Modifying Dataframe based on Column Names and values in it

Question

I have following dataframe

A	B	C	D	E
-0.1	0	0.2	0	4
0	0	-1	-2	5

I would like an output as following based on A,B belong to category X and C,D,E belong to category Y -

A	B	C	D	E
-cat X	0	+cat Y	0	+cat Y
0	0	-cat Y	-cat Y	+cat Y

It basically checks the column name and assigns a category and checks the value to assign a sign. Is there any easy way to do this in Python? I am using COLAB, so probably have latest version.

92Sanki · Accepted Answer · 2021-03-23 18:19:35Z

1

One way could be to just choose whatever columns you want and then perform check like below:

import numpy as np

for col in ['A','B']:
    df[col] = [x if x == 0 else '-Cat X' if x < 0 else '+ Cat X' for x in df[col]]

for col in ['C','D','E']:
    df[col] = [x if x == 0 else '-Cat Y' if x < 0 else '+ Cat Y' for x in df[col]]

This should produce the required result.

answered Mar 23, 2021 at 18:19

92Sanki

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2 Comments

Pushkar Over a year ago

If there are many categories and many columns this would look very ugly, is there a way I can reference a dictionary and use X/Y as value of some variable(object)?

92Sanki Over a year ago

I suggest np.select then. It allows you to set multiple conditions and their choices along with default values if required.numpy.org/doc/stable/reference/generated/numpy.select.html

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