In [2]:

    

import pandas as pd


    

    




RangeIndex(start=0, stop=146, step=1)

In [10]:

    

#will return a new DataFrame that is indexed by the values in the specified column 
#and will drop that column from the DataFrame
#without the FILM column dropped 
fandango = pd.read_csv('fandango_score_comparison.csv')
print (type(fandango))
fandango_films = fandango.set_index('FILM', drop=False)
#print(fandango_films.index)


    

    




<class 'pandas.core.frame.DataFrame'>

In [4]:

    

# Slice using either bracket notation or loc[]
fandango_films["Avengers: Age of Ultron (2015)":"Hot Tub Time Machine 2 (2015)"]
fandango_films.loc["Avengers: Age of Ultron (2015)":"Hot Tub Time Machine 2 (2015)"]

# Specific movie
fandango_films.loc['Kumiko, The Treasure Hunter (2015)']

# Selecting list of movies
movies = ['Kumiko, The Treasure Hunter (2015)', 'Do You Believe? (2015)', 'Ant-Man (2015)']
fandango_films.loc[movies]

#When selecting multiple rows, a DataFrame is returned, 
#but when selecting an individual row, a Series object is returned instead


    

    
Out[4]:






  

    

      

      
FILM
      
RottenTomatoes
      
RottenTomatoes_User
      
Metacritic
      
Metacritic_User
      
IMDB
      
Fandango_Stars
      
Fandango_Ratingvalue
      
RT_norm
      
RT_user_norm
      
...
      
IMDB_norm
      
RT_norm_round
      
RT_user_norm_round
      
Metacritic_norm_round
      
Metacritic_user_norm_round
      
IMDB_norm_round
      
Metacritic_user_vote_count
      
IMDB_user_vote_count
      
Fandango_votes
      
Fandango_Difference
    
    

      
FILM
      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
Kumiko, The Treasure Hunter (2015)
      
Kumiko, The Treasure Hunter (2015)
      
87
      
63
      
68
      
6.4
      
6.7
      
3.5
      
3.5
      
4.35
      
3.15
      
...
      
3.35
      
4.5
      
3.0
      
3.5
      
3.0
      
3.5
      
19
      
5289
      
41
      
0.0
    
    

      
Do You Believe? (2015)
      
Do You Believe? (2015)
      
18
      
84
      
22
      
4.7
      
5.4
      
5.0
      
4.5
      
0.90
      
4.20
      
...
      
2.70
      
1.0
      
4.0
      
1.0
      
2.5
      
2.5
      
31
      
3136
      
1793
      
0.5
    
    

      
Ant-Man (2015)
      
Ant-Man (2015)
      
80
      
90
      
64
      
8.1
      
7.8
      
5.0
      
4.5
      
4.00
      
4.50
      
...
      
3.90
      
4.0
      
4.5
      
3.0
      
4.0
      
4.0
      
627
      
103660
      
12055
      
0.5
    
  

3 rows × 22 columns

In [15]:

    

#The apply() method in Pandas allows us to specify Python logic
#The apply() method requires you to pass in a vectorized operation 
#that can be applied over each Series object.
import numpy as np

# returns the data types as a Series
types = fandango_films.dtypes
#print (types)
# filter data types to just floats, index attributes returns just column names
float_columns = types[types.values == 'float64'].index
# use bracket notation to filter columns to just float columns
float_df = fandango_films[float_columns]
#print float_df
# `x` is a Series object representing a column
deviations = float_df.apply(lambda x: np.std(x))

print(deviations)


    

    




Metacritic_User               1.505529
IMDB                          0.955447
Fandango_Stars                0.538532
Fandango_Ratingvalue          0.501106
RT_norm                       1.503265
RT_user_norm                  0.997787
Metacritic_norm               0.972522
Metacritic_user_nom           0.752765
IMDB_norm                     0.477723
RT_norm_round                 1.509404
RT_user_norm_round            1.003559
Metacritic_norm_round         0.987561
Metacritic_user_norm_round    0.785412
IMDB_norm_round               0.501043
Fandango_Difference           0.152141
dtype: float64

In [16]:

    

rt_mt_user = float_df[['RT_user_norm', 'Metacritic_user_nom']]
rt_mt_user.apply(lambda x: np.std(x), axis=1)


    

    
Out[16]:





FILM
Avengers: Age of Ultron (2015)                    0.375
Cinderella (2015)                                 0.125
Ant-Man (2015)                                    0.225
Do You Believe? (2015)                            0.925
Hot Tub Time Machine 2 (2015)                     0.150
The Water Diviner (2015)                          0.150
Irrational Man (2015)                             0.575
Top Five (2014)                                   0.100
Shaun the Sheep Movie (2015)                      0.150
Love & Mercy (2015)                               0.050
Far From The Madding Crowd (2015)                 0.050
Black Sea (2015)                                  0.150
Leviathan (2014)                                  0.175
Unbroken (2014)                                   0.125
The Imitation Game (2014)                         0.250
Taken 3 (2015)                                    0.000
Ted 2 (2015)                                      0.175
Southpaw (2015)                                   0.050
Night at the Museum: Secret of the Tomb (2014)    0.000
Pixels (2015)                                     0.025
McFarland, USA (2015)                             0.425
Insidious: Chapter 3 (2015)                       0.325
The Man From U.N.C.L.E. (2015)                    0.025
Run All Night (2015)                              0.350
Trainwreck (2015)                                 0.350
Selma (2014)                                      0.375
Ex Machina (2015)                                 0.175
Still Alice (2015)                                0.175
Wild Tales (2014)                                 0.100
The End of the Tour (2015)                        0.350
                                                  ...  
Clouds of Sils Maria (2015)                       0.100
Testament of Youth (2015)                         0.000
Infinitely Polar Bear (2015)                      0.075
Phoenix (2015)                                    0.025
The Wolfpack (2015)                               0.075
The Stanford Prison Experiment (2015)             0.050
Tangerine (2015)                                  0.325
Magic Mike XXL (2015)                             0.250
Home (2015)                                       0.200
The Wedding Ringer (2015)                         0.825
Woman in Gold (2015)                              0.225
The Last Five Years (2015)                        0.225
Mission: Impossible – Rogue Nation (2015)       0.250
Amy (2015)                                        0.075
Jurassic World (2015)                             0.275
Minions (2015)                                    0.125
Max (2015)                                        0.350
Paul Blart: Mall Cop 2 (2015)                     0.300
The Longest Ride (2015)                           0.625
The Lazarus Effect (2015)                         0.650
The Woman In Black 2 Angel of Death (2015)        0.475
Danny Collins (2015)                              0.100
Spare Parts (2015)                                0.300
Serena (2015)                                     0.700
Inside Out (2015)                                 0.025
Mr. Holmes (2015)                                 0.025
'71 (2015)                                        0.175
Two Days, One Night (2014)                        0.250
Gett: The Trial of Viviane Amsalem (2015)         0.200
Kumiko, The Treasure Hunter (2015)                0.025
dtype: float64

In [ ]: