
            
              import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

plt.style.use('seaborn-darkgrid')


            
              # Load your data and print out a few lines. Perform operations to inspect data
# types and look for instances of missing or possibly errant data.
df = pd.read_csv('data/tmdb_movies_data.csv')
df.head()


            
              df.tail()


            
              df.shape

(10866, 21)


            
              df.dtypes

id                        int64
imdb_id                  object
popularity              float64
budget                    int64
revenue                   int64
original_title           object
cast                     object
homepage                 object
director                 object
tagline                  object
keywords                 object
overview                 object
runtime                   int64
genres                   object
production_companies     object
release_date             object
vote_count                int64
vote_average            float64
release_year              int64
budget_adj              float64
revenue_adj             float64
dtype: object


            
              df.describe()


            
              df.query('runtime == 0 | budget == 0')


            
              df.query('revenue_adj == 0')


            
              df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   imdb_id               10856 non-null  object 
 2   popularity            10866 non-null  float64
 3   budget                10866 non-null  int64  
 4   revenue               10866 non-null  int64  
 5   original_title        10866 non-null  object 
 6   cast                  10790 non-null  object 
 7   homepage              2936 non-null   object 
 8   director              10822 non-null  object 
 9   tagline               8042 non-null   object 
 10  keywords              9373 non-null   object 
 11  overview              10862 non-null  object 
 12  runtime               10866 non-null  int64  
 13  genres                10843 non-null  object 
 14  production_companies  9836 non-null   object 
 15  release_date          10866 non-null  object 
 16  vote_count            10866 non-null  int64  
 17  vote_average          10866 non-null  float64
 18  release_year          10866 non-null  int64  
 19  budget_adj            10866 non-null  float64
 20  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(11)
memory usage: 1.7+ MB


            
              df.sample(5)


            
              # pourcentage de remplissage
1 - (df.isnull().sum() / df.shape[0])

id                      1.000000
imdb_id                 0.999080
popularity              1.000000
budget                  1.000000
revenue                 1.000000
original_title          1.000000
cast                    0.993006
homepage                0.270201
director                0.995951
tagline                 0.740107
keywords                0.862599
overview                0.999632
runtime                 1.000000
genres                  0.997883
production_companies    0.905209
release_date            1.000000
vote_count              1.000000
vote_average            1.000000
release_year            1.000000
budget_adj              1.000000
revenue_adj             1.000000
dtype: float64


            
              df['keywords']

0        monster|dna|tyrannosaurus rex|velociraptor|island
1         future|chase|post-apocalyptic|dystopia|australia
2        based on novel|revolution|dystopia|sequel|dyst...
3                    android|spaceship|jedi|space opera|3d
4                      car race|speed|revenge|suspense|car
                               ...                        
10861                             surfer|surfboard|surfing
10862                            car race|racing|formula 1
10863                             car|trolley|stealing car
10864                                                spoof
10865                  fire|gun|drive|sacrifice|flashlight
Name: keywords, Length: 10866, dtype: object


            
              df.duplicated().sum()

1


            
              # Delete columns with less than 80% in filling rate
# otherwise drop the rows with null values since they're all strings
df_movies = df.copy()
df_movies.drop(labels=['homepage', 'tagline'], axis = 1, inplace = True)
df_movies.dropna(inplace=True)


            
              df_movies.isnull().sum().any()

False


            
              df_movies.shape

(8665, 19)


            
              # convertir la colonne 'release_date' en type date
df_movies['release_date'] = pd.to_datetime(df_movies['release_date'])
df_movies['release_date'].dtype

dtype('<M8[ns]')


            
              df_movies.drop_duplicates(inplace=True)
df_movies.duplicated().sum()

0


            
              df_movies.popularity.hist()

<AxesSubplot:>


            
              df_movies.popularity.plot.box()

<AxesSubplot:>


            
              df_movies.query('popularity < 3').popularity.hist()

<AxesSubplot:>


            
              df_movies.vote_average.hist()

<AxesSubplot:>


            
              df_movies.plot.scatter(x = 'vote_average', y = 'popularity')

<AxesSubplot:xlabel='vote_average', ylabel='popularity'>


            
              df_movies.vote_average.corr(df_movies.popularity)

0.2449651587361198


            
              most_popular_sep = df_movies.popularity.median()
most_popular_sep

0.449377


            
              df_popular_movies = df_movies.loc[df_movies.popularity >= most_popular_sep]
df_popular_movies.shape

