Using Merge on a column and Index in Pandas

If you want to use an index in your merge you have to specify left_index=True or right_index=True, and then use left_on or right_on. For you it should look something like this:

merged = pd.merge(type_df, time_df, left_index=True, right_on='Project')

Another solution is use DataFrame.join:

df3 = type_df.join(time_df, on='Project')

For version pandas 0.23.0+ the on, left_on, and right_on parameters may now refer to either column names or index level names:

left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],                    'B': ['B0', 'B1', 'B2', 'B3'],                     'key2': ['K0', 'K1', 'K0', 'K1']},                    index=left_index)                    right_index = pd.Index(['K0', 'K1', 'K2', 'K2'], name='key1')right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],                     'D': ['D0', 'D1', 'D2', 'D3'],                     'key2': ['K0', 'K0', 'K0', 'K1']},                      index=right_index)          print (left)           A   B key2key1             K0    A0  B0   K0K0    A1  B1   K1K1    A2  B2   K0K2    A3  B3   K1        print (right)       C   D key2key1             K0    C0  D0   K0K1    C1  D1   K0K2    C2  D2   K0K2    C3  D3   K1

df = left.merge(right, on=['key1', 'key2'])print (df)       A   B key2   C   Dkey1                     K0    A0  B0   K0  C0  D0K1    A2  B2   K0  C1  D1K2    A3  B3   K1  C3  D3

You must have the same column in each dataframe to merge on.

In this case, just make a 'Project' column for type_df, then merge on that:

type_df['Project'] = type_df.index.valuesmerged = pd.merge(time_df,type_df, on='Project', how='inner')merged#    Project  Time Project Type#0  Project1    13       Type 2#1  Project1    12       Type 2#2  Project2    41       Type 1print merged[merged['Project Type'] == 'Type 2']['Project Type'].count()2