# How to Apply a Function to List Elements

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## Problem Formulation and Solution Overview

As a Pythonista, coding issues may occur where you need to apply a function against array/matrix elements.

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

The organization Happy Mortgages has six (6) different Mortgage Terms available: `30-Year`, `20-Year`, `15-Year`, `10-Year`, `7-Year`, and `5-Year` terms.

The US Federal Reserve has decided to increase the Mortgage Rate by 1.23%.

💬 Question: How would we update the Array/Matrix entries to increase the matrix/array elements accordingly?

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

Consider the following related tutorial if you want to apply a function to column elements instead of the matrix or array.

Related Tutorial: How to Apply a Function to Column Elements?

## Preparation

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

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.

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 List Comprehension

List Comprehension offers a single-line expression to change all the Mortgage Rates in one fell swoop!

```m_terms = [30, 20, 15, 10, 7, 5]
m_rates = [4.6, 4.3, 3.6, 4.7, 3.8, 3.9]
m_rates = [round(x*.0123+x, 3) for x in m_rates]
print(m_rates)```

Above is a list of Mortgage Terms (`m_terms`) available for the six (6) existing Mortgage Rates (`m_rates`).

In our code, List Comprehension loops through `m_rates` applying the Mortgage Rate increase to each element accordingly. The `round()` method trims the decimal places to three (3). The results save back to `m_rates`.

Output

## Method 2: Use Map and a Lambda

This method is a bit more complex than Method 1. Here we use the `map()` and lambda functions to accomplish the same task.

```m_terms = [30, 20, 15, 10, 7, 5]
m_rates = [4.6, 4.3, 3.6, 4.7, 3.8, 3.9]
m_rates = list(map(lambda x : round(x*.0123+x, 3), m_rates))
print(m_rates)```

In this code, we loop through `m_rates` using `map()` and passing a `lambda` as a parameter. The Mortgage Rate increases using the `lambda` to adust each element accordingly.

The `round()` method trims the decimal places to three (3). The results save back to `m_rates` as a list.

Output

## Method 3: Use a For Loop and enumerate()

The `for` loop is initiated with an index (counter) and an item (element value) for `m_rates`. This variable is wrapped inside `enumerate()` as an iterable.

```m_terms = [30, 20, 15, 10, 7, 5]
m_rates = [4.6, 4.3, 3.6, 4.7, 3.8, 3.9]

for index, item in enumerate(m_rates):
m_rates[index] = round(m_rates[index]*.0123+m_rates[index], 3)

print (m_rates)```

This code loops through `m_rates` and applies the Mortgage Rate increase to each element.

The `round()` method trims the decimal places to three (3). Each element saves accordingly.

In case you need a quick refresher on the `enumerate()` function, have a look at this video tutorial:

Output

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