(4332, 19)


            
              def parse_list_with_sep(list, sep):
    '''
        Parcourir chaque ligne en isolant les différents items et
        les ajoutant à une liste s'il ne sont pas deja present.
    '''
    list_elem = [elem for row in list for elem in row.split(sep) ]
    return list_elem


actors = pd.Series(parse_list_with_sep(df_popular_movies.cast, '|'))
actors_num_mv = actors.value_counts()
print('Nombre des acteurs qui ont apparu dans plus qu\'un film populaire :')
actors_num_mv[actors_num_mv > 1].shape

Nombre des acteurs qui ont apparu dans plus qu'un film populaire :

(3007,)


            
              actors_num_mv[:15]

Robert De Niro           57
Bruce Willis             49
Nicolas Cage             46
Samuel L. Jackson        46
Johnny Depp              38
Tom Hanks                37
Brad Pitt                35
Sylvester Stallone       35
Morgan Freeman           35
Arnold Schwarzenegger    33
Nicole Kidman            33
Mark Wahlberg            33
Ewan McGregor            33
Matt Damon               33
Liam Neeson              32
dtype: int64


            
              def plot_hist_column_per_actor(column_name, actor_name, color = 'b'):
    actor_mv = df_movies.loc[df_movies.cast.str.contains(actor_name)]
    print(f'Number of movies for {actor_name} : {actor_mv.shape[0]}')
    plt.hist(actor_mv[column_name], color = color, alpha = 0.5)
    plt.xlabel('votes mean')
    plt.ylabel('density')
    plt.title('Distributions of vote mean per actor')

column_name = 'popularity'
plot_hist_column_per_actor(column_name,'Robert De Niro', 'y')
plot_hist_column_per_actor(column_name,'Tom Cruise', 'g')
plot_hist_column_per_actor(column_name,'Johnny Depp', 'w')

Number of movies for Robert De Niro : 68
Number of movies for Tom Cruise : 37
Number of movies for Johnny Depp : 43


            
              mean_popular_mv = df.popularity.quantile(0.75)
mean_popular_mv

0.7138169999999999


            
              depp_movies = df_movies.loc[df_movies.cast.str.contains('Johnny Depp')]
depp_movies.shape

(43, 19)


            
              depp_movies.popularity.plot.box()

<AxesSubplot:>


            
              def remove_outlier(column):
    Q1 , Q3 = column.quantile(0.25), column.quantile(0.75)
    IQ = Q3 - Q1
    max = Q3 + (1.5*IQ)
    min = Q1 - (1.5*IQ)
    print(f'outliers : {column[(column > max) | (column < min)]}')
    return column[(column <= max) & (column >=min)]

depp_movies_pop = remove_outlier(depp_movies.popularity)
depp_movies_pop.shape

outliers : 1921    5.572950
3375    4.955130
4951    6.864067
6555    4.205992
7387    4.965391
Name: popularity, dtype: float64

(38,)


            
              from scipy import stats as st

st.ttest_1samp(depp_movies_pop, popmean=0.71, alternative='less')

Ttest_1sampResult(statistic=3.8912811800694986, pvalue=0.9997995019905364)


            
              genres_bst_pop = pd.Series(parse_list_with_sep(df_popular_movies.genres, '|'))
print(f'Nombre de genres des films populaires : {genres_bst_pop.nunique()}')
ax = genres_bst_pop.value_counts()[:15].plot.bar(title = 'Popularité des genres', figsize=(10,5))
ax.set_xlabel("Genres des films")
ax.set_ylabel("Nombre d'occurence du genre");

Nombre de genres des films populaires : 20


            
              genres_depp = pd.Series(parse_list_with_sep(depp_movies.genres, '|'))
genres_depp.value_counts()[:10].plot.bar()

<AxesSubplot:>


            
              prod_companies = pd.Series(parse_list_with_sep(df_popular_movies.production_companies, '|'))
ax = prod_companies.value_counts()[:10].plot.bar(title = 'Popularité des films selon les sociétés de productions')
ax.set_xlabel("Sociétés de production")
ax.set_ylabel("Nombre d'occurence de la société");


            
              key_words = pd.Series(parse_list_with_sep(df_popular_movies.keywords, '|'))
key_words.value_counts()[:15]

based on novel          167
sex                     119
dystopia                106
duringcreditsstinger    103
woman director           99
new york                 94
prison                   86
sport                    75
musical                  75
london                   74
murder                   70
biography                67
sequel                   67
independent film         67
high school              63
dtype: int64


