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In [4]:



    


%matplotlib inline
import pandas as pd
import seaborn as sbn
sbn.set()



    











In [5]:



    


from IPython.core.display import HTML
css = open('style-table.css').read() + open('style-notebook.css').read()
HTML('<style>{}</style>'.format(css))



    


















    
Out[5]:























In [6]:



    


titles = pd.DataFrame.from_csv('data/titles.csv', index_col=None)
titles.head()



    


















    
Out[6]:










  
    

      
      title
      year
    

  
  
    

      0
      The Rising Son
      1990
    

    

      1
      Ashes of Kukulcan
      2016
    

    

      2
      The Thousand Plane Raid
      1969
    

    

      3
      Crucea de piatra
      1993
    

    

      4
      The 86
      2015
    

  




















In [7]:



    


cast = pd.DataFrame.from_csv('data/cast.csv', index_col=None)
cast.head()



    


















    
Out[7]:










  
    

      
      title
      year
      name
      type
      character
      n
    

  
  
    

      0
      Suuri illusioni
      1985
      Homo $
      actor
      Guests
      22
    

    

      1
      Gangsta Rap: The Glockumentary
      2007
      Too $hort
      actor
      Himself
      NaN
    

    

      2
      Menace II Society
      1993
      Too $hort
      actor
      Lew-Loc
      27
    

    

      3
      Porndogs: The Adventures of Sadie
      2009
      Too $hort
      actor
      Bosco
      3
    

    

      4
      Stop Pepper Palmer
      2014
      Too $hort
      actor
      Himself
      NaN

What are the ten most common movie names of all time?





In [14]:



    


titles.title.value_counts().head(10)



    


















    
Out[14]:








Hamlet                  19
Macbeth                 14
Carmen                  14
The Three Musketeers    12
Maya                    11
She                     11
The Outsider            11
Blood Money             11
Victory                 10
Eva                     10
dtype: int64

















In [ ]:

Which three years of the 1930s saw the most films released?





In [17]:



    


titles[titles.year // 10 == 193].year.value_counts().head(3)



    


















    
Out[17]:








1937    1186
1936    1130
1938    1117
dtype: int64

















In [ ]:

Plot the number of films that have been released each decade over the history of cinema.





In [40]:



    


titles['decade'] = ((titles.year // 10) * 10)
titles.decade.value_counts().sort_index().plot(kind='bar')



    


















    
Out[40]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee49c4e10>










    
























In [41]:



    


titles.year.value_counts().sort_index().plot(kind='bar')



    


















    
Out[41]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee47200f0>

Plot the number of "Hamlet" films made each decade.





In [42]:



    


titles['decade'] = ((titles.year // 10) * 10)
titles[titles.title=='Hamlet'].decade.value_counts().sort_index().plot(kind='bar')



    


















    
Out[42]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee4669b00>










    
























In [ ]:

Plot the number of "Rustler" characters in each decade of the history of film.





In [54]:



    


cast['decade'] = ((cast.year // 10) * 10)
cast[cast.character=='Rustler'].decade.value_counts().sort_index().plot(kind='bar')



    


















    
Out[54]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee3f0e9e8>










    
























In [ ]:

Plot the number of "Hamlet" characters each decade.





In [45]:



    


cast['decade'] = ((cast.year // 10) * 10)
cast[cast.character=='Hamlet'].decade.value_counts().sort_index().plot(kind='bar')



    


















    
Out[45]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee4347f60>










    
























In [ ]:

What are the 11 most common character names in movie history?





In [12]:



    


cast.character.value_counts().head(11)



    


















    
Out[12]:








Himself        18916
Dancer         11076
Extra           9158
Reporter        7655
Doctor          6850
Policeman       6506
Student         6377
Bartender       6183
Nurse           6171
Party Guest     5923
Minor Role      5920
dtype: int64

















In [ ]:

Who are the 10 people most often credited as "Herself" in film history?





In [18]:



    


cast[cast.character=='Herself'].name.value_counts().head(10)



    


















    
Out[18]:








Joyce Brothers         14
Queen Elizabeth II     11
Margaret Thatcher       8
Joan Rivers             7
Mary Jo Pehl            7
Lili?n Garc?a           6
Kareena Kapoor          5
Sally Jessy Raphael     5
Juhi Chawla             5
Caroline Rhea           5
dtype: int64

















In [ ]:

Who are the 10 people most often credited as "Himself" in film history?





