Pandas conditional creation of a series/dataframe column

Pandas conditional creation of a series/dataframe column

You can conditionally create a new column in a Pandas DataFrame using the numpy.where() function or the pandas.DataFrame.apply() method with a custom function. Here are two common approaches:

1. Using numpy.where() function:

The numpy.where() function allows you to create a new column based on a condition. It has the following syntax:

import pandas as pd import numpy as np # Sample DataFrame data = {'A': [1, 2, 3, 4, 5]} df = pd.DataFrame(data) # Conditionally create a new column 'B' based on a condition df['B'] = np.where(df['A'] > 3, 'High', 'Low') print(df)

In this example, we create a new column 'B' where the values are 'High' if the corresponding value in column 'A' is greater than 3, and 'Low' otherwise.

2. Using pandas.DataFrame.apply() method:

You can also use the apply() method along with a custom function to conditionally create a column. Here's an example:

import pandas as pd # Sample DataFrame data = {'A': [1, 2, 3, 4, 5]} df = pd.DataFrame(data) # Define a custom function to apply the condition def categorize_value(x): if x > 3: return 'High' else: return 'Low' # Apply the custom function to create a new column 'B' df['B'] = df['A'].apply(categorize_value) print(df)

In this example, we define the categorize_value function to apply the condition, and then we use the apply() method to create a new column 'B' based on the result of the function.

Both of these methods will create a new column in the DataFrame based on a specified condition. You can adapt the condition and the function to your specific requirements.

Examples

  1. How to conditionally create a new column in Pandas DataFrame based on a single condition?

    Description: Learn how to create a new column in a Pandas DataFrame based on a single condition using np.where() function.

    import pandas as pd import numpy as np # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Conditionally create new column 'B' based on condition df['B'] = np.where(df['A'] > 3, 'Yes', 'No')

  2. Creating a new column in Pandas DataFrame based on multiple conditions

    Description: Understand how to create a new column in a Pandas DataFrame based on multiple conditions using np.select() function.

    import pandas as pd import numpy as np # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Define conditions and choices conditions = [df['A'] < 3, df['A'] >= 3] choices = ['Low', 'High'] # Conditionally create new column 'B' based on multiple conditions df['B'] = np.select(conditions, choices, default='Medium')

  3. How to use boolean indexing to create a new column in Pandas DataFrame?

    Description: Explore using boolean indexing to create a new column in a Pandas DataFrame based on a condition.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Create new column 'B' based on condition using boolean indexing df['B'] = 'Low' df.loc[df['A'] >= 3, 'B'] = 'High'

  4. Conditional creation of a column in Pandas DataFrame using lambda functions

    Description: Learn how to use lambda functions for conditional column creation in a Pandas DataFrame.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Conditionally create new column 'B' using lambda function df['B'] = df['A'].apply(lambda x: 'High' if x >= 3 else 'Low')

  5. Creating a new column in Pandas DataFrame based on a string condition

    Description: Understand how to create a new column in a Pandas DataFrame based on a string condition using boolean indexing.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': ['apple', 'banana', 'apple', 'orange', 'banana']}) # Create new column 'B' based on string condition df['B'] = 'Fruit' df.loc[df['A'] == 'banana', 'B'] = 'Tropical Fruit'

  6. Conditional creation of a column in Pandas DataFrame using .loc

    Description: Learn how to conditionally create a new column in a Pandas DataFrame using .loc for indexing.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Conditionally create new column 'B' using .loc df['B'] = 'Low' df.loc[df['A'] >= 3, 'B'] = 'High'

  7. How to create a new column in Pandas DataFrame based on a function condition?

    Description: Explore creating a new column in a Pandas DataFrame based on a custom function condition.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Define custom function for condition def condition_func(x): if x < 3: return 'Low' else: return 'High' # Conditionally create new column 'B' based on function condition df['B'] = df['A'].apply(condition_func)

  8. Conditional creation of a column in Pandas DataFrame using .apply() with a lambda function

    Description: Learn how to conditionally create a new column in a Pandas DataFrame using .apply() with a lambda function.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Conditionally create new column 'B' using .apply() with lambda function df['B'] = df['A'].apply(lambda x: 'High' if x >= 3 else 'Low')

  9. Creating a new column in Pandas DataFrame based on a complex condition

    Description: Understand how to create a new column in a Pandas DataFrame based on a complex condition involving multiple columns.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]}) # Create new column 'C' based on complex condition involving columns 'A' and 'B' df['C'] = 'Low' df.loc[(df['A'] >= 3) & (df['B'] >= 30), 'C'] = 'High'

  10. Conditional creation of a column in Pandas DataFrame using .apply() with a custom function

    Description: Explore how to conditionally create a new column in a Pandas DataFrame using .apply() with a custom function for complex conditions.

    import pandas as pd # Example DataFrame df = pd.DataFrame({'A': [1, 2, 3, 4, 5]}) # Define custom function for complex condition def complex_condition(x): if x % 2 == 0: return 'Even' else: return 'Odd' # Conditionally create new column 'B' based on complex condition using .apply() with custom function df['B'] = df['A'].apply(complex_condition)

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