            
              df_popular_movies.release_year.min() , df_popular_movies.release_year.max()

(1960, 2015)


            
              df_popular_movies.release_year.nunique()

56


            
              genres_bst_pop.nunique()

20


            
              # TODO : partager les années en 15 groupes et étudier le genre le plus dominant pour chaque groupe
# commmençons par voir le genre dominant pour chaque année
df_popular_movies_by_year = df_popular_movies[['release_year', 'genres']].copy()
df_popular_movies_by_year['genres'] = df_popular_movies.groupby(['release_year'], as_index=True)['genres'].transform(lambda x: '|'.join(x))
df_popular_movies_by_year = df_popular_movies_by_year.drop_duplicates(subset=['release_year', 'genres'])
df_popular_movies_by_year.head()


            
              df_popular_movies_by_year.set_index('release_year', inplace=True)
df_popular_movies_by_year.head()


            
              def get_top_genre(genres):
    genres = pd.Series(genres.split('|')).value_counts()
    max = genres.max()
    top_genre = genres[genres == max]
    return (top_genre.index.values[0], top_genre.values[0])

get_top_genre(df_popular_movies_by_year.genres[1966])

('Drama', 4)


            
              df_popular_movies_by_year['top_genre'] = df_popular_movies_by_year.genres.apply(get_top_genre)
df_popular_movies_by_year['genre_count'] = df_popular_movies_by_year.top_genre.apply(lambda x : x[1])
df_popular_movies_by_year['top_genre'] = df_popular_movies_by_year.top_genre.apply(lambda x : x[0])
df_popular_movies_by_year.head()


            
              # split the years into 10 groups
years_grps = np.array_split(df_popular_movies_by_year.index.sort_values().values, 10)
years_grps

[array([1960, 1961, 1962, 1963, 1964, 1965]),
 array([1966, 1967, 1968, 1969, 1970, 1971]),
 array([1972, 1973, 1974, 1975, 1976, 1977]),
 array([1978, 1979, 1980, 1981, 1982, 1983]),
 array([1984, 1985, 1986, 1987, 1988, 1989]),
 array([1990, 1991, 1992, 1993, 1994, 1995]),
 array([1996, 1997, 1998, 1999, 2000]),
 array([2001, 2002, 2003, 2004, 2005]),
 array([2006, 2007, 2008, 2009, 2010]),
 array([2011, 2012, 2013, 2014, 2015])]


            
              def get_top_genre_by_yr_grp():
    '''
        Iterate over years groups and for each group
        get genre with max popularity by summing over
        the occurences.
    '''
    dict = {}
    for i ,yr_grp in enumerate(years_grps):
        top_genre_count = df_popular_movies_by_year.loc[yr_grp].groupby('top_genre')['genre_count'].sum()
        max_val = top_genre_count.max()
        max_genre = top_genre_count[top_genre_count == max_val]
        dict[i] = (max_genre.index.values[0], max_genre.values[0])
    return dict

top_genre_by_yr_grp = get_top_genre_by_yr_grp()


            
              heights = [x[1] for x in top_genre_by_yr_grp.values()]
labels_years = ['{}-{}'.format(yr_grp[0],yr_grp[len(yr_grp)-1]) for yr_grp in years_grps]
labels_genres = [x[0] for x in top_genre_by_yr_grp.values()]
labels = ['{}\n{}'.format(lbl_yr,labels_genres[i]) for i,lbl_yr in enumerate(labels_years)]


plt.figure(figsize=(10, 7))
indx = np.arange(len(years_grps))
plt.bar(indx , height=heights)
plt.xticks(indx, labels)
plt.title("Popularité des genres selon les années")
plt.xlabel("Groupe d'année - Genre populaire")
plt.ylabel("Nombre d'occurrence du genre dans un groupe d'année");


            
              ax = df_popular_movies.director.value_counts()[:15].plot.bar(title = 'Popularité des films selon les réalisateurs')
ax.set_xlabel("Nom du réalisateur")
ax.set_ylabel("Nombre d'occurence du nom");

