Issue
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
Solution
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
Answered By - jezrael
0 comments:
Post a Comment
Note: Only a member of this blog may post a comment.