\n[73 rows x 3 columns]"
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"trusted": false
},
"id": "frequent-highway",
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "py3.10",
"display_name": "Python 3.10",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.10.0",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "",
"data": {
"description": "Lesson14",
"public": false
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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