如何在多列中使用pandas isin [英] how to use pandas isin for multiple columns

问题描述

我想找到col1和col2的值，其中第一个数据帧的col1和col2都在第二个数据帧中.

I want to find the values of col1 and col2 where the col1 and col2 of the first dataframe are both in the second dataframe.

这些行应位于结果数据框中:

These rows should be in the result dataframe:

1. 比萨饼，男孩

1. pizza, boy

比萨饼，女孩

冰淇淋，男孩

因为所有三行都在第一个和第二个数据帧中.

because all three rows are in the first and second dataframes.

我怎么可能做到这一点?我当时想使用isin，但是当我不得不考虑不止一列时，我不确定如何使用它.

How do I possibly accomplish this? I was thinking of using isin, but I am not sure how to use it when I have to consider more than one column.

推荐答案

执行

Perform an inner merge on col1 and col2:

import pandas as pd
df1 = pd.DataFrame({'col1': ['pizza', 'hamburger', 'hamburger', 'pizza', 'ice cream'], 'col2': ['boy', 'boy', 'girl', 'girl', 'boy']}, index=range(1,6))
df2 = pd.DataFrame({'col1': ['pizza', 'pizza', 'chicken', 'cake', 'cake', 'chicken', 'ice cream'], 'col2': ['boy', 'girl', 'girl', 'boy', 'girl', 'boy', 'boy']}, index=range(10,17))

print(pd.merge(df2.reset_index(), df1, how='inner').set_index('index'))

收益

            col1  col2
index                 
10         pizza   boy
11         pizza  girl
16     ice cream   boy

reset_index和set_index调用的目的是保留df2的索引，就像您发布的所需结果一样.如果索引不重要，则

The purpose of the reset_index and set_index calls are to preserve df2's index as in the desired result you posted. If the index is not important, then

pd.merge(df2, df1, how='inner')
#         col1  col2
# 0      pizza   boy
# 1      pizza  girl
# 2  ice cream   boy

足够了.

或者，您可以在其中构建 MultiIndex s col1和col2列，然后调用 MultiIndex.isin方法:

Alternatively, you could construct MultiIndexs out of the col1 and col2 columns, and then call the MultiIndex.isin method:

index1 = pd.MultiIndex.from_arrays([df1[col] for col in ['col1', 'col2']])
index2 = pd.MultiIndex.from_arrays([df2[col] for col in ['col1', 'col2']])
print(df2.loc[index2.isin(index1)])

收益

         col1  col2
10      pizza   boy
11      pizza  girl
16  ice cream   boy

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