How to convert true false values in dataframe as 1 for true and 0 for false in python

How to convert true false values in dataframe as 1 for true and 0 for false in python

You can convert True and False values in a pandas DataFrame to 1 and 0 respectively using the .astype(int) method. Here's how you can do it:

import pandas as pd # Sample DataFrame with boolean values data = {'column1': [True, False, True], 'column2': [False, True, False]} df = pd.DataFrame(data) # Convert True/False to 1/0 df = df.astype(int) print(df)

In this example, the .astype(int) method is applied to the entire DataFrame, converting True to 1 and False to 0 in all columns.

Keep in mind that applying .astype(int) will modify the DataFrame in-place. If you want to create a new DataFrame with the converted values without modifying the original DataFrame, you can do:

new_df = df.astype(int)

Examples

  1. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.replace():

    Description: Use the replace() method to replace True and False values with 1 and 0, respectively, in the entire DataFrame.

    import pandas as pd # Assuming df is your DataFrame df.replace({True: 1, False: 0}, inplace=True)

  2. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.applymap():

    Description: Use applymap() to apply a function element-wise to the entire DataFrame, converting True to 1 and False to 0.

    import pandas as pd # Assuming df is your DataFrame df = df.applymap(lambda x: 1 if x else 0)

  3. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.astype():

    Description: Use astype(int) to convert boolean values to integers, where True becomes 1 and False becomes 0.

    import pandas as pd # Assuming df is your DataFrame df = df.astype(int)

  4. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.replace() with a dictionary:

    Description: Use replace() method with a dictionary mapping True to 1 and False to 0 to replace values in specific columns.

    import pandas as pd # Assuming df is your DataFrame and col_names are the columns with boolean values col_names = ['column1', 'column2'] df[col_names] = df[col_names].replace({True: 1, False: 0})

  5. Convert True/False values in DataFrame to 1 for True and 0 for False using numpy.where():

    Description: Use numpy.where() to conditionally assign values in the DataFrame where True becomes 1 and False becomes 0.

    import pandas as pd import numpy as np # Assuming df is your DataFrame df = df.apply(np.where, other=1, cond=df, axis=0).astype(int)

  6. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.replace() with regex:

    Description: Use replace() method with a regular expression to replace True with 1 and False with 0 in the DataFrame.

    import pandas as pd # Assuming df is your DataFrame df = df.replace({True: 1, False: 0}, regex=True)

  7. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.map():

    Description: Use map() method with a dictionary to map True to 1 and False to 0 element-wise in the DataFrame.

    import pandas as pd # Assuming df is your DataFrame df = df.applymap({True: 1, False: 0}.get)

  8. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.replace() with a list:

    Description: Use replace() method with a list of values to replace True with 1 and False with 0 in the DataFrame.

    import pandas as pd # Assuming df is your DataFrame df = df.replace([True, False], [1, 0])

  9. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.map() with a lambda function:

    Description: Use map() method with a lambda function to map True to 1 and False to 0 element-wise in the DataFrame.

    import pandas as pd # Assuming df is your DataFrame df = df.applymap(lambda x: 1 if x else 0)

  10. Convert True/False values in DataFrame to 1 for True and 0 for False using DataFrame.replace() with to_numeric():

    Description: Use replace() method with pd.to_numeric() to convert True to 1 and False to 0 in the DataFrame.

    import pandas as pd # Assuming df is your DataFrame df = df.replace({True: 1, False: 0}).apply(pd.to_numeric)

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