Merge two dataframes by index

Use merge, which is an inner join by default:

pd.merge(df1, df2, left_index=True, right_index=True)

Or join, which is a left join by default:

df1.join(df2)

Or concat), which is an outer join by default:

pd.concat([df1, df2], axis=1)

Samples:

df1 = pd.DataFrame({'a':range(6),                    'b':[5,3,6,9,2,4]}, index=list('abcdef'))print (df1)   a  ba  0  5b  1  3c  2  6d  3  9e  4  2f  5  4df2 = pd.DataFrame({'c':range(4),                    'd':[10,20,30, 40]}, index=list('abhi'))print (df2)   c   da  0  10b  1  20h  2  30i  3  40

# Default inner joindf3 = pd.merge(df1, df2, left_index=True, right_index=True)print (df3)   a  b  c   da  0  5  0  10b  1  3  1  20# Default left joindf4 = df1.join(df2)print (df4)   a  b    c     da  0  5  0.0  10.0b  1  3  1.0  20.0c  2  6  NaN   NaNd  3  9  NaN   NaNe  4  2  NaN   NaNf  5  4  NaN   NaN# Default outer joindf5 = pd.concat([df1, df2], axis=1)print (df5)     a    b    c     da  0.0  5.0  0.0  10.0b  1.0  3.0  1.0  20.0c  2.0  6.0  NaN   NaNd  3.0  9.0  NaN   NaNe  4.0  2.0  NaN   NaNf  5.0  4.0  NaN   NaNh  NaN  NaN  2.0  30.0i  NaN  NaN  3.0  40.0

You can use concat([df1, df2, ...], axis=1) in order to concatenate two or more DFs aligned by indexes:

pd.concat([df1, df2, df3, ...], axis=1)

Or merge for concatenating by custom fields / indexes:

# join by _common_ columns: `col1`, `col3`pd.merge(df1, df2, on=['col1','col3'])# join by: `df1.col1 == df2.index`pd.merge(df1, df2, left_on='col1' right_index=True)

or join for joining by index:

 df1.join(df2)

By default:
join is a column-wise left join
pd.merge is a column-wise inner join
pd.concat is a row-wise outer join

pd.concat:
takes Iterable arguments. Thus, it cannot take DataFrames directly (use [df,df2])
Dimensions of DataFrame should match along axis

Join and pd.merge:
can take DataFrame arguments