In the pandas I use df[df['Column']!='value'] to get desired column values. But how can I use it for multiple condition?

I used this

import numpy as np
df[df['Column']!=np.nan and df['Column']!='value_x' and df['Column']!='value_y']

But it is throwing

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

CodePudding user response：

For compare missing values need Series.notna and because priority operators add () in next conditions, for bitwise AND use &:

df[df['Column'].notna() & (df['Column']!='value_x') & (df['Column']!='value_y')]

Another out of box solution is replace missing values, test if value_x or value_y and invert condition by ~:

df[~df['Column'].fillna('value_x').isin(['value_x','value_y'])]