How to replace all column values except the first column values based on some condition

Question

Still learning the nuances of python so please excuse me if the question is trivial for some.

I have a data frame of format:

I want to replace all columns except the index in a way such that:

1. values between 0.1 and 0.4 are replaced with 'low'
2. values between 0.4 and 0.6 are replaced with 'medium'
3. values between 0.6 and 1 are replaced with "high"

So, the resulting column will look like:

I am aware that I can replace a specific column values using something like:

Referred here: https://kanoki.org/2019/07/17/pandas-how-to-replace-values-based-on-conditions/ Replace a specific range of values in a pandas dataframe

df['column1'] = np.where(df['column1'] < 0.4, 'Low', df['column1'])
df['column1'] = np.where(df['column1'].between(0.4,0.6), 'Medium', df['column1'])
df['column1'] = np.where(df['column1'] > 0.6, 'High', df['column1'])

Also, there is one issue with above approach. After first replacement of values I get error
Probably because, the string 'low' is now present in df and is causing trouble when comparing with int values.

Is there a way to replace for all three columns in a more pythonic way? One may ignore first column because I might as well drop it temporarily and then add it back after value replacement in column1/2/3. My main question is how to replace for all columns in a concise and pythonic way.

Thanks,

Answer 1

You can use numpy.select:

>>> import numpy as np
>>> subset = df[['column1', 'column2', 'column3']]
>>> df.loc[:, subset.columns] =  np.select([subset < 0.4,
                                           (0.4 <= subset) & (subset <= 0.6),
                                           subset > 0.6],
                                           ['Low', 'Medium', 'High'])

   my_index column1 column2 column3
0        54  Medium  Medium    High
1       101     Low  Medium    High
2        75    High    High     Low