两个或多个DataFrame列的交集 [英] Intersection of two or more DataFrame columns
问题描述
我试图找到三个数据帧的交集,但是pd.intersect1d
不喜欢使用三个数据帧.
I am trying to find the intersect of three dataframes, however the pd.intersect1d
does not like to use three dataframes.
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('BCDE'))
df3 = pd.DataFrame(np.random.randint(0,10,size=(10, 4)), columns=list('CDEF'))
inclusive_list = np.intersect1d(df1.columns, df2.columns, df3.columns)
错误:
ValueError: The truth value of a Index is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
inclusive_list
应该仅包括列名称C& D.任何帮助将不胜感激.谢谢你.
The inclusive_list
should only include column names C & D. Any help would be appreciated. Thank you.
推荐答案
为什么当前的方法不起作用 :
Why your current approach doesn't work:
intersect1d
确实可以不使用N
数组,它仅比较2.
intersect1d
does not take N
arrays, it only compares 2.
numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)
从定义中可以看到,您将第三个数组作为assume_unique
参数传递,并且由于您将数组视为单个布尔值,因此会收到ValueError
.
You can see from the definition that you are passing the third array as the assume_unique
parameter, and since you are treating an array like a single boolean, you receive a ValueError
.
您可以使用functools.reduce
扩展intersect1d
的功能以在N
阵列上工作:
You can extend the functionality of intersect1d
to work on N
arrays using functools.reduce
:
from functools import reduce
reduce(np.intersect1d, (df1.columns, df2.columns, df3.columns))
array(['C', 'D'], dtype=object)
更好的方法
A better approach
但是,最简单的方法是仅在Index
对象上使用交集:
However, the easiest approach is to just use intersection on the Index
object:
df1.columns & df2.columns & df3.columns
Index(['C', 'D'], dtype='object')
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