PANDAS BOOLEAN DataFrameS

Pandas supports Boolean operations on DataFrames, such as the logical or, the logical and, and the logical negation of a pair of DataFrames. Listing 3.3 displays the contents of pandas_boolean_df.py that illustrates how to define a Pandas DataFrame whose rows and columns are Boolean values.

LISTING 3.3: pandas_boolean_df.py

import pandas as pd

df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1] }, dtype=bool)
df2 = pd.DataFrame({'a': [0, 1, 1], 'b': [1, 1, 0] }, dtype=bool)

print("df1 & df2:")
print(df1 & df2)

print("df1 | df2:")
print(df1 | df2)

print("df1 ^ df2:")
print(df1 ^ df2)

Listing 3.3 initializes the DataFrames df1 and df2, and then computes df1 & df2, df1 | df2, df1 ^ df2, which represent the logical AND, the logical OR, and the logical negation, respectively, of df1 and df2. The output from launching the code in Listing 3.3 is here:

df1 & df2:
       a     ...