In [1]:
import os
import pandas as pd
import morejson as json
import holcrawl
import holcrawl.shared
from tqdm import tqdm
import numpy as np

In [2]:
UNITED_DIR_PATH = holcrawl.shared._get_united_dir_path()
verbose = True

In [3]:
DEMOGRAPHICS = [
    'aged_under_18',
    'males_under_18',
    'males_aged_45+',
    'females',
    'males_aged_18-29',
    'imdb_staff',
    'imdb_users',
    'males',
    'aged_30-44',
    'females_aged_45+',
    'aged_18-29',
    'females_aged_18-29',
    'aged_45+',
    'males_aged_30-44',
    'top_1000_voters',
    'females_under_18',
    'females_aged_30-44',
    'us_users',
    'non-us_users'
]

In [4]:
def _decompose_dict_column(df, colname, allowed_cols):
    newdf = df[colname].apply(pd.Series)
    newdf = newdf.drop([
        col for col in newdf.columns if col not in allowed_cols], axis=1)
    newdf.columns = [colname+'.'+col for col in newdf.columns]
    return pd.concat([df.drop([colname], axis=1), newdf], axis=1)


def _dummy_list_column(df, colname):
    value_set = set([
        value for value_list in df[colname].dropna() for value in value_list])
    def _value_list_to_dict(value_list):
        try:
            return {
                value : 1 if value in value_list else 0
                for value in value_set}
        except TypeError:
            return {value : 0 for value in value_set}
    df[colname] = df[colname].apply(_value_list_to_dict)
    return _decompose_dict_column(df, colname, list(value_set))

In [5]:
def _list_to_sorted_str(listi):
    return '_'.join(sorted([string.replace('-', '') for string in listi]))

In [6]:
profiles = []
profile_files = os.listdir(UNITED_DIR_PATH)
if verbose:
    profile_files = tqdm(profile_files)
for profile_file in profile_files:
    if verbose:
        profile_files.set_description('Reading {}'.format(profile_file))
    file_path = os.path.join(UNITED_DIR_PATH, profile_file)
    _, ext = os.path.splitext(file_path)
    if ext == '.json':
        with open(file_path, 'r') as json_file:
            profiles.append(json.load(json_file))


Reading woman_in_gold.json: 100%|██████████| 180/180 [00:00<00:00, 675.89it/s]           

In [11]:
df['opening_weekend_date'][0]


Out[11]:
datetime.date(2014, 4, 18)

In [ ]:
def _avg_review_by_opening_generator(colname):
    def _avg_review_by_opening(row):
        return np.mean([
            review['score'] for review in row[colname] 
            if review['review_date'] <= row['opening_weekend_date']
        ])
    return _avg_review_by_opening

In [9]:
df = pd.DataFrame(profiles)

In [23]:
df['opening_weekend_date'].map(lambda opendate: opendate.year)


Out[23]:
0      2014
1      2014
2      2014
3      2014
4      2014
5      2014
6      2014
7      2014
8      2015
9      2014
10     2014
11     2015
12     2014
13     2014
14     2015
15     2014
16     2015
17     2014
18     2014
19     2015
20     2014
21     2015
22     2015
23     2015
24     2014
25     2014
26     2015
27     2014
28     2015
29     2015
       ... 
150    2015
151    2015
152    2014
153    2014
154    2014
155    2014
156    2014
157    2015
158    2015
159    2014
160    2015
161    2015
162    2015
163    2014
164    2015
165    2015
166    2014
167    2014
168    2014
169    2015
170    2015
171    2014
172    2014
173    2015
174    2014
175    2015
176    2014
177    2014
178    2014
179    2015
Name: opening_weekend_date, dtype: int64

In [22]:
df['opening_weekend_date'].map(lambda opendate: opendate.timetuple().tm_yday)


Out[22]:
0      108
1      164
2       66
3       52
4       87
5      108
6      262
7       45
8      114
9       45
10     283
11     149
12     206
13     276
14     198
15     241
16     121
17      52
18      80
19     107
20     311
21     261
22      30
23      16
24     143
25     115
26      23
27      94
28      65
29     107
      ... 
150    100
151    275
152    262
153     38
154     17
155     38
156    199
157    359
158     65
159    269
160    212
161     16
162      2
163    262
164    142
165    198
166    108
167    262
168    360
169     65
170    107
171     38
172     73
173    331
174    122
175    149
176    283
177    339
178    199
179     93
Name: opening_weekend_date, dtype: int64

