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In [8]:

    
%matplotlib inline
import pandas as pd



In [9]:

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









    Out[9]:



In [10]:

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









    Out[10]:






  
    
      
      title
      year
    
  
  
    
      0
      Ligaw na daigdig
      1962
    
    
      1
      Sluby ulanskie
      1934
    
    
      2
      The House of the Seven Gables
      1940
    
    
      3
      Mandala - Il simbolo
      2008
    
    
      4
      Shi bian
      1958



In [11]:

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









    Out[11]:






  
    
      
      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



In [ ]:

Define a year as a "Superman year" whose films feature more Superman characters than Batman. How many years in film history have been Superman years?



In [17]:

    
c = cast
c = c[(c.character == 'Superman') | (c.character == 'Batman')]
c = c.groupby(['year', 'character']).size()
c = c.unstack()
c = c.fillna(0)
c.head()



In [18]:

    
d = c.Superman - c.Batman
print('Superman years:')
print(len(d[d > 0.0]))









    



Superman years:
13

How many years have been "Batman years", with more Batman characters than Superman characters?



In [19]:

    
d = c.Superman - c.Batman
print('Batman years:')
print(len(d[d < 0.0]))









    



Batman years:
23



In [ ]:

Plot the number of actor roles each year and the number of actress roles each year over the history of film.



In [23]:

    
c = cast
#c = c[(c.character == 'Superman') | (c.character == 'Batman')]
c = c.groupby(['year', 'type']).size()
c = c.unstack()
c = c.fillna(0)
c.plot()









    Out[23]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8c70cef438>



In [ ]:

Plot the number of actor roles each year and the number of actress roles each year, but this time as a kind='area' plot.



In [24]:

    
c.plot(kind='area')









    Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8cc19da780>



In [ ]:

Plot the difference between the number of actor roles each year and the number of actress roles each year over the history of film.



In [29]:

    
c = cast
c = c.groupby(['year', 'type']).size()
c = c.unstack('type')
(c.actor - c.actress).plot()









    Out[29]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8cc19c6748>



In [ ]:

Plot the fraction of roles that have been 'actor' roles each year in the hitsory of film.



In [32]:

    
(c.actor/ (c.actor + c.actress)).plot(ylim=[0,1])









    Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8cc07f6940>



In [ ]:

Plot the fraction of supporting (n=2) roles that have been 'actor' roles each year in the history of film.



In [40]:

    
c = cast[(cast["n"] == 2) ]
c = c.groupby(['year','type']).size()
c = c.unstack('type')
(c.actor/ (c.actor + c.actress)).plot(ylim=[0,1])









    Out[40]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8cc06262b0>



In [ ]:

Build a plot with a line for each rank n=1 through n=3, where the line shows what fraction of that rank's roles were 'actor' roles for each year in the history of film.



In [62]:

    
c = cast
c = c[c.n <= 3]
c = c.groupby(['year', 'type', 'n']).size()
c = c.unstack('type')
r = c.actor / (c.actor + c.actress)
r = r.unstack('n')
r.plot(ylim=[0,1])









    Out[62]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8cc0355ef0>



In [ ]:

	title	year
0	Ligaw na daigdig	1962
1	Sluby ulanskie	1934
2	The House of the Seven Gables	1940
3	Mandala - Il simbolo	2008
4	Shi bian	1958

	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