In [76]:

    

import pandas as pd
import matplotlib.pyplot as plt
import dateutil.parser
import numpy as np

%matplotlib inline


    

In [11]:

    

df = pd.read_csv('2013_movies.csv')


    

In [12]:

    

df.head()


    

    
Out[12]:






  

    

      

      
Title
      
Budget
      
DomesticTotalGross
      
Director
      
Rating
      
Runtime
      
ReleaseDate
    
  
  

    

      
0
      
The Hunger Games: Catching Fire
      
130000000.0
      
424668047
      
Francis Lawrence
      
PG-13
      
146
      
2013-11-22 00:00:00
    
    

      
1
      
Iron Man 3
      
200000000.0
      
409013994
      
Shane Black
      
PG-13
      
129
      
2013-05-03 00:00:00
    
    

      
2
      
Frozen
      
150000000.0
      
400738009
      
Chris BuckJennifer Lee
      
PG
      
108
      
2013-11-22 00:00:00
    
    

      
3
      
Despicable Me 2
      
76000000.0
      
368061265
      
Pierre CoffinChris Renaud
      
PG
      
98
      
2013-07-03 00:00:00
    
    

      
4
      
Man of Steel
      
225000000.0
      
291045518
      
Zack Snyder
      
PG-13
      
143
      
2013-06-14 00:00:00
    
  

In [13]:

    

df['ReleaseDateParsed'] = df.ReleaseDate.apply(lambda x: dateutil.parser.parse(x))


    

In [14]:

    

zerotime = dateutil.parser.parse('2012-12-31')


    

In [15]:

    

df['Days'] = (df.ReleaseDateParsed - zerotime)
df['Days'] = df.Days.apply(lambda x: x.days)


    

In [68]:

    

plt.scatter(df.Days, df.DomesticTotalGross);


    

In [70]:

    

plt.scatter(df.Runtime, df.DomesticTotalGross);


    

In [16]:

    

(df.groupby('Rating').mean())[['Runtime','DomesticTotalGross']]


    

    
Out[16]:






  

    

      

      
Runtime
      
DomesticTotalGross
    
    

      
Rating
      

      

    
  
  

    

      
G
      
107.000000
      
2.684928e+08
    
    

      
PG
      
99.933333
      
1.311357e+08
    
    

      
PG-13
      
117.510638
      
1.114498e+08
    
    

      
R
      
110.729730
      
6.989243e+07
    
  

In [27]:

    

x = df[df.Rating == 'G']['Days']
y = df[df.Rating == 'G']['DomesticTotalGross']
plt.scatter(x,y);


    

In [23]:

    

x = df[df.Rating == 'PG']['Days']
y = df[df.Rating == 'PG']['DomesticTotalGross']
plt.scatter(x,y);


    

In [24]:

    

x = df[df.Rating == 'PG-13']['Days']
y = df[df.Rating == 'PG-13']['DomesticTotalGross']
plt.scatter(x,y);


    

In [25]:

    

x = df[df.Rating == 'R']['Days']
y = df[df.Rating == 'R']['DomesticTotalGross']
plt.scatter(x,y);


    

In [33]:

    

(df.groupby('Director').mean())[['DomesticTotalGross']].sort_values('DomesticTotalGross', ascending=False).head(1)


    

    
Out[33]:






  

    

      

      
DomesticTotalGross
    
    

      
Director
      

    
  
  

    

      
Francis Lawrence
      
424668047.0
    
  

In [38]:

    

df['Month'] = df['ReleaseDateParsed'].apply(lambda x: x.month)


    

In [78]:

    

# Months
i = [1,2,3,4,5,6,7,8,9,10,11,12]

# Mean
m = (df.groupby('Month').mean())

# Standard Deviation
s = (df.groupby('Month').std())

# Calculate Standard Error
n = (df.groupby('Month').count())
sem = s.DomesticTotalGross/np.sqrt(n.DomesticTotalGross)


    

In [83]:

    

plt.bar(i, m.DomesticTotalGross, color='green', yerr=sem);
plt.title('Mean Domestic Total Gross by Month in 2013');