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Adding a prefix to column values Adding leading zeros to strings of a column Adding new column using lists Adding padding to a column of strings Bit-wise OR Changing column type to string Conditionally updating values of a DataFrame Converting all object-typed columns to categorical type Converting column type to date Converting column type to float Converting column type to integer Converting K and M to numerical form Converting string categories or labels to numeric values Encoding categorical variables Expanding lists vertically in a DataFrame Expanding strings vertically in a DataFrame Extracting numbers from column Filling missing value in Index of DataFrame Filtering column values using boolean masks Logical AND operation Making DataFrame string column lowercase Mapping True and False to 1 and 0 respectively Mapping values of a DataFrame using a dictionary Modifying a single value in a DataFrame Removing characters from columns Removing comma from column values Removing first n characters from column values Removing last n characters from column values Removing leading substring Removing trailing substring Replacing infinities with another value in DataFrame Replacing values in a DataFrame Rounding values Sorting categorical columns Using previous row to create new columns

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Logical AND operation in Pandas DataFrame

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Data Manipulation Cookbook

schedule Jul 1, 2022

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Use & to perform a logical AND operation in Pandas DataFrame.

Consider the following DataFrame:


        
        
            
                
                
                    import pandas as pd
df = pd.DataFrame({"A":[True,False,False],"B":[True,True,False]})
df
                
            
               A      B
0  True   True
1  False  True
2  False  False

To perform a bit-wise logical AND operation between columns A and B:


        
        
            
                
                
                    df["A"] & df["B"]
                
            
            0     True
1    False
2    False
dtype: bool

Here, a True is returned for the first row because both columns A and B have True as the first row.

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