How to keep index when using pandas merge

Question

In [5]: a.reset_index().merge(b, how="left").set_index('index')
Out[5]:
       col1  to_merge_on  col2
index
a         1            1     1
b         2            3     2
c         3            4   NaN

Note that for some left merge operations, you may end up with more rows than in a when there are multiple matches between a and b. In this case, you may need to drop duplicates.

You can make a copy of index on left dataframe and do merge.

a['copy_index'] = a.index
a.merge(b, how='left')

I found this simple method very useful while working with large dataframe and using pd.merge_asof() (or dd.merge_asof()).

This approach would be superior when resetting index is expensive (large dataframe).

There is a non-pd.merge solution using Series.map and DataFrame.set_index.

In: a['col2'] = a['to_merge_on'].map(b.set_index('to_merge_on')['col2']))
In: a['col2']
Out:
   col1  to_merge_on  col2
a     1            1   1.0
b     2            3   2.0
c     3            4   NaN

This doesn't introduce a dummy index name for the index.

Note however that there is no DataFrame.map method, and so this approach is not for multiple columns.

df1 = df1.merge(df2, how="inner", left_index=True, right_index=True)

This allows to preserve the index of df1

Assuming that the resulting df has the same number of rows and order as your first df, you can do this:

c = pd.merge(a, b, on='to_merge_on')
c.set_index(a.index,inplace=True)

another simple option is to rename the index to what was before:

a.merge(b, how="left").set_axis(a.index)

merge preserves the order at dataframe 'a', but just resets the index so it's save to use set_axis