17

I have the following dataframe:

import pandas as pd
import numpy as np
df1 = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(),
                   'B': 'one one two three two two one three'.split(),
                   'C': np.arange(8), 'D': np.arange(8) * 2})
print(df1)

    A      B   C   D
0  foo    one  0   0
1  bar    one  1   2
2  foo    two  2   4
3  bar  three  3   6
4  foo    two  4   8
5  bar    two  5  10
6  foo    one  6  12
7  foo  three  7  14

I hope to select rows in df1 by the df2 as follows:

df2 = pd.DataFrame({'A': 'foo bar'.split(),
                   'B': 'one two'.split()
                   })
print(df2)

     A    B
0  foo  one
1  bar  two

Here is what I have tried in Python, but I just wonder if there is another method. Thanks.

df = df1.merge(df2, on=['A','B'])
print(df)

This is the output expected.

    A      B   C   D
0  foo    one  0   0
1  bar    two  5  10
2  foo    one  6  12

Using pandas to select rows using two different columns from dataframe?

Select Columns of a DataFrame based on another DataFrame

ah bon
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1 Answers1

20

Simpliest is use merge with inner join.

Another solution with filtering:

arr = [np.array([df1[k] == v for k, v in x.items()]).all(axis=0) for x in df2.to_dict('r')]
df = df1[np.array(arr).any(axis=0)]
print(df)
     A    B  C   D
0  foo  one  0   0
5  bar  two  5  10
6  foo  one  6  12

Or create MultiIndex and filter with Index.isin:

df = df1[df1.set_index(['A','B']).index.isin(df2.set_index(['A','B']).index)]
print(df)
     A    B  C   D
0  foo  one  0   0
5  bar  two  5  10
6  foo  one  6  12
jezrael
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