GroupBy一列，对pandas中另一列分组记录进行自定义操作

Question

I wanted to apply a custom operation on a column by grouping the values on another column. Group by column to get the count, then divide the another column value with this count for all the grouped records.

My Data Frame:

   emp opp amount
0  a   1   10
1  b   1   10
2  c   2   30
3  b   2   30
4  d   2   30

My scenario:

• For opp=1, two emp's worked(a,b). So the amount should be shared like 10/2 =5
• For opp=2, two emp's worked(b,c,d). So the amount should be like 30/3 = 10

Final Output DataFrame:

      emp opp amount
    0  a   1   5
    1  b   1   5
    2  c   2   10
    3  b   2   10
    4  d   2   10

What is the best possible to do so

Answer 1

df['amount'] = df.groupby('opp')['amount'].transform(lambda g: g/g.size)

df
#  emp  opp amount
# 0  a    1      5
# 1  b    1      5
# 2  c    2     10
# 3  b    2     10
# 4  d    2     10

Or:

df['amount'] = df.groupby('opp')['amount'].apply(lambda g: g/g.size)

does similar thing.

Answer 2

You could try something like this:

df2 = df.groupby('opp').amount.count()
df.loc[:, 'calculated'] = df.apply( lambda row: \
                                  row.amount / df2.ix[row.opp], axis=1)
df

Yields:

  emp  opp  amount  calculated
0   a    1      10           5
1   b    1      10           5
2   c    2      30          10
3   b    2      30          10
4   d    2      30          10