Pandas: Merge (Join) Two Data Frames on Multiple Columns

Pandas: Merge (Join) Two Data Frames on Multiple Columns

I am trying to join two pandas data frames using two columns:

new_df = pd.merge(A_df, B_df,  how='left', left_on='[A_c1,c2]', right_on = '[B_c1,c2]')

but got the following error:

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4164)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4028)()

pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13166)()

pandas/src/hashtable_class_helper.pxi in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13120)()

KeyError: '[B_1, c2]'

Any idea what should be the right way to do this? Thanks!

3

4 Answers

Try this

new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1','c2'], right_on = ['B_c1','c2'])

left_on : label or list, or array-like Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns

right_on : label or list, or array-like Field names to join on in right DataFrame or vector/list of vectors per left_on docs

4

the problem here is that by using the apostrophes you are setting the value being passed to be a string, when in fact, as @Shijo stated from the documentation, the function is expecting a label or list, but not a string! If the list contains each of the name of the columns beings passed for both the left and right dataframe, then each column-name must individually be within apostrophes. With what has been stated, we can understand why this is inccorect:

new_df = pd.merge(A_df, B_df,  how='left', left_on='[A_c1,c2]', right_on = '[B_c1,c2]')

And this is the correct way of using the function:

new_df = pd.merge(A_df, B_df,  how='left', left_on=['A_c1','c2'], right_on = ['B_c1','c2'])

Another way of doing this:

new_df = A_df.merge(B_df, left_on=['A_c1','c2'], right_on = ['B_c1','c2'], how='left')

you can use below which is short and simple to understand:

merged_data= df1.merge(df2, on=["column1","column2"])

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Sarah Jenkins
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Sarah Jenkins

Sarah Jenkins is a veteran tech journalist with over 12 years of experience covering artificial intelligence, mobile innovations, and digital ethics. Her insights have appeared in leading technology publications worldwide.