How to use Python's Pandas Boolean index
This article mainly explains "how to use Python's Pandas Boolean index". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to use Python's Pandas Boolean index".
1. Calculate Boolean statistics
Import pandas as pd import numpy as np import matplotlib.pyplot as plt # reads movie and sets the row index as movie_title pd.options.display.max_columns = 50 movie = pd.read_csv (". / data/movie.csv" Index_col = 'movie_title') # determine whether the duration of a movie exceeds two hours # Figure1movie_2_hours = movie [' duration'] > 12 the total number of movies whose duration exceeds two hours print (movie_2_hours.sum ()) # result:1039# counts the proportion of movies with a duration of more than two hours print (movie_2_hours.mean ()) # Statistics the ratio of False to True (movie_2_hours.value_) Counts (normalize = True) # compare two columns of actors = movie [['actor_1_facebook_likes'] in the same DataFrame 'actor_2_facebook_likes']] .dropna () print ((actors [' actor_1_facebook_likes'] > actors ['actor_2_facebook_likes']) .mean ()) # Figure2
Running result:
Figure1
Figure2
two。 Build multiple Boolean conditions
Import pandas as pd import numpy as np import matplotlib.pyplot as plt # reads movie and sets the row index as movie_title pd.options.display.max_columns = 50 movie = pd.read_csv (". / data/movie.csv", index_col = 'movie_title') # create multiple Boolean conditions criteria1 = movie.imdb_score > 8criteria2 = movie.content_rating = = "PG-13" criteria3 = (movie.title_year
< 2000) | (movie.title_year >= 2010) "" print (criteria1.head ()) print (criteria2.head ()) print (criteria3.head ()) run result: Figure1 "# merge multiple Boolean conditions into one criteria_final = criteria1 & criteria2 & criteria3 print (criteria_final.head ()) # run result: Figure2
Running result:
Figure1
Figure2
3. Filter with Boolean index
Import pandas as pd import numpy as np import matplotlib.pyplot as plt # reads movie and sets the row index as movie_title pd.options.display.max_columns = 50 movie = pd.read_csv (". / data/movie.csv", index_col = 'movie_title') # create the first Boolean condition crit_a1 = movie.imdb_score > 8 crit_a2 = movie.content_rating = =' PG-13'crit_a3 = (movie.title_year)
< 2000) | (movie.title_year >2009) final_crit_a = crit_a1 & crit_a2 & crit_a3# creates the second Boolean condition crit_b1 = movie.imdb_score
< 5crit_b2 = movie.content_rating == 'R'crit_b3 = (movie.title_year >= 2000) & (movie.title_year 8 crit_a2 = movie.content_rating = 'PG-13'crit_a3 = (movie.title_year)
< 2000) | (movie.title_year >2009) final_crit_a = crit_a1 & crit_a2 & crit_a3# creates the second Boolean condition crit_b1 = movie.imdb_score
< 5crit_b2 = movie.content_rating == 'R'crit_b3 = (movie.title_year >= 2000) & (movie.title_year