In [ ]:
df = _decompose_dict_column(df, 'avg_rating_per_demo', DEMOGRAPHICS)
df = _decompose_dict_column(df, 'votes_per_demo', DEMOGRAPHICS)
df = _decompose_dict_column(
    df, 'rating_freq', [str(i) for i in range(1, 11)])
df = _dummy_list_column(df, 'genres')
# df.genres = df.genres.apply(_list_to_sorted_str)
df['avg_mc_critic_by_opening'] = df.apply(_avg_review_by_opening_generator('mc_pro_critic_reviews'), axis=1)
df['avg_mc_user_by_opening'] = df.apply(_avg_review_by_opening_generator('mc_user_reviews'), axis=1)
df['opening_month'] = df['opening_weekend_date'].map(lambda opendate: opendate.month)
df['opening_day'] = df['opening_weekend_date'].map(lambda opendate: opendate.day)
df['opening_day_of_year'] = df['opening_weekend_date'].map(lambda opendate: opendate.timetuple().tm_yday)

In [ ]:
df.columns

In [15]:
pd.options.display.max_columns = 999
df


Out[15]:
avg_screens budget budget_currency closing_date critic_review_count duration gross_income max_screens mc_avg_user_score mc_metascore mc_mixed_rating_frequency mc_movie_name mc_negative_rating_frequency mc_positive_rating_frequency mc_pro_critic_reviews mc_user_reviews metascore name num_weekends opening_weekend_date opening_weekend_income opening_weekend_income_currency rating rating_count release_day release_month release_year user_review_count year avg_rating_per_demo.aged_18-29 avg_rating_per_demo.aged_30-44 avg_rating_per_demo.aged_45+ avg_rating_per_demo.aged_under_18 avg_rating_per_demo.females avg_rating_per_demo.females_aged_18-29 avg_rating_per_demo.females_aged_30-44 avg_rating_per_demo.females_aged_45+ avg_rating_per_demo.females_under_18 avg_rating_per_demo.imdb_staff avg_rating_per_demo.imdb_users avg_rating_per_demo.males avg_rating_per_demo.males_aged_18-29 avg_rating_per_demo.males_aged_30-44 avg_rating_per_demo.males_aged_45+ avg_rating_per_demo.males_under_18 avg_rating_per_demo.non-us_users avg_rating_per_demo.top_1000_voters avg_rating_per_demo.us_users votes_per_demo.aged_18-29 votes_per_demo.aged_30-44 votes_per_demo.aged_45+ votes_per_demo.aged_under_18 votes_per_demo.females votes_per_demo.females_aged_18-29 votes_per_demo.females_aged_30-44 votes_per_demo.females_aged_45+ votes_per_demo.females_under_18 votes_per_demo.imdb_staff votes_per_demo.imdb_users votes_per_demo.males votes_per_demo.males_aged_18-29 votes_per_demo.males_aged_30-44 votes_per_demo.males_aged_45+ votes_per_demo.males_under_18 votes_per_demo.non-us_users votes_per_demo.top_1000_voters votes_per_demo.us_users rating_freq.1 rating_freq.10 rating_freq.2 rating_freq.3 rating_freq.4 rating_freq.5 rating_freq.6 rating_freq.7 rating_freq.8 rating_freq.9 genres.action genres.adventure genres.animation genres.biography genres.comedy genres.crime genres.documentary genres.drama genres.family genres.fantasy genres.history genres.horror genres.music genres.musical genres.mystery genres.romance genres.sci-fi genres.sport genres.thriller genres.war avg_mc_critic_by_opening avg_mc_user_by_opening
