How to Apply a Function to Series Elements

Problem Formulation and Solution Overview

As a Python Coder, situations arise where you will need to apply a function against elements of a Series.

To make it more fun, we have the following running scenario:

Rivers Clothing does business with six (6) different countries. The Tax Rates for the associated countries have increased by 2%.

💬 Question: How would we update the Series entries to increase the elements accordingly?

We can accomplish this task by one of the following options:

• Method 1: Use apply() and a lambda
• Method 2: Use List Comprehension
• Method 3: Use a for loop and iat[]
• Method 4: Use update()

Preparation

Before any data manipulation can occur, one (1) new library will require installation.

• The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installation was successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd 

Method 1: Use Apply and a Lambda

You can apply a function to each element of an array by using apply() where you pass an anonymous lambda function. This function executes on each array element.

Here’s an example:

t_rates  = [15, 17, 18, 19, 20, 21]
t_series = pd.Series(t_rates, index=['CA', 'US', 'UK', 'AU', 'DK', 'DE'])
t_series = t_series.apply(lambda x: x*.02+x)
print(t_series)

Above is a list of current Tax Rates (t_rates) for the six (6) countries. To update the Tax Rate, we create a Series (t_series) from the above list by calling the pd.Series() function and passing it two (2) parameters:

• t_rates.
• index (as a List of 2-character Country Codes).

Using apply() and a lambda we increase each Tax Rate by 2% in a single one-liner statement.

The results save back to t_series. As simple as that!

Output

💡 Note: Initially, all Tax Rates were integers. However, once the increase is applied, they automatically convert to float64.

Method 2: Use List Comprehension

List Comprehension uses a single expression to iterate through all list elements. You can apply a particular function to each array element in the expression.

For example, you can use list comprehension to make the appropriate Tax Rate adjustments.

t_rates  = [15, 17, 18, 19, 20, 21]
t_series = pd.Series(t_rates, index=['CA', 'US', 'UK', 'AU', 'DK', 'DE'])
t_series = [x*.02+x for(x) in t_series]
print(t_series)

Above is a list of current Tax Rates (t_rates) for the six (6) countries. To update the Tax Rate, we create a Series (t_series) from the above list by calling the pd.Series() function and passing it two (2) parameters:

• t_rates.
• index (as a List of 2-character Country Codes).

Using List Comprehension and a for loop, we increase each Tax Rate by 2% in a single statement. The results save back to t_series.

Output

Unlike Method 1, the output displays on one line.

Method 3: Use a for loop and iat

Another way to handle the Tax Rate change is to use a for loop and reference the elements using iat[].

The code below accesses each element via the position and updates the Tax Rate by 2% accordingly. The results save back to t_series.

t_rates  = [15, 17, 18, 19, 20, 21]
t_series = pd.Series(t_rates, index=['CA', 'US', 'UK', 'AU', 'DK', 'DE'])
icount = 0
for i in t_series:
    t_series.iat[icount] += t_series.iat[icount]*.02
    icount += 1

This code is not as efficient as other methods, but the output remains the same.

Output

💡 Note: iat[] is similar to iloc[]. However, iat[] returns a single value and therefore executes faster.

Method 4: Use update

The Series update() method modifies the Series data in place. We recommend using this method when only a few elements require adjustments. However, the entire Series could be changed using this method.

Let’s assume two (2) countries decide to increase their Tax Rate by 2%, and the remaining Countries ignore any Tax Rate change.

t_rates  = [15, 17, 18, 19, 20, 21]
t_series = pd.Series(t_rates, index=['CA', 'US', 'UK', 'AU', 'DK', 'DE'])
t_series.update(pd.Series([18.36, 19.38], index=['UK', 'AU']))
print(t_series)

In this code, we pass t_series.update() the pd.Series() a function containing two (2) parameters:

• A list of modified Tax Rates (calculated manually).
• An index containing a list of 2-character Country Codes. This list indicates which Country will have the new Tax Rate applied.

After running this code, the two (2) countries with the 2% tax increase applied (UK & AU) are updated. The results save back to t_series.

Output

💡 Note: Initially, all Tax Rates were integers. However, once the increase is applied, they automatically convert to float64.

Summary

As you can see, there are a few ways to accomplish the same task. It is up to you to decide which method best meets your coding requirements.

Good Luck & Happy Coding!