In [19]:



    


cast[cast.character=='Himself'].name.value_counts().head(10)



    


















    
Out[19]:








Adolf Hitler             93
Richard Nixon            39
Ronald Reagan            31
John F. Kennedy          26
Ron Jeremy               24
Bill Clinton             20
Franklin D. Roosevelt    20
George W. Bush           20
Winston Churchill        20
Martin Luther King       19
dtype: int64

















In [ ]:

Which actors or actresses appeared in the most movies in the year 1945?





In [20]:



    


cast[cast.year==1945].name.value_counts().head(10)



    


















    
Out[20]:








Emmett Vogan        39
Sam (II) Harris     30
Bess Flowers        28
Harold Miller       28
Nolan Leary         27
Frank O'Connor      26
Franklyn Farnum     25
Tom London          24
Charles Sullivan    24
Edmund Cobb         24
dtype: int64

















In [ ]:

Which actors or actresses appeared in the most movies in the year 1985?





In [21]:



    


cast[cast.year==1985].name.value_counts().head(10)



    


















    
Out[21]:








Mammootty        19
Shakti Kapoor    19
Sukumari         17
Lou Scheimer     15
Aruna Irani      14
Deven Verma      13
Raj Babbar       13
Rajesh Khanna    13
Mohanlal         13
Jagdish Raj      12
dtype: int64

















In [ ]:

Plot how many roles Mammootty has played in each year of his career.





In [22]:



    


cast[cast.name=='Ron Jeremy'].year.value_counts().sort_index().plot(kind='bar')



    


















    
Out[22]:








<matplotlib.axes._subplots.AxesSubplot at 0x7ff5936d7be0>










    
























In [ ]:

What are the 10 most frequent roles that start with the phrase "Patron in"?





In [59]:



    


c = cast[cast.character.str.startswith('Patron in')]
c.character.value_counts().head(10)



    


















    
Out[59]:








Patron in Frisky Rabbit         16
Patron in the Coffee House       9
Patron in Chinese Restaurant     9
Patron in Billiard Parlor        5
Patron in Bar                    4
Patron in Club                   3
Patron in restaurant             3
Patron in Restaurant             3
Patron in cabaret                3
Patron in Quiet Bar              2
dtype: int64

















In [ ]:

What are the 10 most frequent roles that start with the word "Science"?





In [60]:



    


c = cast[cast.character.str.startswith('Science')]
c.character.value_counts().head(10)



    


















    
Out[60]:








Science Teacher         54
Science Student          8
Science Fair Student     8
Science Club Member      5
Science Reporter         5
Science Kid              4
Science Promo Cadet      4
Science Fair Judge       4
Science teacher          3
Science Officer          3
dtype: int64

















In [ ]:

Plot the n-values of the roles that Judi Dench has played over her career.





In [67]:



    


c=cast[cast.name=="Judi Dench"]
#c.n.value_counts().sort_index().plot(kind="bar")
c.plot(kind="scatter",x='year',y='n',alpha=0.5)



    


















    
Out[67]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee3d19d30>










    
























In [ ]:

Plot the n-values of Cary Grant's roles through his career.





In [69]:



    


d=cast[cast.name=="Cary Grant"]
#c.n.value_counts().sort_index().plot(kind="bar")
d.plot(kind="scatter",x='year',y='n',alpha=0.5)



    


















    
Out[69]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f8ee3c8f0f0>










    
























In [ ]:

Plot the n-value of the roles that Sidney Poitier has acted over the years.





In [25]:



    


cast[cast.name=='Ron Jeremy'].plot(kind='scatter',x='year',y='n')



    


















    
Out[25]:








<matplotlib.axes._subplots.AxesSubplot at 0x7ff59360ada0>










    
























In [ ]:

How many leading (n=1) roles were available to actors, and how many to actresses, in the 1950s?





In [28]:



    


cast[(cast.year//10==195)&(cast.n==1)].type.value_counts()



    


















    
Out[28]:








actor      6389
actress    2813
dtype: int64

















In [ ]:

How many supporting (n=2) roles were available to actors, and how many to actresses, in the 1950s?





In [29]:



    


cast[(cast.year//10==195)&(cast.n==2)].type.value_counts()



    


















    
Out[29]:








actress    4405
actor      4382
dtype: int64

















In [ ]:

Content source: RobbieNesmith/PandasTutorial

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