Python: pivot table with growing columns

Question

I have a table df:

    class teacher January February
0     A    Mary       4        3
1     B     Ann       5        7
2     C   Kevin       6        8

code:

import pandas as pd
df = pd.DataFrame( {'class':['A', 'B', 'C'], 
                    'teacher':['Mary', 'Ann', 'Kevin'],
                    'January':['4', '5', '6'], 
                    'February':['3', '7', '8']
                   } )

I need to pivot month columns to rows as new_df:

     month     class teacher count
0   January      A    Mary      4     
1   January      B    Ann       5        
2   January      C    Kevin     6        
3   February     A    Mary      3     
4   February     B    Ann       7        
5   February     C    Kevin     8       

And the month columns might grow in the future like this, so I need to pivot all of the month to new_df:

df in future:

    class teacher January February March
0     A    Mary       4        3    4
1     B     Ann       5        7    6
2     C   Kevin       6        8    4

Not really sure how to convert df to new_df by pivot. Do I need to swap columns and rows first?

Answer 1

(
    df.set_index(['class', 'teacher'])
      .stack()
      .rename_axis(['class', 'teacher', 'month'])
      .rename('count')
      .reset_index()
)

Or:

df.melt(id_vars=['class', 'teacher'], var_name='month', value_name='count')

---

  class teacher     month count
0     A    Mary   January     4
1     A    Mary  February     3
2     B     Ann   January     5
3     B     Ann  February     7
4     C   Kevin   January     6
5     C   Kevin  February     8