How can I split my normalization in two according to column values?

Question

HI I have a column data in pandas with a hugely skewed distribution:

I split the data in two according to a cutoff value of 1000 and this is the distribution of the two groups.

Now, I want to normalize with values between 0-1. I want to perform a 'differential' normalization, in a way that the left panel values are normalized between 0-0.5 and the right panel is normalized to 0.5 to 1, everything in the same column. How can I do it?

Answer 1

It's not pretty, but works.

df = pd.DataFrame({'dataExample': [0,1,2,1001,1002,1003]})

less1000 = df.loc[df['dataExample'] <= 1000]
df.loc[df['dataExample'] <= 1000, 'datanorm'] =  less1000['dataExample'] / (less1000['dataExample'].max() * 2)

high1000 = df.loc[df['dataExample'] > 1000]
df.loc[df['dataExample'] > 1000, 'datanorm'] =  ((high1000['dataExample'] - high1000['dataExample'].min()) / ((high1000['dataExample'].max() - high1000['dataExample'].min()) * 2) + 0.5)

output:
    dataExample datanorm
0   0   0.00
1   1   0.25
2   2   0.50
3   1001    0.50
4   1002    0.75
5   1003    1.00