Normalize within groups in Pandas

Question

I have a collection of data that has a grouping variable, a position, and a value at that position:

Sample    Position    Depth

A         1           2

A         2           3

A         3           4

B         1           1

B         2           3

B         3           2

I want to create a new column that is a privately normalized depth as follows:

Sample    Position    Depth    NormalizedDepth

A         1           2        0

A         2           3        0.5

A         3           4        1

B         1           1        0

B         2           3        1

B         3           2        0.5

This is typically expressed by the formula NormalizedDepth = (x - min(x))/(max(x)-min(x)) such that the minimum and maximum are of the group.

I know that we can achieve that with dplyr in R with the following:

depths %>% 

  group_by(Sample) %>%

  mutate(NormalizedDepth = 100 * (Depth - min(Depth))/(max(Depth) - min(Depth)))

I just want to know that how we can do this with pandas

Answer 1

You can just use the transform() with ptp (getting the difference between the max and min):

import numpy as np

g=df.groupby('Sample').Depth

df['new']=(df.Depth-g.transform('min'))/g.transform(np.ptp)

0    0.0

1    0.5

2    1.0

3    0.0

4    1.0

5    0.5

Name: Depth, dtype: float64

If you are a beginner and want to know more about R then do refer to the R programming course.