Standardize some columns in Python Pandas dataframe?
Or in case the column index is used instead of the column names:
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.DataFrame({"cost": [300,3,1,5,2], "sales": [50,88,70,80,999], "item": ["pen","bottle","drink","cup","ink"]})
# Scale selected columns by index
df.iloc[:, 0:2] = StandardScaler().fit_transform(df.iloc[:, 0:2])
cost sales item
0 1.999876 -0.559003 pen
1 -0.497867 -0.456582 bottle
2 -0.514686 -0.505097 drink
3 -0.481047 -0.478144 cup
4 -0.506276 1.998826 ink
The sclaer object can also be saved so to scale "new data" based on the existing scaler:
df = pd.DataFrame({"cost": [300,3,1,5,2], "sales": [50,88,70,80,999], "item": ["pen","bottle","drink","cup","ink"]})
df_new = pd.DataFrame({"cost": [299,5,12,64,2], "sales": [55,99,48,20,999], "item": ["pen","bottle","drink","cup","ink"]})
# Set up scaler
scaler = StandardScaler().fit(df.iloc[:, 0:2])
# Scale original data
df.iloc[:, 0:2] = scaler.transform(df.iloc[:, 0:2])
# Scale new data
df_new.iloc[:, 0:2] = scaler.transform(df_new.iloc[:, 0:2])
Simply assign it back
df[['cost', 'sales']] = StandardScaler().fit_transform(df[['cost', 'sales']])
df
Out[45]:
id cost sales item
0 1 1.999876 -0.559003 pen
1 2 -0.497867 -0.456582 bottle
2 3 -0.514686 -0.505097 drink
3 4 -0.481047 -0.478144 cup
4 5 -0.506276 1.998826 ink