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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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Converting all object-typed columns to categorical type in Pandas DataFrame

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Data Manipulation Cookbook

schedule Jul 1, 2022

Last updated

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Solution

To convert object-typed columns to categorical:


        
        
            
                
                
                    list_str_obj_cols = df.columns[df.dtypes == "object"].tolist()
for str_obj_col in list_str_obj_cols:
    df[str_obj_col] = df[str_obj_col].astype("category")

df.dtypes
                
            
            A    category
B    category
dtype: object

Explanation

We first obtain a list of the column labels where the column is of type object:


        
        
            
                
                
                    list_str_obj_cols = df.columns[df.dtypes == "object"].tolist()
list_str_obj_cols
                
            
            ['A', 'B']