Normalizing Pandas Series With Condition

I'm learning Python/Pandas with a DataFrame having the following structure: import pandas as pd df = pd.DataFrame({'key' : [111, 222, 333, 444, 555, 666, 777, 888, 999],

Solution 1:

Idea is convert -1 values to missing values:

cols = ['score1','score2']
df[cols] = df[cols].mask(df[cols] == -1)

x = df[cols].values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = df.join(pd.DataFrame(x_scaled, columns=cols).add_prefix('norm_'))
print (df)
   key  score1  score2  norm_score1  norm_score2
0  111     NaN     2.0          NaN          0.2
1  222     0.0     2.0     0.000000          0.2
2  333     2.0     NaN     0.133333          NaN
3  444     NaN    10.0          NaN          1.0
4  555     7.0     0.0     0.466667          0.0
5  666     0.0     5.0     0.000000          0.5
6  777    15.0     NaN     1.000000          NaN
7  888     0.0     1.0     0.000000          0.1
8  999     1.0     0.0     0.066667          0.0