import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

展示表格基本信息

# 读取movie，设定行索引是movie_title
pd.options.display.max_columns = 50
movie = pd.read_csv('data/movie.csv', index_col='movie_title')
movie.head()
  color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres actor_1_name num_voted_users cast_total_facebook_likes actor_3_name facenumber_in_poster plot_keywords movie_imdb_link num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
movie_title                                                      
Avatar Color James Cameron 723.0 178.0 0.0 855.0 Joel David Moore 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi CCH Pounder 886204 4834 Wes Studi 0.0 avatar|future|marine|native|paraplegic http://www.imdb.com/title/tt0499549/?ref_=fn_t... 3054.0 English USA PG-13 237000000.0 2009.0 936.0 7.9 1.78 33000
Pirates of the Caribbean: At World's End Color Gore Verbinski 302.0 169.0 563.0 1000.0 Orlando Bloom 40000.0 309404152.0 Action|Adventure|Fantasy Johnny Depp 471220 48350 Jack Davenport 0.0 goddess|marriage ceremony|marriage proposal|pi... http://www.imdb.com/title/tt0449088/?ref_=fn_t... 1238.0 English USA PG-13 300000000.0 2007.0 5000.0 7.1 2.35 0
Spectre Color Sam Mendes 602.0 148.0 0.0 161.0 Rory Kinnear 11000.0 200074175.0 Action|Adventure|Thriller Christoph Waltz 275868 11700 Stephanie Sigman 1.0 bomb|espionage|sequel|spy|terrorist http://www.imdb.com/title/tt2379713/?ref_=fn_t... 994.0 English UK PG-13 245000000.0 2015.0 393.0 6.8 2.35 85000
The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller Tom Hardy 1144337 106759 Joseph Gordon-Levitt 0.0 deception|imprisonment|lawlessness|police offi... http://www.imdb.com/title/tt1345836/?ref_=fn_t... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
Star Wars: Episode VII - The Force Awakens NaN Doug Walker NaN NaN 131.0 NaN Rob Walker 131.0 NaN Documentary Doug Walker 8 143 NaN 0.0 NaN http://www.imdb.com/title/tt5289954/?ref_=fn_t... NaN NaN NaN NaN NaN NaN 12.0 7.1 NaN 0

一、计算布尔值统计信息

# 判断电影时长是否超过两小时
movie_2_hours = movie['duration'] > 120
movie_2_hours.head(10)
'''
movie_title
Avatar                                         True
Pirates of the Caribbean: At World's End       True
Spectre                                        True
The Dark Knight Rises                          True
Star Wars: Episode VII - The Force Awakens    False
John Carter                                    True
Spider-Man 3                                   True
Tangled                                       False
Avengers: Age of Ultron                        True
Harry Potter and the Half-Blood Prince         True
Name: duration, dtype: bool
'''
# 有多少时长超过两小时的电影
movie_2_hours.sum()
#1039
# 超过两小时的电影的比例
movie_2_hours.mean()
#0.2113506916192026# 用describe()输出一些该布尔Series信息
movie_2_hours.describe()
# count：总个数
# unique：表示有多少种不同的值
# top：数据中出现次数最高的值
# freq：出现次数最高的那个值（top）的出现频率count      4916
unique        2
top       False
freq       3877
Name: duration, dtype: object# 实际上，dureation这列是有缺失值的，要想获得真正的超过两小时的电影的比例，需要先删掉缺失值
movie['duration'].dropna().gt(120).mean() #gt(120)是大于120分钟是 greater than
#107.0907977963681原理
# 统计False和True值的比例
movie_2_hours.value_counts(normalize=True)
'''
False    0.788649
True     0.211351
Name: duration, dtype: float64
'''# 比较同一个DataFrame中的两列
actors = movie[['actor_1_facebook_likes', 'actor_2_facebook_likes']].dropna()
(actors['actor_1_facebook_likes'] > actors['actor_2_facebook_likes']).mean()
#0.9777687130328371