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt\|Bryce Dallas Howard\|Irrfan Khan\|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Twenty-two years after the events of Jurassic ...	124	Action\|Adventure\|Science Fiction\|Thriller	Universal Studios\|Amblin Entertainment\|Legenda...	6/9/2015	5562	6.5	2015	137999939.3	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy\|Charlize Theron\|Hugh Keays-Byrne\|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	An apocalyptic story set in the furthest reach...	120	Action\|Adventure\|Science Fiction\|Thriller	Village Roadshow Pictures\|Kennedy Miller Produ...	5/13/2015	6185	7.1	2015	137999939.3	3.481613e+08
2	262500	tt2908446	13.112507	110000000	295238201	Insurgent	Shailene Woodley\|Theo James\|Kate Winslet\|Ansel...	http://www.thedivergentseries.movie/#insurgent	Robert Schwentke	One Choice Can Destroy You	...	Beatrice Prior must confront her inner demons ...	119	Adventure\|Science Fiction\|Thriller	Summit Entertainment\|Mandeville Films\|Red Wago...	3/18/2015	2480	6.3	2015	101199955.5	2.716190e+08
3	140607	tt2488496	11.173104	200000000	2068178225	Star Wars: The Force Awakens	Harrison Ford\|Mark Hamill\|Carrie Fisher\|Adam D...	http://www.starwars.com/films/star-wars-episod...	J.J. Abrams	Every generation has a story.	...	Thirty years after defeating the Galactic Empi...	136	Action\|Adventure\|Science Fiction\|Fantasy	Lucasfilm\|Truenorth Productions\|Bad Robot	12/15/2015	5292	7.5	2015	183999919.0	1.902723e+09
4	168259	tt2820852	9.335014	190000000	1506249360	Furious 7	Vin Diesel\|Paul Walker\|Jason Statham\|Michelle ...	http://www.furious7.com/	James Wan	Vengeance Hits Home	...	Deckard Shaw seeks revenge against Dominic Tor...	137	Action\|Crime\|Thriller	Universal Pictures\|Original Film\|Media Rights ...	4/1/2015	2947	7.3	2015	174799923.1	1.385749e+09

	id	imdb_id	popularity	budget	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj
10861	21	tt0060371	0.080598	0	The Endless Summer	Michael Hynson\|Robert August\|Lord 'Tally Ho' B...	NaN	Bruce Brown	NaN	...	The Endless Summer, by Bruce Brown, is one of ...	95	Documentary	Bruce Brown Films	6/15/1966	11	7.4	1966	0.0000
10862	20379	tt0060472	0.065543	0	Grand Prix	James Garner\|Eva Marie Saint\|Yves Montand\|Tosh...	NaN	John Frankenheimer	Cinerama sweeps YOU into a drama of speed and ...	...	Grand Prix driver Pete Aron is fired by his te...	176	Action\|Adventure\|Drama	Cherokee Productions\|Joel Productions\|Douglas ...	12/21/1966	20	5.7	1966	0.0000
10863	39768	tt0060161	0.065141	0	Beregis Avtomobilya	Innokentiy Smoktunovskiy\|Oleg Efremov\|Georgi Z...	NaN	Eldar Ryazanov	NaN	...	An insurance agent who moonlights as a carthie...	94	Mystery\|Comedy	Mosfilm	1/1/1966	11	6.5	1966	0.0000
10864	21449	tt0061177	0.064317	0	What's Up, Tiger Lily?	Tatsuya Mihashi\|Akiko Wakabayashi\|Mie Hama\|Joh...	NaN	Woody Allen	WOODY ALLEN STRIKES BACK!	...	In comic Woody Allen's film debut, he took the...	80	Action\|Comedy	Benedict Pictures Corp.	11/2/1966	22	5.4	1966	0.0000
10865	22293	tt0060666	0.035919	19000	Manos: The Hands of Fate	Harold P. Warren\|Tom Neyman\|John Reynolds\|Dian...	NaN	Harold P. Warren	It's Shocking! It's Beyond Your Imagination!	...	A family gets lost on the road and stumbles up...	74	Horror	Norm-Iris	11/15/1966	15	1.5	1966	127642.2792

