Unlocking Advanced Data Manipulation- Utilizing Pandas’ apply Function to Return Multiple Columns

Pandas apply return multiple columns is a powerful feature that allows users to apply a function to each row or column of a DataFrame and return multiple columns as the result. This functionality is particularly useful when you need to perform complex transformations or calculations on your data and require additional output columns. In this article, we will explore how to use the apply method in pandas to return multiple columns and discuss its applications in data analysis.

The apply method in pandas is a versatile tool that can be used to apply a function to each element of a DataFrame. By default, the apply method returns a Series, but it can be configured to return multiple columns. This is achieved by using the axis parameter, which determines the axis along which the function is applied. When axis=1, the function is applied to each row, and when axis=0, the function is applied to each column.

To return multiple columns using the apply method, you can use a lambda function or a custom function that returns a list or a Series with the desired number of columns. Let’s consider an example to illustrate this concept.

Suppose we have a DataFrame containing information about a group of students, including their age, gender, and GPA. We want to apply a function that calculates the student’s grade level based on their age and returns both the grade level and the gender as separate columns.

“`python
import pandas as pd

Create a sample DataFrame
data = {‘Age’: [18, 19, 20, 21, 22],
‘Gender’: [‘Male’, ‘Female’, ‘Female’, ‘Male’, ‘Male’],
‘GPA’: [3.5, 3.7, 3.9, 3.2, 3.6]}
df = pd.DataFrame(data)

Define a function that returns multiple columns
def calculate_grade_level(row):
if row[‘Age’] < 20: return ['Freshman', row['Gender']] elif row['Age'] < 21: return ['Sophomore', row['Gender']] elif row['Age'] < 22: return ['Junior', row['Gender']] else: return ['Senior', row['Gender']] Apply the function to each row and return multiple columns df['Grade Level'] = df.apply(calculate_grade_level, axis=1) print(df) ``` In this example, the calculate_grade_level function takes a row as input and returns a list containing the grade level and the gender. The apply method is then used to apply this function to each row of the DataFrame, and the result is assigned to a new column named 'Grade Level'. By using the apply method to return multiple columns, you can easily perform complex transformations and calculations on your data. This feature is particularly useful in data analysis tasks such as feature engineering, data preprocessing, and model building. In the next section, we will discuss some practical applications of pandas apply return multiple columns in data analysis.