import pandas as pd
# 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 droppedfandango = pd.read_csv('fandango_score_comparison.csv')print(type(fandango))fandango_films = fandango.set_index('FILM',drop=False)print(fandango_films.index) Index(['Avengers: Age of Ultron (2015)', 'Cinderella (2015)', 'Ant-Man (2015)', 'Do You Believe? (2015)', 'Hot Tub Time Machine 2 (2015)', 'The Water Diviner (2015)', 'Irrational Man (2015)', 'Top Five (2014)', 'Shaun the Sheep Movie (2015)', 'Love & Mercy (2015)', ... 'The Woman In Black 2 Angel of Death (2015)', 'Danny Collins (2015)', 'Spare Parts (2015)', 'Serena (2015)', 'Inside Out (2015)', 'Mr. Holmes (2015)', ''71 (2015)', 'Two Days, One Night (2014)', 'Gett: The Trial of Viviane Amsalem (2015)', 'Kumiko, The Treasure Hunter (2015)'], dtype='object', name='FILM', length=146)
# 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 moviefandango_films.loc["Kumiko, The Treasure Hunter (2015)"]# Selecting list of moviesmovies = ['Kumiko, The Treasure Hunter (2015)', 'Do You Believe? (2015)', 'Ant-Man (2015)']print(fandango_films.loc[movies])#When selecting multiple rows, a DataFrame is returned, #but when selecting an individual row, a Series object is returned instead
FILM \FILM Kumiko, The Treasure Hunter (2015) Kumiko, The Treasure Hunter (2015) Do You Believe? (2015) Do You Believe? (2015) Ant-Man (2015) Ant-Man (2015) RottenTomatoes RottenTomatoes_User \FILM Kumiko, The Treasure Hunter (2015) 87 63 Do You Believe? (2015) 18 84 Ant-Man (2015) 80 90 Metacritic Metacritic_User IMDB \FILM Kumiko, The Treasure Hunter (2015) 68 6.4 6.7 Do You Believe? (2015) 22 4.7 5.4 Ant-Man (2015) 64 8.1 7.8 Fandango_Stars Fandango_Ratingvalue \FILM Kumiko, The Treasure Hunter (2015) 3.5 3.5 Do You Believe? (2015) 5.0 4.5 Ant-Man (2015) 5.0 4.5 RT_norm RT_user_norm ... IMDB_norm \FILM ... Kumiko, The Treasure Hunter (2015) 4.35 3.15 ... 3.35 Do You Believe? (2015) 0.90 4.20 ... 2.70 Ant-Man (2015) 4.00 4.50 ... 3.90 RT_norm_round RT_user_norm_round \FILM Kumiko, The Treasure Hunter (2015) 4.5 3.0 Do You Believe? (2015) 1.0 4.0 Ant-Man (2015) 4.0 4.5 Metacritic_norm_round \FILM Kumiko, The Treasure Hunter (2015) 3.5 Do You Believe? (2015) 1.0 Ant-Man (2015) 3.0 Metacritic_user_norm_round \FILM Kumiko, The Treasure Hunter (2015) 3.0 Do You Believe? (2015) 2.5 Ant-Man (2015) 4.0 IMDB_norm_round \FILM Kumiko, The Treasure Hunter (2015) 3.5 Do You Believe? (2015) 2.5 Ant-Man (2015) 4.0 Metacritic_user_vote_count \FILM Kumiko, The Treasure Hunter (2015) 19 Do You Believe? (2015) 31 Ant-Man (2015) 627 IMDB_user_vote_count Fandango_votes \FILM Kumiko, The Treasure Hunter (2015) 5289 41 Do You Believe? (2015) 3136 1793 Ant-Man (2015) 103660 12055 Fandango_Difference FILM Kumiko, The Treasure Hunter (2015) 0.0 Do You Believe? (2015) 0.5 Ant-Man (2015) 0.5 [3 rows x 22 columns]
# 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 objectimport numpy as np# return the data types as a seriestypes = fandango_films.dtypes# print(types)# filter data types to just floats, index attributes returns just column namesfloat_columns = types[types.values=='float64'].index# use bracket notation to filter columns to just float columnsfloat_df = fandango_films[float_columns]# print(float_df)# 'x' is a Series object representing a columndeviations = float_df.apply(lambda x: np.std(x))print(deviations)
Metacritic_User 1.505529IMDB 0.955447Fandango_Stars 0.538532Fandango_Ratingvalue 0.501106RT_norm 1.503265RT_user_norm 0.997787Metacritic_norm 0.972522Metacritic_user_nom 0.752765IMDB_norm 0.477723RT_norm_round 1.509404RT_user_norm_round 1.003559Metacritic_norm_round 0.987561Metacritic_user_norm_round 0.785412IMDB_norm_round 0.501043Fandango_Difference 0.152141dtype: float64