Pandas – group by quantile position

Question

I have a pandas dataframe with the following structure:

import pandas as pd
df = pd.DataFrame(
    {
        "value": [0, 1, 2, 3, 4, 5, 5, 4, 3, 2, 1, 0],
        "group": ["A"] * 6 + ["B"] * 6
    }
)

I would like to obtain a new dataframe, with the same number of rows, that has, for each row the quantile that corresponds to the value in the group.

For this case, the output would be like this:

There can be a very large number of groups and values on completely different scales, and each group may have different sizes.

Asked By: David Masip

||

Source

Answer 1

import pandas as pd

df = pd.DataFrame(
    {
        "value": [0, 1, 2, 3, 4, 5, 5, 4, 3, 2, 1, 0],
        "group": ["A"] * 6 + ["B"] * 6,
    }
)


def norm_group(group):
    g_min = group.value.min()
    g_max = group.value.max()
    group["quantile"] = (group.value - g_min) / (g_max - g_min)
    return group


print(df.groupby("group").apply(norm_group))

seems to do the trick:

    value group  quantile
0       0     A       0.0
1       1     A       0.2
2       2     A       0.4
3       3     A       0.6
4       4     A       0.8
5       5     A       1.0
6       5     B       1.0
7       4     B       0.8
8       3     B       0.6
9       2     B       0.4
10      1     B       0.2
11      0     B       0.0

Answered By: AKX

Pandas – group by quantile position

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