pandas中isin()函数及其逆函数使用

最新推荐文章于 2024-06-24 21:18:01 发布

原创最新推荐文章于 2024-06-24 21:18:01 发布 · 7k 阅读

21 ·

CC 4.0 BY-SA版权

文章标签：

#isin

python 专栏收录该内容

59 篇文章

订阅专栏

博客介绍了DataFrame中的布尔索引，可利用满足布尔条件的列值过滤数据。还提到isin()方法，它接受列表，能判断列中元素是否在列表里，同时提及了isin的逆用法，并给出参考链接，记录了学习过程。

布尔索引

这里你需要知道DateFrame中布尔索引这个东西，可以用满足布尔条件的列值来过滤数据，如下

import numpy as np
import pandas as pd
from pandas import *
from numpy import *

data_1=DataFrame(np.random.randn(4,4),columns=list("ABCD"))
print(data_1)
print(data_1.A>1)
print(data_1[data_1.A>1])
# =============================================================================
#           A         B         C         D
# 0 -1.222857 -1.043994 -0.442975  1.827680
# 1  0.726999  0.563181  2.319214  0.802055
# 2  0.065099 -0.008755  1.202573  0.180843
# 3  1.344852 -1.061955 -0.333201 -0.584720
# 0    False
# 1    False
# 2    False
# 3     True
# Name: A, dtype: bool
#           A         B         C        D
# 3  1.344852 -1.061955 -0.333201 -0.58472
# =============================================================================

isin()接受一个列表，判断该列中元素是否在列表中。

import numpy as np
import pandas as pd
from pandas import *
from numpy import *

data_1=DataFrame(np.random.randn(4,4),columns=list("ABCD"))
print(data_1)
print(data_1.A>1)
print(data_1[data_1.A>1])

# =============================================================================
#           A         B         C         D
# 0  0.169127  0.040550  0.559088  0.017715
# 1  1.994837  0.631279  0.094372  0.614455
# 2  0.682492 -1.601469 -0.569590  0.638142
# 3  1.690618  0.935999 -0.334878 -0.167669
# 0    False
# 1     True
# 2    False
# 3     True
# Name: A, dtype: bool
#           A         B         C         D
# 1  1.994837  0.631279  0.094372  0.614455
# 3  1.690618  0.935999 -0.334878 -0.167669
# =============================================================================

data_1["E"]=list("aabc")
data_1["F"]=list("adfd")
print(data_1)
print(data_1.E.isin(["a","c"]))
print(data_1[data_1.E.isin(["a","c"])])
# =============================================================================
#           A         B         C         D  E  F
# 0  0.169127  0.040550  0.559088  0.017715  a  a
# 1  1.994837  0.631279  0.094372  0.614455  a  d
# 2  0.682492 -1.601469 -0.569590  0.638142  b  f
# 3  1.690618  0.935999 -0.334878 -0.167669  c  d
# 0     True
# 1     True
# 2    False
# 3     True
# Name: E, dtype: bool
#           A         B         C         D  E  F
# 0  0.169127  0.040550  0.559088  0.017715  a  a
# 1  1.994837  0.631279  0.094372  0.614455  a  d
# 3  1.690618  0.935999 -0.334878 -0.167669  c  d
# =============================================================================
#可以同时对多列进行过滤
data_2=data_1[(data_1.A>0)&data_1.E.isin(["a"])&data_1.F.isin(["d","f"])]
print(data_2)
# =============================================================================
#           A         B         C         D  E  F
# 1  1.994837  0.631279  0.094372  0.614455  a  d
# =============================================================================

isin逆用法：

data_1=DataFrame(np.random.randn(4,4),columns=list("ABCD"))
data_1["E"]=list("aabc")
data_1["F"]=list("adfd")
print(data_1)
data_2=data_1[~(data_1.E.isin(["a"])&data_1.F.isin(["d","f"]))]
print(data_2)
# =============================================================================
#           A         B         C         D  E  F
# 0 -1.367389 -0.010230 -1.129077  1.710639  a  a
# 1  1.685804  2.060111  1.262265  0.729453  a  d
# 2  0.277453 -1.108263  0.150806 -1.038848  b  f
# 3 -1.364977 -0.343693  0.450186  1.236273  c  d
#           A         B         C         D  E  F
# 0 -1.367389 -0.010230 -1.129077  1.710639  a  a
# 2  0.277453 -1.108263  0.150806 -1.038848  b  f
# 3 -1.364977 -0.343693  0.450186  1.236273  c  d
# =============================================================================

参考：https://blog.youkuaiyun.com/lzw2016/article/details/80472649

以上，记录本人学习过程。