0 45.000000 4000000.0 $ 2014-04-18 94 93 9134 45 6.8 44 10 13 Sins 3 23 [{'publication': 'RogerEbert.com', 'review_dat... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 44 13 Sins 1 2014-04-18 9134 $ 6.3 25068 NaN NaN NaN 69 2014 6.4 6.2 6.3 7.3 6.4 6.5 6.3 6.5 7.6 7.0 6.3 6.3 6.4 6.2 6.3 7.3 6.2 5.8 6.4 8575.0 10068.0 2256.0 55.0 3968.0 1882.0 1525.0 325.0 11.0 4.0 25068.0 17778.0 6605.0 8426.0 1889.0 44.0 12964.0 230.0 3845.0 337 1254 280 544 1255 3351 6991 6984 3093 979 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 49.700000 NaN
1 2605.375000 50000000.0 $ 2014-11-07 335 112 191616238 3426 8.1 71 81 22 Jump Street 42 621 [{'publication': 'Boston Globe', 'review_date'... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 71 22 Jump Street 8 2014-06-13 57071445 $ 7.1 274236 13.0 June 2014.0 322 2014 7.3 6.7 6.3 7.9 7.1 7.3 6.8 6.4 8.2 6.1 7.1 7.0 7.3 6.7 6.3 7.8 6.9 6.2 7.2 128688.0 65135.0 9028.0 2267.0 40031.0 25654.0 8858.0 1383.0 620.0 16.0 274236.0 180467.0 101712.0 55439.0 7490.0 1628.0 119172.0 506.0 35147.0 2883 25688 1901 3165 6626 16774 43760 81836 64989 26614 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72.844444 7.833333
2 1891.833333 110000000.0 $ 2014-05-16 367 102 106369117 3490 6.0 48 179 300: Rise of an Empire 105 277 [{'publication': 'Chicago Sun-Times', 'review_... [{'total_reactions': 4, 'neg_reactions': 0, 'u... 48 300: Rise of an Empire 12 2014-03-07 45038460 $ 6.2 234420 7.0 March 2014.0 532 2014 6.3 6.1 6.1 6.7 6.3 6.3 6.2 6.2 6.8 6.3 6.2 6.2 6.3 6.1 6.1 6.7 6.1 5.7 6.2 95933.0 73988.0 11634.0 691.0 20214.0 10390.0 6941.0 1219.0 78.0 11.0 234420.0 174235.0 84680.0 66014.0 10209.0 610.0 110596.0 496.0 22633.0 10053 15924 4188 7262 13817 29217 53207 56500 31549 12703 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 49.833333 7.142857
3 938.750000 28000000.0 $ 2014-05-09 215 117 30688364 2872 5.6 40 51 3 Days to Kill 24 41 [{'publication': 'Chicago Sun-Times', 'review_... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 40 3 Days to Kill 12 2014-02-21 12242218 $ 6.2 72337 21.0 February 2014.0 216 2014 6.3 6.0 6.3 6.7 6.3 6.4 6.2 6.5 6.7 6.1 6.2 6.2 6.3 6.0 6.3 6.7 6.1 5.7 6.1 21120.0 28352.0 8328.0 182.0 7668.0 3020.0 2906.0 1089.0 39.0 6.0 72337.0 53624.0 17895.0 25103.0 7092.0 142.0 36147.0 381.0 9738.0 1167 3518 1138 2125 4308 9575 19788 19413 8586 2719 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 45.523810 6.500000
