Use apply() and unique() for sample data set using Python | S-Logix

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How to Use `apply()` and `unique()` for Sample Dataset Using Python

How to Use apply() and unique() for Sample Dataset Using Python

Condition for Using `apply()` and `unique()` in Python

Description:
The apply() function in Pandas is used to apply a function along an axis (rows or columns) of a DataFrame or Series. This is useful when you need to perform a custom operation on each row or column.

The unique() function is used to return the unique values in a DataFrame column or Series. It helps to quickly identify the distinct values present in the data.

Step-by-Step Process

Using apply():
You can use the apply() function to apply any custom function to each row or column of a DataFrame or Series.
Syntax:
DataFrame.apply(func, axis=0) where axis=0 applies the function to columns and axis=1 applies it to rows.
Using unique():
The unique() function returns an array of unique values in a particular column or Series.
Syntax:
DataFrame['column_name'].unique()

Sample Source Code

import pandas as pd

data = {
'Employee ID': [1, 2, 3, 4, 5],
'Department': ['HR', 'IT', 'IT', 'HR', 'Finance'],
'Age': [25, 30, 28, 35, 40]
}

df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# 1. Using apply() to create a new column based on a custom function
df['Age Category'] = df['Age'].apply(lambda x: 'Young' if x < 30 else 'Adult')

print("\nDataFrame after using apply() to create Age Category:")
print(df)

# 2. Using unique() to find the unique departments
unique_departments = df['Department'].unique()

print("\nUnique Departments:")
print(unique_departments)

Screenshots

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