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:
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
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.
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
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