Assume you have a series and the result for Boolean operations,
And operation is: 0 True 1 True 2 False dtype: bool Or operation is: 0 True 1 True 2 True dtype: bool Xor operation is: 0 False 1 False 2 True dtype: bool
Solution
To solve this, we will follow the below approach.
Define a Series
Create a series with boolean and nan values
Perform boolean True against bitwise & operation to each element in the series defined below,
series_and = pd.Series([True, np.nan, False], dtype="bool") & True
Perform boolean True against bitwise | operation to each element in the series defined below,
series_or = pd.Series([True, np.nan, False], dtype="bool") | True
Perform boolean True against bitwise ^ operation to each element in the series defined below,
series_xor = pd.Series([True, np.nan, False], dtype="bool") ^ True
Example
Let us see the complete implementation to get a better understanding −
import pandas as pd import numpy as np series_and = pd.Series([True, np.nan, False], dtype="bool") & True print("And operation is: \n",series_and) series_or = pd.Series([True, np.nan, False], dtype="bool") | True print("Or operation is: \n", series_or) series_xor = pd.Series([True, np.nan, False], dtype="bool") ^ True print("Xor operation is: \n", series_xor)
Output
And operation is: 0 True 1 True 2 False dtype: bool Or operation is: 0 True 1 True 2 True dtype: bool Xor operation is: 0 False 1 False 2 True dtype: bool