4 29.000000 10000000.0 $ 2014-10-10 8 110 1063723 133 8.1 37 0 50 to 1 1 6 [{'publication': 'Washington Post', 'review_da... [{'total_reactions': 0, 'neg_reactions': 0, 'u... 37 50 to 1 14 2014-03-28 39638 $ 6.6 1482 NaN NaN NaN 55 2014 6.5 6.2 6.7 NaN 7.6 6.8 7.4 8.1 NaN NaN 6.6 6.2 6.4 5.9 6.3 NaN 6.2 5.2 6.7 174.0 456.0 533.0 NaN 457.0 55.0 147.0 235.0 NaN NaN 1482.0 747.0 117.0 306.0 290.0 NaN 480.0 48.0 405.0 36 433 25 38 56 123 231 279 169 92 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 NaN NaN
5 1303.600000 4000000.0 $ 2014-06-06 69 86 17314483 2310 3.8 17 22 A Haunted House 2 57 27 [{'publication': 'St. Louis Post-Dispatch', 'r... [{'total_reactions': 2, 'neg_reactions': 0, 'u... 17 A Haunted House 2 5 2014-04-18 8843875 $ 4.7 15409 18.0 April 2014.0 50 2014 5.0 4.3 4.0 5.7 4.8 5.0 4.5 4.1 6.3 NaN 4.7 4.6 4.9 4.3 4.0 5.6 4.5 3.9 4.4 6946.0 4103.0 788.0 161.0 1932.0 1157.0 499.0 104.0 30.0 NaN 15409.0 10838.0 5726.0 3563.0 672.0 131.0 6458.0 172.0 2592.0 1663 1620 1096 1384 1921 2701 2185 1535 902 402 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 21.785714 4.000000
6 2714.000000 28000000.0 $ 2014-10-17 256 114 25977365 2714 6.5 57 53 A Walk Among the Tombstones 13 95 [{'publication': 'RogerEbert.com', 'review_dat... [{'total_reactions': 4, 'neg_reactions': 0, 'u... 57 A Walk Among the Tombstones 1 2014-09-19 12758780 $ 6.5 92251 19.0 September 2014.0 208 2014 6.6 6.5 6.6 7.1 6.5 6.5 6.5 6.8 7.1 6.8 6.5 6.5 6.6 6.5 6.6 7.1 6.5 6.4 6.7 29490.0 34653.0 8845.0 238.0 9328.0 3785.0 3604.0 1130.0 42.0 12.0 92251.0 68213.0 25446.0 30638.0 7580.0 195.0 46413.0 435.0 11518.0 835 3565 592 1158 3042 9591 26634 31444 12168 3222 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 60.323529 5.333333
7 778.090909 12500000.0 $ 2014-04-25 90 100 48637684 2253 6.1 62 17 About Last Night 12 27 [{'publication': 'Tampa Bay Times', 'review_da... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 62 About Last Night 11 2014-02-14 27838201 $ 6.1 17804 14.0 February 2014.0 42 2014 6.3 5.9 5.6 7.1 6.3 6.4 6.0 6.2 6.8 5.3 6.1 6.0 6.2 5.8 5.4 7.3 5.9 5.0 6.2 7620.0 5049.0 1092.0 62.0 4567.0 2619.0 1251.0 284.0 28.0 3.0 17804.0 10224.0 4919.0 3744.0 787.0 34.0 7521.0 160.0 3056.0 545 1392 297 499 1074 2472 4809 4049 1885 782 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 64.642857 8.000000
8 22.250000 NaN NaN 2015-05-29 50 92 104415 54 6.6 57 6 Adult Beginners 1 5 [{'publication': 'The Playlist', 'review_date'... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 57 Adult Beginners 4 2015-04-24 36657 $ 5.6 6010 NaN NaN NaN 23 2014 5.7 5.5 5.4 5.8 5.8 5.9 5.8 5.8 5.8 7.0 5.6 5.5 5.7 5.4 5.3 5.9 5.4 4.8 5.9 2166.0 2287.0 475.0 17.0 1409.0 724.0 502.0 94.0 6.0 2.0 6010.0 3708.0 1403.0 1746.0 376.0 10.0 2737.0 105.0 1330.0 209 207 151 214 571 1367 1824 1040 323 104 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 59.166667 NaN
