Python Apply function across a DataFrame

Absolutely! Let's dive into applying functions across a DataFrame in Python, especially using the apply function. This function is a powerful tool in pandas, a popular data manipulation library.
The apply function in pandas is used to apply a function along the axis of a DataFrame. It allows you to transform data in a DataFrame by applying a function to each element, row, or column. This tutorial will walk you through the basics of using apply with a DataFrame.
Make sure you have pandas installed. If not, you can install it using:
Let's start by importing pandas and creating a simple DataFrame:
Suppose you want to square the 'Age' and double the 'Salary'. You can achieve this using apply:
In this example, the transform_column function is applied to each column separately.
To apply a function to each row, set the axis parameter to 1:
In this case, the transform_row function modifies each row individually.
You can also use lambda functions for concise transformations:
The apply function is a versatile tool for applying custom transformations to DataFrames in pandas. Whether you need to operate on columns or rows, it provides a flexible and efficient way to manipulate your data.
Feel free to experiment with different functions and DataFrame structures to get a better grasp of the apply function's capabilities!
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