DataFrame.apply() - Pandas DataFrame Basics

The DataFrame.apply() method in Pandas is a powerful function that allows you to apply a function to every row or column of a DataFrame. It takes a function as an argument and applies it to every value in the DataFrame, returning a new DataFrame with the results.

Syntax

The syntax for using DataFrame.apply() is:

DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwds)

• func: The function to apply to the DataFrame or Series.
• axis: Specifies whether to apply the function along the rows (axis=0) or columns (axis=1) of the DataFrame.
• raw: If True, the function is applied to the underlying NumPy array in the DataFrame, instead of the default pandas-aligned axis labels.
• result_type: The type of the resulting DataFrame.
• args: Arguments to pass to func.
• **kwds: Keyword arguments to pass to func.

Example

Let's create a sample DataFrame first:

import pandas as pd

data = {'name':['John', 'Jane', 'Steve', 'Bill'], 
        'age':[28, 34, 29, 42], 
        'gender':['Male', 'Female', 'Male', 'Male']}
df = pd.DataFrame(data)
print(df)

This creates a DataFrame with three columns (name, age, and gender) and four rows:

    name  age  gender
0   John   28    Male
1   Jane   34  Female
2  Steve   29    Male
3   Bill   42    Male

Now, let's apply a function to the age column to add 2 to each value:

def add_two(x):
    return x + 2

df['age'] = df['age'].apply(add_two)
print(df)

The output should be:

    name  age  gender
0   John   30    Male
1   Jane   36  Female
2  Steve   31    Male
3   Bill   44    Male

In this example, we define a function add_two that takes a single argument and returns the argument plus 2. We then use apply() to apply this function to every element of the age column. The resulting DataFrame has each age in the age column increased by 2.

Output

The output of our example will be:

    name  age  gender
0   John   30    Male
1   Jane   36  Female
2  Steve   31    Male
3   Bill   44    Male

Explanation

The apply() method applies the given function to every element in the specified axis and returns a new DataFrame with the results. In the example, we pass the function add_two to the apply() method, which takes each value in the age column, adds 2 to it, and returns a new DataFrame with the resulting values.

Use

The apply() method is commonly used to apply a function to every element in a DataFrame or Series. This can be useful for transforming or cleaning data, as well as for creating new columns based on existing data.

Important Points

• The apply() method allows you to apply a function to every element in a DataFrame or Series.
• The axis argument specifies whether to apply the function along the rows (axis=0) or columns (axis=1) of the DataFrame.
• The apply() method returns a new DataFrame with the results of the applied function.

Summary

In summary, the apply() method in Pandas is a powerful function that allows you to apply a function to every element in a DataFrame or Series. It provides a convenient way to transform data or create new columns based on existing data.