9 6.500000 NaN NaN 2014-03-07 40 97 16740 11 7.1 61 4 Adult World 3 12 [{'publication': 'Village Voice', 'review_date... [{'total_reactions': 0, 'neg_reactions': 0, 'u... 61 Adult World 2 2014-02-14 4368 $ 6.2 13983 14.0 February 2014.0 44 2013 6.3 5.9 6.0 7.3 6.5 6.5 6.2 6.4 7.4 6.5 6.2 6.0 6.2 5.8 6.0 7.3 6.1 5.5 6.2 6289.0 3941.0 1150.0 75.0 4535.0 3160.0 876.0 210.0 54.0 2.0 13983.0 7277.0 3045.0 3004.0 917.0 19.0 6558.0 156.0 2721.0 234 853 210 389 883 2014 3842 3416 1549 593 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 59.312500 NaN
10 93.333333 28000000.0 $ 2015-02-20 120 81 66950483 157 5.7 54 34 Alexander and the Terrible, Horrible, No Good,... 18 38 [{'publication': 'RogerEbert.com', 'review_dat... [{'total_reactions': 3, 'neg_reactions': 0, 'u... 54 Alexander and the Terrible, Horrible, No Good,... 3 2014-10-10 18360230 $ 6.2 31607 10.0 October 2014.0 86 2014 6.3 6.1 6.2 6.4 6.5 6.5 6.4 6.6 7.1 7.0 6.2 6.1 6.2 6.0 6.1 6.1 6.1 5.5 6.3 11924.0 9976.0 2399.0 276.0 6969.0 3727.0 2067.0 452.0 105.0 1.0 31607.0 19075.0 8042.0 7777.0 1897.0 167.0 14306.0 243.0 4693.0 596 1886 392 656 1690 4498 9256 8110 3424 1099 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 56.714286 NaN
11 1308.666667 37000000.0 $ 2015-07-17 142 105 20991497 2815 4.3 40 32 Aloha 52 31 [{'publication': 'Chicago Sun-Times', 'review_... [{'total_reactions': 6, 'neg_reactions': 2, 'u... 40 Aloha 6 2015-05-29 9670235 $ 5.4 43735 29.0 May 2015.0 177 2015 5.5 5.3 5.5 5.9 5.5 5.5 5.4 5.7 6.1 5.8 5.4 5.4 5.5 5.3 5.5 5.7 5.4 4.8 5.3 15871.0 13953.0 3728.0 189.0 12186.0 6473.0 3884.0 890.0 96.0 4.0 43735.0 23195.0 9204.0 9837.0 2776.0 90.0 19449.0 256.0 6962.0 1825 1868 1594 2702 5078 9184 10397 6848 3150 1089 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 44.030303 3.500000
12 1199.333333 18000000.0 $ 2014-09-26 109 94 15155772 1816 6.1 38 11 And So It Goes 0 5 [{'publication': 'Chicago Sun-Times', 'review_... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 38 And So It Goes 3 2014-07-25 4642329 $ 5.8 9629 25.0 July 2014.0 50 2014 5.7 5.6 6.0 5.8 5.8 5.8 5.7 6.2 6.4 7.0 5.8 5.7 5.7 5.6 5.9 5.3 5.7 4.9 5.7 2160.0 3549.0 1996.0 24.0 3072.0 1046.0 1218.0 607.0 9.0 1.0 9629.0 4986.0 1085.0 2274.0 1354.0 15.0 4641.0 164.0 1588.0 247 374 185 411 787 1851 2876 1856 794 248 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 44.428571 5.000000
13 3185.000000 6500000.0 $ 2014-12-12 275 99 84263837 3185 5.0 37 92 Annabelle 95 96 [{'publication': 'TheWrap', 'review_date': dat... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 37 Annabelle 1 2014-10-03 37134255 $ 5.4 88605 3.0 October 2014.0 302 2014 5.4 5.3 5.4 5.5 5.5 5.5 5.4 5.6 6.1 5.8 5.4 5.3 5.4 5.3 5.3 5.3 5.3 4.9 5.3 37653.0 23538.0 4067.0 712.0 17648.0 10324.0 4571.0 828.0 243.0 4.0 88605.0 53914.0 27007.0 18639.0 3178.0 467.0 36747.0 364.0 10704.0 4061 4859 3312 5853 10408 17865 20483 13047 6205 2512 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 41.720000 5.125000