二、构建多个布尔条件

# 创建多个布尔条件
criteria1 = movie.imdb_score > 8
criteria2 = movie.content_rating == 'PG-13'
criteria3 = (movie.title_year < 2000) | (movie.title_year >= 2010)
criteria2.head()
'''
movie_title
Avatar                                         True
Pirates of the Caribbean: At World's End       True
Spectre                                        True
The Dark Knight Rises                          True
Star Wars: Episode VII - The Force Awakens    False
Name: content_rating, dtype: bool
'''# 将这些布尔条件合并成一个
criteria_final = criteria1 & criteria2 & criteria3
criteria_final.head()movie_title
Avatar                                        False
Pirates of the Caribbean: At World's End      False
Spectre                                       False
The Dark Knight Rises                          True
Star Wars: Episode VII - The Force Awakens    False
dtype: bool#在Pandas中，位运算符（&, |, ~）的优先级高于比较运算符，因此前面的运算需要添加括号
(movie.title_year < 2000)| movie.title_year > 2009movie_title
Avatar                                        False
Pirates of the Caribbean: At World's End      False
Spectre                                       False
The Dark Knight Rises                         False
Star Wars: Episode VII - The Force Awakens    False...
Signed Sealed Delivered                       False
The Following                                 False
A Plague So Pleasant                          False
Shanghai Calling                              False
My Date with Drew                             False
Name: title_year, Length: 4916, dtype: bool

三、用布尔索引过滤

# 读取movie数据集，创建布尔条件
movie = pd.read_csv('data/movie.csv', index_col='movie_title')
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# 创建第二个布尔条件
crit_b1 = movie.imdb_score < 5
crit_b2 = movie.content_rating == 'R'
crit_b3 = (movie.title_year >= 2000) & (movie.title_year <= 2010)
final_crit_b = crit_b1 & crit_b2 & crit_b3# 将这两个条件用或运算合并起来
final_crit_all = final_crit_a | final_crit_b
final_crit_all.head()
'''
movie_title
Avatar                                        False
Pirates of the Caribbean: At World's End      False
Spectre                                       False
The Dark Knight Rises                          True
Star Wars: Episode VII - The Force Awakens    False
dtype: bool
'''
# 用最终的布尔条件过滤数据
movie[final_crit_all].head()
  color director_name num_critic_for_reviews duration director_facebook_likes actor_3_facebook_likes actor_2_name actor_1_facebook_likes gross genres actor_1_name num_voted_users cast_total_facebook_likes actor_3_name facenumber_in_poster plot_keywords movie_imdb_link num_user_for_reviews language country content_rating budget title_year actor_2_facebook_likes imdb_score aspect_ratio movie_facebook_likes
movie_title                                                      
The Dark Knight Rises Color Christopher Nolan 813.0 164.0 22000.0 23000.0 Christian Bale 27000.0 448130642.0 Action|Thriller Tom Hardy 1144337 106759 Joseph Gordon-Levitt 0.0 deception|imprisonment|lawlessness|police offi... http://www.imdb.com/title/tt1345836/?ref_=fn_t... 2701.0 English USA PG-13 250000000.0 2012.0 23000.0 8.5 2.35 164000
The Avengers Color Joss Whedon 703.0 173.0 0.0 19000.0 Robert Downey Jr. 26000.0 623279547.0 Action|Adventure|Sci-Fi Chris Hemsworth 995415 87697 Scarlett Johansson 3.0 alien invasion|assassin|battle|iron man|soldier http://www.imdb.com/title/tt0848228/?ref_=fn_t... 1722.0 English USA PG-13 220000000.0 2012.0 21000.0 8.1 1.85 123000
Captain America: Civil War Color Anthony Russo 516.0 147.0 94.0 11000.0 Scarlett Johansson 21000.0 407197282.0 Action|Adventure|Sci-Fi Robert Downey Jr. 272670 64798 Chris Evans 0.0 based on comic book|knife|marvel cinematic uni... http://www.imdb.com/title/tt3498820/?ref_=fn_t... 1022.0 English USA PG-13 250000000.0 2016.0 19000.0 8.2 2.35 72000
Guardians of the Galaxy Color James Gunn 653.0 121.0 571.0 3000.0 Vin Diesel 14000.0 333130696.0 Action|Adventure|Sci-Fi Bradley Cooper 682155 32438 Djimon Hounsou 3.0 bounty hunter|outer space|raccoon|talking anim... http://www.imdb.com/title/tt2015381/?ref_=fn_t... 1097.0 English USA PG-13 170000000.0 2014.0 14000.0 8.1 2.35 96000
Interstellar Color Christopher Nolan 712.0 169.0 22000.0 6000.0 Anne Hathaway 11000.0 187991439.0 Adventure|Drama|Sci-Fi Matthew McConaughey 928227 31488 Mackenzie Foy 1.0 black hole|father daughter relationship|saving... http://www.imdb.com/title/tt0816692/?ref_=fn_t... 2725.0 English USA PG-13 165000000.0 2014.0 11000.0 8.6 2.35 349000 
# 使用loc，对指定的列做过滤操作，可以清楚地看到过滤是否起作用
cols = ['imdb_score', 'content_rating', 'title_year']
movie_filtered = movie.loc[final_crit_all, cols]
movie_filtered.head(10)
  imdb_score content_rating title_year
movie_title      
The Dark Knight Rises 8.5 PG-13 2012.0
The Avengers 8.1 PG-13 2012.0
Captain America: Civil War 8.2 PG-13 2016.0
Guardians of the Galaxy 8.1 PG-13 2014.0
Interstellar 8.6 PG-13 2014.0
Inception 8.8 PG-13 2010.0
The Martian 8.1 PG-13 2015.0
Town & Country 4.4 R 2001.0
Sex and the City 2 4.3 R 2010.0
Rollerball 3.0 R 2002.0 
# 用一个长布尔表达式代替前面由短表达式生成的布尔条件((movie.title_year < 2000) | (movie.title_year > 2009))
final_crit_a2 = (movie.imdb_score > 8) & \(movie.content_rating == 'PG-13') & \((movie.title_year < 2000) | (movie.title_year > 2009))
final_crit_a2.equals(final_crit_a)
#True

四、用标签索引代替布尔索引

# 用布尔索引选取所有得克萨斯州的学校
college = pd.read_csv('data/college.csv')
college[college['STABBR'] == 'TX'].head()
  INSTNM CITY STABBR HBCU MENONLY WOMENONLY RELAFFIL SATVRMID SATMTMID DISTANCEONLY UGDS UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN PPTUG_EF CURROPER PCTPELL PCTFLOAN UG25ABV MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
3610 Abilene Christian University Abilene TX 0.0 0.0 0.0 1 530.0 545.0 0.0 3572.0 0.6739 0.0798 0.1414 0.0090 0.0039 0.0000 0.0454 0.0423 0.0045 0.0468 1 0.2595 0.5527 0.0381 40200 25985
3611 Alvin Community College Alvin TX 0.0 0.0 0.0 0 NaN NaN 0.0 4682.0 0.5126 0.1034 0.3093 0.0500 0.0064 0.0038 0.0002 0.0000 0.0143 0.7123 1 0.1549 0.0625 0.2841 34500 6750
3612 Amarillo College Amarillo TX 0.0 0.0 0.0 0 NaN NaN 0.0 9346.0 0.5104 0.0507 0.3888 0.0293 0.0122 0.0000 0.0000 0.0001 0.0085 0.6922 1 0.3786 0.1573 0.3431 31700 10950
3613 Angelina College Lufkin TX 0.0 0.0 0.0 0 NaN NaN 0.0 3825.0 0.5854 0.1508 0.2207 0.0076 0.0073 0.0013 0.0264 0.0005 0.0000 0.5600 1 0.5308 0.0000 0.2603 26900 PrivacySuppressed
3614 Angelo State University San Angelo TX 0.0 0.0 0.0 0 475.0 490.0 0.0 5290.0 0.5225 0.0841 0.3166 0.0087 0.0036 0.0017 0.0285 0.0331 0.0011 0.1289 1 0.4068 0.5279 0.1407 37700 21319.5 
# 用STABBR作为行索引，然后用loc选取
college2 = college.set_index('STABBR')
college2.loc['TX'].head()
  INSTNM CITY HBCU MENONLY WOMENONLY RELAFFIL SATVRMID SATMTMID DISTANCEONLY UGDS UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN PPTUG_EF CURROPER PCTPELL PCTFLOAN UG25ABV MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
STABBR                                                    
TX Abilene Christian University Abilene 0.0 0.0 0.0 1 530.0 545.0 0.0 3572.0 0.6739 0.0798 0.1414 0.0090 0.0039 0.0000 0.0454 0.0423 0.0045 0.0468 1 0.2595 0.5527 0.0381 40200 25985
TX Alvin Community College Alvin 0.0 0.0 0.0 0 NaN NaN 0.0 4682.0 0.5126 0.1034 0.3093 0.0500 0.0064 0.0038 0.0002 0.0000 0.0143 0.7123 1 0.1549 0.0625 0.2841 34500 6750
TX Amarillo College Amarillo 0.0 0.0 0.0 0 NaN NaN 0.0 9346.0 0.5104 0.0507 0.3888 0.0293 0.0122 0.0000 0.0000 0.0001 0.0085 0.6922 1 0.3786 0.1573 0.3431 31700 10950
TX Angelina College Lufkin 0.0 0.0 0.0 0 NaN NaN 0.0 3825.0 0.5854 0.1508 0.2207 0.0076 0.0073 0.0013 0.0264 0.0005 0.0000 0.5600 1 0.5308 0.0000 0.2603 26900 PrivacySuppressed
TX Angelo State University San Angelo 0.0 0.0 0.0 0 475.0 490.0 0.0 5290.0 0.5225 0.0841 0.3166 0.0087 0.0036 0.0017 0.0285 0.0331 0.0011 0.1289 1 0.4068 0.5279 0.1407 37700 21319. 
# 比较二者的速度(使用loc比直接取值更节省时间)
%timeit college[college['STABBR'] == 'TX']
#4.33 ms ± 743 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)%timeit college2.loc['TX']
#2.05 ms ± 327 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)# 使用STABBR作为行索引所用的时间
%timeit college2 = college.set_index('STABBR')
#6.83 ms ± 1.35 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

使用布尔索引和标签选取多列isin()

# 使用布尔索引和标签选取多列
states =['TX', 'CA', 'NY']
college[college['STABBR'].isin(states)]
college2.loc[states].head()
  INSTNM CITY HBCU MENONLY WOMENONLY RELAFFIL SATVRMID SATMTMID DISTANCEONLY UGDS UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN PPTUG_EF CURROPER PCTPELL PCTFLOAN UG25ABV MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
STABBR                                                    
TX Abilene Christian University Abilene 0.0 0.0 0.0 1 530.0 545.0 0.0 3572.0 0.6739 0.0798 0.1414 0.0090 0.0039 0.0000 0.0454 0.0423 0.0045 0.0468 1 0.2595 0.5527 0.0381 40200 25985
TX Alvin Community College Alvin 0.0 0.0 0.0 0 NaN NaN 0.0 4682.0 0.5126 0.1034 0.3093 0.0500 0.0064 0.0038 0.0002 0.0000 0.0143 0.7123 1 0.1549 0.0625 0.2841 34500 6750
TX Amarillo College Amarillo 0.0 0.0 0.0 0 NaN NaN 0.0 9346.0 0.5104 0.0507 0.3888 0.0293 0.0122 0.0000 0.0000 0.0001 0.0085 0.6922 1 0.3786 0.1573 0.3431 31700 10950
TX Angelina College Lufkin 0.0 0.0 0.0 0 NaN NaN 0.0 3825.0 0.5854 0.1508 0.2207 0.0076 0.0073 0.0013 0.0264 0.0005 0.0000 0.5600 1 0.5308 0.0000 0.2603 26900 PrivacySuppressed
TX Angelo State University San Angelo 0.0 0.0 0.0 0 475.0 490.0 0.0 5290.0 0.5225 0.0841 0.3166 0.0087 0.0036 0.0017 0.0285 0.0331 0.0011 0.1289 1 0.4068 0.5279 0.1407 37700 21319