	id	popularity	budget	revenue	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj
count	10866.000000	10866.000000	1.086600e+04	1.086600e+04	10866.000000	10866.000000	10866.000000	10866.000000	1.086600e+04	1.086600e+04
mean	66064.177434	0.646441	1.462570e+07	3.982332e+07	102.070863	217.389748	5.974922	2001.322658	1.755104e+07	5.136436e+07
std	92130.136561	1.000185	3.091321e+07	1.170035e+08	31.381405	575.619058	0.935142	12.812941	3.430616e+07	1.446325e+08
min	5.000000	0.000065	0.000000e+00	0.000000e+00	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00
25%	10596.250000	0.207583	0.000000e+00	0.000000e+00	90.000000	17.000000	5.400000	1995.000000	0.000000e+00	0.000000e+00
50%	20669.000000	0.383856	0.000000e+00	0.000000e+00	99.000000	38.000000	6.000000	2006.000000	0.000000e+00	0.000000e+00
75%	75610.000000	0.713817	1.500000e+07	2.400000e+07	111.000000	145.750000	6.600000	2011.000000	2.085325e+07	3.369710e+07
max	417859.000000	32.985763	4.250000e+08	2.781506e+09	900.000000	9767.000000	9.200000	2015.000000	4.250000e+08	2.827124e+09

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
30	280996	tt3168230	3.927333	0	29355203	Mr. Holmes	Ian McKellen\|Milo Parker\|Laura Linney\|Hattie M...	http://www.mrholmesfilm.com/	Bill Condon	The man behind the myth	...	The story is set in 1947, following a long-ret...	103	Mystery\|Drama	BBC Films\|See-Saw Films\|FilmNation Entertainme...	6/19/2015	425	6.4	2015	0.0	2.700677e+07
36	339527	tt1291570	3.358321	0	22354572	Solace	Abbie Cornish\|Jeffrey Dean Morgan\|Colin Farrel...	NaN	Afonso Poyart	A serial killer who can see your future, a psy...	...	A psychic doctor, John Clancy, works with an F...	101	Crime\|Drama\|Mystery	Eden Rock Media\|FilmNation Entertainment\|Flynn...	9/3/2015	474	6.2	2015	0.0	2.056620e+07
72	284289	tt2911668	2.272044	0	45895	Beyond the Reach	Michael Douglas\|Jeremy Irvine\|Hanna Mangan Law...	NaN	Jean-Baptiste LÃ©onetti	NaN	...	A high-rolling corporate shark and his impover...	95	Thriller	Furthur Films	4/17/2015	81	5.5	2015	0.0	4.222338e+04
74	347096	tt3478232	2.165433	0	0	Mythica: The Darkspore	Melanie Stone\|Kevin Sorbo\|Adam Johnson\|Jake St...	http://www.mythicamovie.com/#!blank/wufvh	Anne K. Black	NaN	...	When Teelaâ€™s sister is murdered and a powerf...	108	Action\|Adventure\|Fantasy	Arrowstorm Entertainment	6/24/2015	27	5.1	2015	0.0	0.000000e+00
75	308369	tt2582496	2.141506	0	0	Me and Earl and the Dying Girl	Thomas Mann\|RJ Cyler\|Olivia Cooke\|Connie Britt...	http://www.foxsearchlight.com/meandearlandthed...	Alfonso Gomez-Rejon	A Little Friendship Never Killed Anyone.	...	Greg is coasting through senior year of high s...	105	Comedy\|Drama	Indian Paintbrush	6/12/2015	569	7.7	2015	0.0	0.000000e+00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10860	5060	tt0060214	0.087034	0	0	Carry On Screaming!	Kenneth Williams\|Jim Dale\|Harry H. Corbett\|Joa...	NaN	Gerald Thomas	Carry On Screaming with the Hilarious CARRY ON...	...	The sinister Dr Watt has an evil scheme going....	87	Comedy	Peter Rogers Productions\|Anglo-Amalgamated Fil...	5/20/1966	13	7.0	1966	0.0	0.000000e+00
10861	21	tt0060371	0.080598	0	0	The Endless Summer	Michael Hynson\|Robert August\|Lord 'Tally Ho' B...	NaN	Bruce Brown	NaN	...	The Endless Summer, by Bruce Brown, is one of ...	95	Documentary	Bruce Brown Films	6/15/1966	11	7.4	1966	0.0	0.000000e+00
10862	20379	tt0060472	0.065543	0	0	Grand Prix	James Garner\|Eva Marie Saint\|Yves Montand\|Tosh...	NaN	John Frankenheimer	Cinerama sweeps YOU into a drama of speed and ...	...	Grand Prix driver Pete Aron is fired by his te...	176	Action\|Adventure\|Drama	Cherokee Productions\|Joel Productions\|Douglas ...	12/21/1966	20	5.7	1966	0.0	0.000000e+00
10863	39768	tt0060161	0.065141	0	0	Beregis Avtomobilya	Innokentiy Smoktunovskiy\|Oleg Efremov\|Georgi Z...	NaN	Eldar Ryazanov	NaN	...	An insurance agent who moonlights as a carthie...	94	Mystery\|Comedy	Mosfilm	1/1/1966	11	6.5	1966	0.0	0.000000e+00
10864	21449	tt0061177	0.064317	0	0	What's Up, Tiger Lily?	Tatsuya Mihashi\|Akiko Wakabayashi\|Mie Hama\|Joh...	NaN	Woody Allen	WOODY ALLEN STRIKES BACK!	...	In comic Woody Allen's film debut, he took the...	80	Action\|Comedy	Benedict Pictures Corp.	11/2/1966	22	5.4	1966	0.0	0.000000e+00