14 1338.578947 130000000.0 $ 2015-12-11 529 117 180191634 3868 8.1 64 145 Ant-Man 61 1297 [{'publication': 'RogerEbert.com', 'review_dat... [{'total_reactions': 47, 'neg_reactions': 14, ... 64 Ant-Man 19 2015-07-17 57225526 $ 7.3 353755 17.0 July 2015.0 564 2015 7.4 7.2 7.3 7.9 7.4 7.5 7.3 7.5 8.1 7.2 7.3 7.3 7.4 7.2 7.3 7.8 7.2 6.9 7.5 130533.0 98097.0 18703.0 3041.0 39342.0 21558.0 11257.0 2454.0 576.0 39.0 353755.0 230139.0 107565.0 85405.0 15913.0 2436.0 144174.0 629.0 42602.0 3037 30880 1522 2400 4694 13549 41856 105410 108894 41513 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 66.731707 8.153846
15 1534.000000 5000000.0 $ 2014-10-03 196 93 21197315 2640 6.3 38 41 As Above, So Below 25 74 [{'publication': 'Film.com', 'review_date': da... [{'total_reactions': 3, 'neg_reactions': 0, 'u... 38 As Above, So Below 2 2014-08-29 10284110 $ 6.2 54022 29.0 August 2014.0 220 2014 6.4 6.0 5.7 6.8 6.4 6.5 6.2 5.9 6.9 6.3 6.2 6.1 6.3 6.0 5.7 6.8 6.1 5.1 6.2 20160.0 18441.0 3638.0 240.0 10050.0 5461.0 3222.0 575.0 81.0 5.0 54022.0 34779.0 14474.0 14963.0 2993.0 159.0 24694.0 327.0 8295.0 1238 3491 1114 1955 3635 7074 12545 13005 7217 2748 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 47.333333 7.000000
16 1541.789474 250000000.0 $ 2015-10-02 650 141 458991599 4276 7.1 66 405 Avengers: Age of Ultron 243 1576 [{'publication': 'Chicago Sun-Times', 'review_... [{'total_reactions': 16, 'neg_reactions': 0, '... 66 Avengers: Age of Ultron 19 2015-05-01 191271109 $ 7.4 501696 1.0 May 2015.0 1128 2015 7.5 7.3 7.3 7.9 7.7 7.7 7.5 7.5 8.5 7.1 7.4 7.4 7.5 7.2 7.3 7.8 7.3 6.7 7.5 191615.0 128734.0 23458.0 4590.0 59344.0 33350.0 15919.0 3426.0 996.0 42.0 501696.0 318845.0 156342.0 110946.0 19613.0 3558.0 192711.0 641.0 58723.0 7727 71148 3058 4651 8698 21623 57620 124712 131908 70551 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 68.875000 7.541667
17 18.000000 6000000.0 $ 2014-02-21 26 90 11766 18 5.6 22 6 Barefoot 5 6 [{'publication': 'RogerEbert.com', 'review_dat... [{'total_reactions': 1, 'neg_reactions': 1, 'u... 22 Barefoot 1 2014-02-21 11766 $ 6.6 18316 NaN NaN NaN 51 2014 6.8 6.3 6.4 6.8 6.6 6.8 6.4 6.5 7.0 NaN 6.6 6.5 6.8 6.3 6.4 6.3 6.5 5.2 6.4 7571.0 5510.0 1474.0 53.0 7304.0 4368.0 2131.0 378.0 43.0 NaN 18316.0 7858.0 3113.0 3307.0 1071.0 9.0 8941.0 139.0 2098.0 228 1826 208 398 786 1773 3719 5146 2909 1323 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 29.500000 NaN
