How to rename multiple columns to Reset index using Pandas

Question

I am trying to figure out if there is a way to rename Pandas columns when trying to reset the index. I see in the documentation that you can use the "name" parameter to set the index column name to reset if there is only one column, but I'm curious if there is a way to do this for multiple columns.

For instance:

df1 = pd.DataFrame({ 'A' : ['a1', 'a1', 'a2', 'a3'], 'B' : ['b1', 'b2', 'b3', 'b4'], 'D1' : [1,0,0,0], 'D2' : [0,1,1,0], 'D3' : [0,0,1,1], }) df1.set_index(['B','A']).stack().reset_index() 

As a result, you will receive:

 Out[82]: BA level_2 0 0 b1 a1 D1 1 1 b1 a1 D2 0 2 b1 a1 D3 0 3 b2 a1 D1 0 4 b2 a1 D2 1 

You can do:

 df1.set_index(['B','A']).stack().reset_index(name='my_col') 

To set the name of the last column, but I wonder if there is a way to use this parameter to set the column name to "level_2".

The first thing that occurred to me was to try:

 df1.set_index(['B','A']).stack().reset_index(name=['my_col2','my_col']) 

However, this did not work, so you need to look for another way. I understand that I could always just rename the columns in the next row, but I hoped there would be a cleaner way to do this in one row.

Thanks! Sam

Answer 1

reset_index not smart enough to do this, but we could use the rename_axis and rename methods to give names to the index and columns / series before resetting the index; once the names are configured correctly, reset_index will automatically convert these names to column names as a result:

Here rename_axis gives the names for the index, which is somewhat equivalent to df.index.names = ... , except for the functional style; rename gives the name of the Series object:

 df1.set_index(['B','A']).stack().rename_axis(['B','A','col2']).rename('col').reset_index() # BA col2 col #0 b1 a1 D1 1 #1 b1 a1 D2 0 #2 b1 a1 D3 0 #3 b2 a1 D1 0 #4 b2 a1 D2 1 # ..