Pandas groupby and qcut
Question:
Is there a way to structure Pandas groupby and qcut commands to return one column that has nested tiles? Specifically, suppose I have 2 groups of data and I want qcut applied to each group and then return the output to one column. This would be similar to MS SQL Server’s ntile() command that allows Partition by().
A B C
0 foo 0.1 1
1 foo 0.5 2
2 foo 1.0 3
3 bar 0.1 1
4 bar 0.5 2
5 bar 1.0 3
In the dataframe above I would like to apply the qcut function to B while partitioning on A to return C.
Answers:
import pandas as pd
df = pd.DataFrame({'A':'foo foo foo bar bar bar'.split(),
'B':[0.1, 0.5, 1.0]*2})
df['C'] = df.groupby(['A'])['B'].transform(
lambda x: pd.qcut(x, 3, labels=range(1,4)))
print(df)
yields
A B C
0 foo 0.1 1
1 foo 0.5 2
2 foo 1.0 3
3 bar 0.1 1
4 bar 0.5 2
5 bar 1.0 3
Is there a way to structure Pandas groupby and qcut commands to return one column that has nested tiles? Specifically, suppose I have 2 groups of data and I want qcut applied to each group and then return the output to one column. This would be similar to MS SQL Server’s ntile() command that allows Partition by().
A B C
0 foo 0.1 1
1 foo 0.5 2
2 foo 1.0 3
3 bar 0.1 1
4 bar 0.5 2
5 bar 1.0 3
In the dataframe above I would like to apply the qcut function to B while partitioning on A to return C.
import pandas as pd
df = pd.DataFrame({'A':'foo foo foo bar bar bar'.split(),
'B':[0.1, 0.5, 1.0]*2})
df['C'] = df.groupby(['A'])['B'].transform(
lambda x: pd.qcut(x, 3, labels=range(1,4)))
print(df)
yields
A B C
0 foo 0.1 1
1 foo 0.5 2
2 foo 1.0 3
3 bar 0.1 1
4 bar 0.5 2
5 bar 1.0 3