python - pandas isin() function with lists -

i have 2 data frames , want use

isin() method

to check exists in df1 , in df2.

when compare 1 column (index1) - result ok - 2 values of df1.index1 exists in df2.index1.

but when compare 2 columns (index1, index2) of both df1 , df2 result false 3 rows, there 1 common row (index1 = 3, index2 = 6).

why that?

thanks.

in [2]: import pandas pd in [3]: df1 = pd.dataframe({'a': [1,2,3], 'index1':[1,2,3], 'index2':[4,5,6]}) in [4]: df2 = pd.dataframe({'a': [4,5,6], 'index1':[2,3,4], 'index2':[7,6,5]})   in [5]: df1 out[5]:         index1  index2 0  1       1       4 1  2       2       5 2  3       3       6  in [6]: df2 out[6]:         index1  index2 0  4       2       7 1  5       3       6 2  6       4       5  in [7]: df1['index1'].isin(df2['index1']) out[7]:  0     false 1     true 2     true name: index1, dtype: bool  in [8]: df1['index2'].isin(df2['index2']) out[8]:  0     false 1     true 2     true name: index2, dtype: bool  in [9]: df1[['index1','index2']].isin(df2[['index1', 'index2']]) out[9]:       index1 index2 0  false  false 1  false  false 2  false  false 

it's because "the result true @ location if labels match." (docs). if want ignore labels, need use underlying numpy array:

df1[['index1','index2']].isin(df2[['index1', 'index2']].values.ravel()) out:     index1  index2 0   false    true 1    true    true 2    true    true 

if want labels match columns, can use dictionary:

df1[['index1','index2']].isin(df2[['index1', 'index2']].to_dict('l')) out:     index1  index2 0   false   false 1    true    true 2    true    true 

if want check whether rows match, need use merge:

df1.merge(df2, how='left', on=['index1', 'index2'], indicator=true) out:     a_x  index1  index2  a_y     _merge 0    1       1       4  nan  left_only 1    2       2       5  nan  left_only 2    3       3       6  5.0       both 

the _merge column returns both when both index1 , index2 match.