18 25.000000 5000000.0 $ 2014-03-21 40 91 72273 25 6.0 40 6 Better Living Through Chemistry 2 5 [{'publication': 'New York Observer', 'review_... [{'total_reactions': 2, 'neg_reactions': 2, 'u... 40 Better Living Through Chemistry 1 2014-03-21 5337 $ 6.3 11800 NaN NaN NaN 31 2014 6.4 6.2 6.2 6.8 6.2 6.1 6.2 6.5 6.5 6.0 6.3 6.3 6.4 6.2 6.2 6.8 6.3 5.6 6.3 3624.0 5023.0 1348.0 10.0 2019.0 935.0 808.0 169.0 2.0 2.0 11800.0 8244.0 2649.0 4140.0 1140.0 8.0 6529.0 132.0 1837.0 144 435 102 208 571 1639 3516 3469 1333 383 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 46.666667 5.000000
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176 318.500000 3300000.0 $ 2015-03-27 544 107 13092000 567 8.9 88 41 Whiplash 36 1031 [{'publication': 'Total Film', 'review_date': ... [{'total_reactions': 41, 'neg_reactions': 5, '... 88 Whiplash 8 2014-10-10 135388 $ 8.5 456446 NaN NaN NaN 791 2014 8.6 8.3 8.1 9.1 8.4 8.5 8.2 8.2 8.9 8.8 8.5 8.5 8.7 8.3 8.1 9.1 8.4 7.9 8.6 196858.0 114083.0 21350.0 3045.0 66421.0 39712.0 17492.0 3504.0 691.0 55.0 456446.0 290221.0 155138.0 94885.0 17430.0 2332.0 201486.0 573.0 46451.0 2502 102630 1101 1666 2671 5497 15200 51706 123008 150465 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 87.857143 9.333333
177 115.250000 15000000.0 $ 2015-05-01 353 115 37877959 505 7.1 76 42 Wild 15 138 [{'publication': 'New Orleans Times-Picayune',... [{'total_reactions': 3, 'neg_reactions': 0, 'u... 76 Wild 12 2014-12-05 606810 $ 7.1 93033 19.0 December 2014.0 258 2014 7.3 7.0 7.1 7.4 7.4 7.5 7.3 7.3 7.6 7.7 7.1 7.1 7.2 7.0 7.0 7.3 7.1 6.6 7.2 33107.0 31820.0 9056.0 225.0 22923.0 11717.0 7596.0 2013.0 74.0 15.0 93033.0 54644.0 21020.0 23749.0 6894.0 149.0 45828.0 397.0 13191.0 927 5456 486 842 1690 4819 14006 31428 24453 8926 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 75.764706 8.500000
178 410.500000 6000000.0 $ 2014-09-05 148 106 3588432 753 7.5 43 18 Wish I Was Here 5 55 [{'publication': 'Hitfix', 'review_date': date... [{'total_reactions': 1, 'neg_reactions': 0, 'u... 43 Wish I Was Here 2 2014-07-18 484401 $ 6.7 31065 25.0 July 2014.0 95 2014 6.8 6.5 6.5 7.3 6.8 6.9 6.7 6.8 7.0 7.5 6.7 6.6 6.8 6.5 6.4 7.2 6.6 5.7 6.7 12185.0 10939.0 2196.0 53.0 6414.0 3377.0 2162.0 456.0 20.0 2.0 31065.0 19856.0 8635.0 8605.0 1692.0 32.0 15683.0 195.0 4906.0 608 2513 333 483 1190 2713 6069 8953 5728 2475 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 48.375000 NaN
179 723.214286 11000000.0 $ 2015-08-07 206 109 33305037 2011 7.2 51 25 Woman in Gold 8 65 [{'publication': 'New York Observer', 'review_... [{'total_reactions': 11, 'neg_reactions': 0, '... 51 Woman in Gold 14 2015-04-03 2091551 $ 7.3 37995 10.0 April 2015.0 157 2015 7.4 7.2 7.5 7.8 7.6 7.7 7.5 7.8 8.1 7.4 7.3 7.2 7.2 7.1 7.4 7.6 7.2 6.2 7.4 10647.0 12575.0 6181.0 98.0 10674.0 4577.0 3610.0 1670.0 42.0 7.0 37995.0 20269.0 5922.0 8753.0 4409.0 54.0 17640.0 288.0 5520.0 419 3827 121 207 435 1338 4322 11037 11406 4883 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 55.200000 10.000000

