[英]Pandas - get dataframe rows based on matching columns with other dataframe
Assume two dataframes, one called df1 and the other df2:假设有两个数据帧,一个叫 df1,另一个叫 df2:
df1 = pd.DataFrame.from_dict({'col1': [1,9,4,7],
'col2': [6,4,3,7],
'col3': [1,4,7,8]})
df2 = pd.DataFrame.from_dict({'col1': [4,8,2,7],
'col2': [7,3,3,3],
'col3': [2,7,7,5]})
df1:
col1 col2 col3
0 1 6 1
1 9 4 4
2 4 3 7
3 7 7 8
df2:
col1 col2 col3
0 4 7 2
1 8 3 7
2 2 3 7
3 7 3 5
As you can see, in both dataframes in 'col2', and 'col3' we have the combination (3, 7).如您所见,在 'col2' 和 'col3' 的两个数据帧中,我们都有 (3, 7) 的组合。 I would like to iterate the rows of df1, and use col2 and col3 as a filter for df2 is such a way that for index (0, 1, 3) in df1 we will receive an empty dataframe, but for row 2 we will receive a dataframe with the rows of indexes (1,2) in df2 with its original indexes:我想迭代 df1 的行,并使用 col2 和 col3 作为 df2 的过滤器,这样对于 df1 中的索引(0、1、3),我们将收到一个空的 dataframe,但对于第 2 行,我们将收到一个 dataframe 与 df2 中的索引行 (1,2) 及其原始索引:
new dataframe:
col1 col2 col3
1 8 3 7
2 2 3 7
Found the solution here for anyone interested: Select rows from a Pandas DataFrame with exactly the same column values in another DataFrame在这里为任何感兴趣的人找到解决方案: Select 行来自 Pandas DataFrame 与另一个 ZBA834Z5C175EB8E12 中的列值完全相同
df1.merge(df2[['col2', 'col3']])
You can use pd.merge
with how as inner
您可以将pd.merge
与 how as inner
一起使用
df2.reset_index().merge(df1[['col2','col3']], how="inner").set_index('index')
col1 col2 col3
index
1 8 3 7
2 2 3 7
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