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
48	265208	tt2231253	2.932340	30000000	0	Wild Card	Jason Statham\|Michael Angarano\|Milo Ventimigli...	NaN	Simon West	Never bet against a man with a killer hand.	...	When a Las Vegas bodyguard with lethal skills ...	92	Thriller\|Crime\|Drama	Current Entertainment\|Lionsgate\|Sierra / Affin...	1/14/2015	481	5.3	2015	2.759999e+07	0.0
67	334074	tt3247714	2.331636	20000000	0	Survivor	Pierce Brosnan\|Milla Jovovich\|Dylan McDermott\|...	http://survivormovie.com/	James McTeigue	His Next Target is Now Hunting Him	...	A Foreign Service Officer in London tries to p...	96	Crime\|Thriller\|Action	Nu Image Films\|Winkler Films\|Millennium Films\|...	5/21/2015	280	5.4	2015	1.839999e+07	0.0
74	347096	tt3478232	2.165433	0	0	Mythica: The Darkspore	Melanie Stone\|Kevin Sorbo\|Adam Johnson\|Jake St...	http://www.mythicamovie.com/#!blank/wufvh	Anne K. Black	NaN	...	When Teelaâ€™s sister is murdered and a powerf...	108	Action\|Adventure\|Fantasy	Arrowstorm Entertainment	6/24/2015	27	5.1	2015	0.000000e+00	0.0
75	308369	tt2582496	2.141506	0	0	Me and Earl and the Dying Girl	Thomas Mann\|RJ Cyler\|Olivia Cooke\|Connie Britt...	http://www.foxsearchlight.com/meandearlandthed...	Alfonso Gomez-Rejon	A Little Friendship Never Killed Anyone.	...	Greg is coasting through senior year of high s...	105	Comedy\|Drama	Indian Paintbrush	6/12/2015	569	7.7	2015	0.000000e+00	0.0
92	370687	tt3608646	1.876037	0	0	Mythica: The Necromancer	Melanie Stone\|Adam Johnson\|Kevin Sorbo\|Nicola ...	http://www.mythicamovie.com/#!blank/y9ake	A. Todd Smith	NaN	...	Mallister takes Thane prisoner and forces Mare...	0	Fantasy\|Action\|Adventure	Arrowstorm Entertainment\|Camera 40 Productions...	12/19/2015	11	5.4	2015	0.000000e+00	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10861	21	tt0060371	0.080598	0	0	The Endless Summer	Michael Hynson\|Robert August\|Lord 'Tally Ho' B...	NaN	Bruce Brown	NaN	...	The Endless Summer, by Bruce Brown, is one of ...	95	Documentary	Bruce Brown Films	6/15/1966	11	7.4	1966	0.000000e+00	0.0
10862	20379	tt0060472	0.065543	0	0	Grand Prix	James Garner\|Eva Marie Saint\|Yves Montand\|Tosh...	NaN	John Frankenheimer	Cinerama sweeps YOU into a drama of speed and ...	...	Grand Prix driver Pete Aron is fired by his te...	176	Action\|Adventure\|Drama	Cherokee Productions\|Joel Productions\|Douglas ...	12/21/1966	20	5.7	1966	0.000000e+00	0.0
10863	39768	tt0060161	0.065141	0	0	Beregis Avtomobilya	Innokentiy Smoktunovskiy\|Oleg Efremov\|Georgi Z...	NaN	Eldar Ryazanov	NaN	...	An insurance agent who moonlights as a carthie...	94	Mystery\|Comedy	Mosfilm	1/1/1966	11	6.5	1966	0.000000e+00	0.0
10864	21449	tt0061177	0.064317	0	0	What's Up, Tiger Lily?	Tatsuya Mihashi\|Akiko Wakabayashi\|Mie Hama\|Joh...	NaN	Woody Allen	WOODY ALLEN STRIKES BACK!	...	In comic Woody Allen's film debut, he took the...	80	Action\|Comedy	Benedict Pictures Corp.	11/2/1966	22	5.4	1966	0.000000e+00	0.0
10865	22293	tt0060666	0.035919	19000	0	Manos: The Hands of Fate	Harold P. Warren\|Tom Neyman\|John Reynolds\|Dian...	NaN	Harold P. Warren	It's Shocking! It's Beyond Your Imagination!	...	A family gets lost on the road and stumbles up...	74	Horror	Norm-Iris	11/15/1966	15	1.5	1966	1.276423e+05	0.0

