groupby.value_counts() 之后的熊猫 reset_index

Question

I am trying to groupby a column and compute value counts on another column.

import pandas as pd
dftest = pd.DataFrame({'A':[1,1,1,1,1,1,1,1,1,2,2,2,2,2], 
               'Amt':[20,20,20,30,30,30,30,40, 40,10, 10, 40,40,40]})

print(dftest)

dftest looks like

    A  Amt
0   1   20
1   1   20
2   1   20
3   1   30
4   1   30
5   1   30
6   1   30
7   1   40
8   1   40
9   2   10
10  2   10
11  2   40
12  2   40
13  2   40

perform grouping

grouper = dftest.groupby('A')
df_grouped = grouper['Amt'].value_counts()

which gives

   A  Amt
1  30     4
   20     3
   40     2
2  40     3
   10     2
Name: Amt, dtype: int64

what I want is to keep top two rows of each group

Also, I was perplexed by an error when I tried to reset_index

df_grouped.reset_index()

which gives following error

df_grouped.reset_index() ValueError: cannot insert Amt, already exists

Answer 1

You need parameter name in reset_index , because Series name is same as name of one of levels of MultiIndex :

df_grouped.reset_index(name='count')

Another solution is rename Series name:

print (df_grouped.rename('count').reset_index())

   A  Amt  count
0  1   30      4
1  1   20      3
2  1   40      2
3  2   40      3
4  2   10      2

---

More common solution instead value_counts is aggregate size :

df_grouped1 =  dftest.groupby(['A','Amt']).size().reset_index(name='count')

print (df_grouped1)
   A  Amt  count
0  1   20      3
1  1   30      4
2  1   40      2
3  2   10      2
4  2   40      3