Applying Functions in Pandas

In real-world data analysis, you often need to transform, calculate, or customize values inside a DataFrame.

Pandas provides powerful ways to apply functions to rows, columns, and individual values. In this lesson, you will learn how to use apply(), applymap(), and map().

Loading the Dataset

We continue using the same dataset throughout this course.

import pandas as pd

df = pd.read_csv("dataplexa_pandas_sales.csv")

Why Applying Functions Is Important

Applying functions allows you to:

• Perform calculations on columns
• Create derived values
• Standardize or clean data
• Apply business logic to datasets

Applying a Function to a Column

Suppose we want to calculate a 10% tax on the total_amount column.

df["tax"] = df["total_amount"].apply(lambda x: x * 0.10)

This creates a new column where each value is 10% of the total amount.

Using apply() on Multiple Columns

You can also apply a function across rows. For example, calculating final price including tax.

df["final_price"] = df.apply(
    lambda row: row["total_amount"] + row["tax"],
    axis=1
)

Here:

• axis=1 means row-wise operation
• Each row is passed to the function

Creating Custom Functions

Instead of lambda functions, you can define your own reusable functions.

def discount(amount):
    if amount > 500:
        return amount * 0.90
    else:
        return amount

df["discounted_price"] = df["total_amount"].apply(discount)

This applies a 10% discount only for orders greater than 500.

Applying Functions to All Values

If you want to apply a function to every value in the DataFrame, use applymap().

df_numeric = df.select_dtypes(include="number")
df_numeric.applymap(lambda x: round(x, 2))

This rounds all numeric values to two decimals.

Using map() for Value Mapping

The map() function is useful for replacing values based on a mapping.

Example: converting payment types into readable labels.

payment_map = {
    "CC": "Credit Card",
    "DC": "Debit Card",
    "COD": "Cash on Delivery"
}

df["payment_method"] = df["payment_code"].map(payment_map)

Handling Missing Values During apply()

Always handle missing values to avoid errors.

df["safe_total"] = df["total_amount"].apply(
    lambda x: 0 if pd.isna(x) else x
)

Performance Considerations

• apply() is flexible but slower
• Vectorized operations are faster when possible
• Use apply only when logic cannot be vectorized

Practice Exercise

Using the dataset:

• Create a new column with 5% tax
• Apply a discount for large orders
• Round numeric values
• Map coded values to readable labels

What’s Next?

In the next lesson, you will learn how to perform string operations for cleaning and transforming text data.