Project: Tmdb movies Dataset Analysis¶

Table of Contents¶

Introduction¶

Data Wrangling¶

Exploratory Data Analysis¶

Question 1 : Est ce qu'il y a une relation entre la popularité et la moyenne des votes?¶

Question 2: Est ce qu'il y'a des acteurs commun dans les films les plus populaires?¶

Question 3: Est ce que la présence de ces acteurs permet d'augmenter la popularité du film?¶

Question 4: Quels sont les genres de films les plus populaires?¶

Question 5: Quel est la société de production ayant les films les plus populaires?¶

Question 6: Quels sont les mots clés qui se répètent dans les films les plus pouplaires?¶

Question 7: Est ce que la tendance des genres des films populaires a changé avec les années?¶

Question 8: Quel est le réalisateur ayant produit les films les plus populaires?¶

Conclusions¶

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
1504	17445	tt1384590	0.725654	3500000	0	Green Lantern: First Flight	Christopher Meloni\|Victor Garber\|Michael Madse...	http://www.warnervideo.com/greenlanterndvd/	Lauren Montgomery	Superman was born a hero. Batman became a hero...	...	Test pilot Hal Jordan finds himself recruited ...	75	Animation\|Science Fiction\|Action	DC Comics\|Warner Bros. Animation	7/28/2009	51	6.2	2009	3.557402e+06	0.000000e+00
10523	1857	tt0092106	0.438751	0	5706456	The Transformers: The Movie	Judd Nelson\|Peter Cullen\|Frank Welker\|Leonard ...	NaN	Nelson Shin	Beyond good. Beyond evil. Beyond your wildest ...	...	The Autobots must stop a colossal planet-consu...	84	Animation	De Laurentiis Entertainment Group (DEG)\|Marvel...	8/8/1986	70	7.2	1986	0.000000e+00	1.135246e+07
4623	94380	tt1928330	0.385121	0	0	Bad Ass	Danny Trejo\|Charles S. Dutton\|Ron Perlman\|Joyf...	http://www.heisbadass.com/	Craig Moss	They messed with the wrong senior citizen.	...	Decorated Vietnam hero Frank Vega returns home...	90	Action\|Drama	Amber Lamps	4/12/2012	87	5.4	2012	0.000000e+00	0.000000e+00
9991	10166	tt0100944	1.125794	0	0	The Witches	Anjelica Huston\|Mai Zetterling\|Jasen Fisher\|Ja...	NaN	Nicolas Roeg	Saving the world from witches is a tall order ...	...	A young boy named Luke and his grandmother go ...	91	Adventure\|Fantasy\|Horror	Lorimar Film Entertainment\|Jim Henson Productions	2/16/1990	88	6.6	1990	0.000000e+00	0.000000e+00
5032	12090	tt0305711	0.648946	18000000	101564935	Just Married	Ashton Kutcher\|Brittany Murphy\|Christian Kane\|...	NaN	Shawn Levy	Welcome to the honeymoon from hell.	...	Tom Leezak and Sarah McNerney fall in love and...	95	Comedy	Twentieth Century Fox Film Corporation\|Robert ...	1/9/2003	242	5.3	2003	2.133635e+07	1.203903e+08

	release_year	genres
0	2015	Action\|Adventure\|Science Fiction\|Thriller\|Acti...
629	2014	Adventure\|Drama\|Science Fiction\|Action\|Science...
1329	1977	Adventure\|Action\|Science Fiction\|Adventure\|Act...
1386	2009	Action\|Adventure\|Fantasy\|Science Fiction\|Drama...
1919	2010	Action\|Thriller\|Science Fiction\|Mystery\|Advent...