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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
  • Apply and Unique Function Output

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