180 rows × 99 columns


In [16]:
dataset_dir = holcrawl.shared._get_dataset_dir_path()

In [17]:
df = pd.read_csv(os.path.join(dataset_dir, 'movies_dataset.csv'))

In [18]:
pd.options.display.max_columns = 999
df.ix[0:2]


Out[18]:
avg_screens budget budget_currency closing_date critic_review_count duration gross_income max_screens mc_avg_user_score mc_metascore mc_mixed_rating_frequency mc_movie_name mc_negative_rating_frequency mc_positive_rating_frequency mc_pro_critic_reviews mc_user_reviews metascore name num_weekends opening_weekend_date opening_weekend_income opening_weekend_income_currency rating rating_count release_day release_month release_year user_review_count year avg_rating_per_demo.aged_18-29 avg_rating_per_demo.aged_30-44 avg_rating_per_demo.aged_45+ avg_rating_per_demo.aged_under_18 avg_rating_per_demo.females avg_rating_per_demo.females_aged_18-29 avg_rating_per_demo.females_aged_30-44 avg_rating_per_demo.females_aged_45+ avg_rating_per_demo.females_under_18 avg_rating_per_demo.imdb_staff avg_rating_per_demo.imdb_users avg_rating_per_demo.males avg_rating_per_demo.males_aged_18-29 avg_rating_per_demo.males_aged_30-44 avg_rating_per_demo.males_aged_45+ avg_rating_per_demo.males_under_18 avg_rating_per_demo.non-us_users avg_rating_per_demo.top_1000_voters avg_rating_per_demo.us_users votes_per_demo.aged_18-29 votes_per_demo.aged_30-44 votes_per_demo.aged_45+ votes_per_demo.aged_under_18 votes_per_demo.females votes_per_demo.females_aged_18-29 votes_per_demo.females_aged_30-44 votes_per_demo.females_aged_45+ votes_per_demo.females_under_18 votes_per_demo.imdb_staff votes_per_demo.imdb_users votes_per_demo.males votes_per_demo.males_aged_18-29 votes_per_demo.males_aged_30-44 votes_per_demo.males_aged_45+ votes_per_demo.males_under_18 votes_per_demo.non-us_users votes_per_demo.top_1000_voters votes_per_demo.us_users rating_freq.1 rating_freq.10 rating_freq.2 rating_freq.3 rating_freq.4 rating_freq.5 rating_freq.6 rating_freq.7 rating_freq.8 rating_freq.9 genres.action genres.adventure genres.animation genres.biography genres.comedy genres.crime genres.documentary genres.drama genres.family genres.fantasy genres.history genres.horror genres.music genres.musical genres.mystery genres.romance genres.sci-fi genres.sport genres.thriller genres.war avg_mc_critic_by_opening avg_mc_user_by_opening
0 45.000000 4000000.0 $ 2014-04-18 94 93 9134 45 6.8 44 10 13 Sins 3 23 [{'critic': 'Christy Lemire', 'publication': '... [{'total_reactions': 1, 'user': 'Tss5078', 'po... 44 13 Sins 1 2014-04-18 9134 $ 6.3 25068 NaN NaN NaN 69 2014 6.4 6.2 6.3 7.3 6.4 6.5 6.3 6.5 7.6 7.0 6.3 6.3 6.4 6.2 6.3 7.3 6.2 5.8 6.4 8575.0 10068.0 2256.0 55.0 3968.0 1882.0 1525.0 325.0 11.0 4.0 25068.0 17778.0 6605.0 8426.0 1889.0 44.0 12964.0 230.0 3845.0 337 1254 280 544 1255 3351 6991 6984 3093 979 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 49.700000 NaN
1 2605.375000 50000000.0 $ 2014-11-07 335 112 191616238 3426 8.1 71 81 22 Jump Street 42 621 [{'critic': 'Ty Burr', 'publication': 'Boston ... [{'total_reactions': 1, 'user': 'travellyn', '... 71 22 Jump Street 8 2014-06-13 57071445 $ 7.1 274236 13.0 June 2014.0 322 2014 7.3 6.7 6.3 7.9 7.1 7.3 6.8 6.4 8.2 6.1 7.1 7.0 7.3 6.7 6.3 7.8 6.9 6.2 7.2 128688.0 65135.0 9028.0 2267.0 40031.0 25654.0 8858.0 1383.0 620.0 16.0 274236.0 180467.0 101712.0 55439.0 7490.0 1628.0 119172.0 506.0 35147.0 2883 25688 1901 3165 6626 16774 43760 81836 64989 26614 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72.844444 7.833333
2 1891.833333 110000000.0 $ 2014-05-16 367 102 106369117 3490 6.0 48 179 300: Rise of an Empire 105 277 [{'critic': 'Richard Roeper', 'publication': '... [{'total_reactions': 4, 'user': 'LXQUICKJUSTIC... 48 300: Rise of an Empire 12 2014-03-07 45038460 $ 6.2 234420 7.0 March 2014.0 532 2014 6.3 6.1 6.1 6.7 6.3 6.3 6.2 6.2 6.8 6.3 6.2 6.2 6.3 6.1 6.1 6.7 6.1 5.7 6.2 95933.0 73988.0 11634.0 691.0 20214.0 10390.0 6941.0 1219.0 78.0 11.0 234420.0 174235.0 84680.0 66014.0 10209.0 610.0 110596.0 496.0 22633.0 10053 15924 4188 7262 13817 29217 53207 56500 31549 12703 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 49.833333 7.142857

In [ ]: