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布尔索引

简书大神SeanCheney的译作，我作了些格式调整和文章目录结构的变化，更适合自己阅读，以后翻阅是更加方便自己查找吧

import pandas as pd
import numpy as np

设定最大列数和最大行数

pd.set_option(‘max_columns‘,5 , ‘max_rows‘, 5)

1 布尔值统计信息

movie = pd.read_csv(‘data/movie.csv‘, index_col=‘movie_title‘)

movie.head()

1.1 基础方法

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
                                            ... 
Avengers: Age of Ultron                     True
Harry Potter and the Half-Blood Prince      True
Name: duration, Length: 10, 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

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()

0.21199755152009794

1.2 统计信息

用describe()输出一些该布尔Series信息

movie_2_hours.describe()

count      4916
unique        2
top       False
freq       3877
Name: duration, dtype: object

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

2 布尔索引

2.1 布尔条件

在Pandas中，位运算符（&, |, ~）的优先级高于比较运算符

2.1.1 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)

criteria3.head()

movie_title
Avatar                                        False
Pirates of the Caribbean: At World‘s End      False
Spectre                                        True
The Dark Knight Rises                          True
Star Wars: Episode VII - The Force Awakens    False
Name: title_year, dtype: bool

2.1.2 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

2.2 布尔过滤

创建第一个布尔条件

 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

合并布尔条件

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

过滤数据

movie[final_crit_all].head()

验证过滤

cols = [‘imdb_score‘, ‘content_rating‘, ‘title_year‘]
movie_filtered = movie.loc[final_crit_all, cols]
movie_filtered.head(10)

2.3 与标签索引对比

college = pd.read_csv(‘data/college.csv‘)
college2 = college.set_index(‘STABBR‘)

2.3.1 单个标签

college2中STABBR作为行索引，用loc选取

college2.loc[‘TX‘].head()

college中，用布尔索引选取所有得克萨斯州的学校

college[college[‘STABBR‘] == ‘TX‘].head()

比较二者的速度

法一

%timeit college[college[‘STABBR‘] == ‘TX‘]

937 μs ± 58.9 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

法二

%timeit college2.loc[‘TX‘]

520 μs ± 21.2 μs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit college2 = college.set_index(‘STABBR‘)

2.11 ms ± 185 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)

2.3.2 多个标签

布尔索引和标签选取多列

states =[‘TX‘, ‘CA‘, ‘NY‘]

college[college[‘STABBR‘].isin(states)]

college2.loc[states].head()

3 查询方法

使用查询方法提高布尔索引的可读性

# 读取employee数据，确定选取的部门和列

employee = pd.read_csv(‘data/employee.csv‘)
depts = [‘Houston Police Department-HPD‘, ‘Houston Fire Department (HFD)‘]
select_columns = [‘UNIQUE_ID‘, ‘DEPARTMENT‘, ‘GENDER‘, ‘BASE_SALARY‘]

# 创建查询字符串，并执行query方法

qs = "DEPARTMENT in @depts and GENDER == ‘Female‘ and 80000 <= BASE_SALARY <= 120000"

emp_filtered = employee.query(qs)
emp_filtered[select_columns].head()

4 唯一和有序索引

4.1 单列索引

college = pd.read_csv(‘data/college.csv‘)
college2 = college.set_index(‘STABBR‘)

college2.index.is_monotonic

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

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

4.2 拼装索引

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()

college.index.is_unique

False

选取所有Miami, FL的大学

法一

college.loc[‘Miami, FL‘].head()

法二

crit1 = college[‘CITY‘] == ‘Miami‘ 
crit2 = college[‘STABBR‘] == ‘FL‘
college[crit1 & crit2]

5 loc/iloc中使用布尔

movie = pd.read_csv(‘data/movie.csv‘, index_col=‘movie_title‘)

5.1 行

c1 = movie[‘content_rating‘] == ‘G‘
c2 = movie[‘imdb_score‘] < 4
criteria = c1 & c2

bool_movie = movie[criteria]
bool_movie

loc使用bool

法一

movie_loc = movie.loc[criteria]

检查loc条件和布尔条件创建出来的两个DataFrame是否一样

movie_loc.equals(movie[criteria])

True

法二

movie_loc2 = movie.loc[criteria.values]

movie_loc2.equals(movie[criteria])

True

iloc使用bool

因为criteria是包含行索引的一个Series，必须要使用底层的ndarray，才能使用，iloc

movie_iloc = movie.iloc[criteria.values]

movie_iloc.equals(movie_loc)

True

5.2 列

布尔索引也可以用来选取列

criteria_col = movie.dtypes == np.int64
criteria_col.head()

color                      False
director_name              False
num_critic_for_reviews     False
duration                   False
director_facebook_likes    False
dtype: bool

movie.loc[:, criteria_col].head()

movie.iloc[:, criteria_col.values].head()

6 使用布尔值 - where/mask

mask() is the inverse boolean operation of where.

DataFrame.where(cond, other=nan, inplace=False **kwgs)
Parameters:

• cond : boolean NDFrame, array-like, or callable

  • Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn’t check it).
  • cond是一个与df通型的dataframe,当dataframe与cond对应的位置是true是，保留原值。否则便为other对应的值
• other : scalar, NDFrame, or callable
• inplace : boolean, default False
  • Whether to perform the operation in place on the data

6.1 Series使用where

movie = pd.read_csv(‘data/movie.csv‘, index_col=‘movie_title‘)
fb_likes = movie[‘actor_1_facebook_likes‘].dropna()
fb_likes.head()

movie_title
Avatar                                         1000.0
Pirates of the Caribbean: At World‘s End      40000.0
Spectre                                       11000.0
The Dark Knight Rises                         27000.0
Star Wars: Episode VII - The Force Awakens      131.0
Name: actor_1_facebook_likes, dtype: float64

使用describe获得对数据的认知

fb_likes.describe(percentiles=[.1, .25, .5, .75, .9]).astype(int)

count      4909
mean       6494
          ...  
90%       18000
max      640000
Name: actor_1_facebook_likes, Length: 10, dtype: int64

检测小于20000个喜欢的的比例

criteria_high = fb_likes < 20000

criteria_high.mean().round(2)

0.91

where条件可以返回一个同样大小的Series，但是所有False会被替换成缺失值

fb_likes.where(criteria_high).head()

movie_title
Avatar                                         1000.0
Pirates of the Caribbean: At World‘s End          NaN
Spectre                                       11000.0
The Dark Knight Rises                             NaN
Star Wars: Episode VII - The Force Awakens      131.0
Name: actor_1_facebook_likes, dtype: float64

第二个参数other，可以让你控制替换值

fb_likes.where(criteria_high, other=20000).head()

movie_title
Avatar                                         1000.0
Pirates of the Caribbean: At World‘s End      20000.0
Spectre                                       11000.0
The Dark Knight Rises                         20000.0
Star Wars: Episode VII - The Force Awakens      131.0
Name: actor_1_facebook_likes, dtype: float64

通过where条件，设定上下限的值

criteria_low = fb_likes > 300
fb_likes_cap = fb_likes.where(criteria_high, other=20000).where(criteria_low, 300)
fb_likes_cap.head()

movie_title
Avatar                                         1000.0
Pirates of the Caribbean: At World‘s End      20000.0
Spectre                                       11000.0
The Dark Knight Rises                         20000.0
Star Wars: Episode VII - The Force Awakens      300.0
Name: actor_1_facebook_likes, dtype: float64

原始Series和修改过的Series的长度是一样的

len(fb_likes), len(fb_likes_cap)

(4909, 4909)

6.2 dataframe使用where

df = pd.DataFrame({‘vals‘: [1, 2, 3, 4], ‘ids‘: [‘a‘, ‘b‘, ‘f‘, ‘n‘],‘ids2‘: [‘a‘, ‘n‘, ‘c‘, ‘n‘]})

print(df)
print(df < 2)
df.where(df<2,1000)

   vals ids ids2
0     1   a    a
1     2   b    n
2     3   f    c
3     4   n    n
    vals   ids  ids2
0   True  True  True
1  False  True  True
2  False  True  True
3  False  True  True

下面的代码等价于 df.where(df < 0,1000).

print(df[df < 2])
df[df < 2].fillna(1000)

   vals ids ids2
0   1.0   a    a
1   NaN   b    n
2   NaN   f    c
3   NaN   n    n

《Pandas CookBook》---- 第五章 布尔索引

标签：nts   数据   not   body   rev   ola   asp   fill   std   

原文地址：https://www.cnblogs.com/shiyushiyu/p/9742808.html