五. 用唯一和有序索引选取

# 读取college数据集，使用STABBR作为行索引，检查行索引是否有序
college = pd.read_csv('data/college.csv')
college2 = college.set_index('STABBR')college2.index.is_monotonic #检验一个数组是否是单调的,先确定一个方向，然后遍历这个数组，看看是否破坏之前的方向。破坏就是不单调
#False# 将college2排序，存储成另一个对象，查看其是否有序
college3 = college2.sort_index()
college3.index.is_monotonic
#True# 从这三个DataFrame选取得克萨斯州，比较速度
%timeit college[college['STABBR'] == 'TX']
#4.32 ms ± 1.11 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)%timeit college2.loc['TX']
#2.63 ms ± 395 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)%timeit college3.loc['TX']
#942 µs ± 235 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)# 使用INSTNM作为行索引，检测行索引是否唯一
college_unique = college.set_index('INSTNM')
college_unique.index.is_unique
#True# 用布尔索引选取斯坦福大学
college[college['INSTNM'] == 'Stanford University']# 用行索引标签选取斯坦福大学
college_unique.loc['Stanford University']# 比较两种方法的速度(直接索引慢一点)
%timeit college[college['INSTNM'] == 'Stanford University']
#3.89 ms ± 745 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit college_unique.loc['Stanford University']
#597 µs ± 87.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)# 使用CITY和STABBR两列作为行索引，并进行排序
college.index = college['CITY'] + ', ' + college['STABBR']
college = college.sort_index()
college.head()
  INSTNM CITY STABBR HBCU MENONLY WOMENONLY RELAFFIL SATVRMID SATMTMID DISTANCEONLY UGDS UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN PPTUG_EF CURROPER PCTPELL PCTFLOAN UG25ABV MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
ARTESIA, CA Angeles Institute ARTESIA CA 0.0 0.0 0.0 0 NaN NaN 0.0 114.0 0.0175 0.2193 0.3860 0.3158 0.0000 0.0263 0.0175 0.0088 0.0088 0.0000 1 0.6275 0.8138 0.5429 NaN 16850
Aberdeen, SD Presentation College Aberdeen SD 0.0 0.0 0.0 1 440.0 480.0 0.0 705.0 0.6525 0.1163 0.0780 0.0128 0.0156 0.0000 0.0284 0.0142 0.0823 0.2865 1 0.4829 0.7560 0.3097 35900 25000
Aberdeen, SD Northern State University Aberdeen SD 0.0 0.0 0.0 0 480.0 475.0 0.0 1693.0 0.8435 0.0230 0.0319 0.0112 0.0207 0.0030 0.0219 0.0425 0.0024 0.1872 1 0.2272 0.4303 0.1766 33600 24847
Aberdeen, WA Grays Harbor College Aberdeen WA 0.0 0.0 0.0 0 NaN NaN 0.0 1121.0 0.7110 0.0169 0.0946 0.0214 0.0312 0.0054 0.0937 0.0009 0.0250 0.1820 1 0.4530 0.1502 0.5087 27000 11490
Abilene, TX Hardin-Simmons University Abilene TX 0.0 0.0 0.0 1 508.0 515.0 0.0 1576.0 0.7126 0.0742 0.1472 0.0076 0.0019 0.0006 0.0298 0.0159 0.0102 0.0685 1 0.3256 0.5547 0.0982 38700 25864 
# 选取所有Miami, FL的大学
college.loc['Miami, FL'].head()
  INSTNM CITY STABBR HBCU MENONLY WOMENONLY RELAFFIL SATVRMID SATMTMID DISTANCEONLY UGDS UGDS_WHITE UGDS_BLACK UGDS_HISP UGDS_ASIAN UGDS_AIAN UGDS_NHPI UGDS_2MOR UGDS_NRA UGDS_UNKN PPTUG_EF CURROPER PCTPELL PCTFLOAN UG25ABV MD_EARN_WNE_P10 GRAD_DEBT_MDN_SUPP
Miami, FL New Professions Technical Institute Miami FL 0.0 0.0 0.0 0 NaN NaN 0.0 56.0 0.0179 0.0714 0.9107 0.0000 0.0 0.0000 0.0000 0.0 0.0 0.4464 1 0.8701 0.6780 0.8358 18700 8682
Miami, FL Management Resources College Miami FL 0.0 0.0 0.0 0 NaN NaN 0.0 708.0 0.0071 0.0523 0.9407 0.0000 0.0 0.0000 0.0000 0.0 0.0 0.0000 1 0.4239 0.5458 0.8698 PrivacySuppressed 12182
Miami, FL Strayer University-Doral Miami FL NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 NaN NaN NaN 49200 36173.5
Miami, FL Keiser University- Miami Miami FL NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 NaN NaN NaN 29700 26063
Miami, FL George T Baker Aviation Technical College Miami FL 0.0 0.0 0.0 0 NaN NaN 0.0 649.0 0.0894 0.1263 0.7735 0.0046 0.0 0.0015 0.0046 0.0 0.0 0.5686 1 0.2567 0.0000 0.4366 38600 PrivacySuppressed 
# 速度比较
%timeit crit1 = college['CITY'] == 'Miami'
crit2 = college['STABBR'] == 'FL'
college[crit1 & crit2]
#2.3 ms ± 826 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)%timeit college.loc['Miami, FL']
#1.01 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)# 判断这两个条件是否相同
college[(college['CITY'] == 'Miami') & (college['STABBR'] == 'FL')].equals(college.loc['Miami, FL'])
#True

loc索引消耗的时间比直接取值用时少。

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