Vectorized Operations – Making Pandas Code Faster

[soniya55531]

Understand how to make your Pandas code faster and more efficient using vectorized operations, an important resource for working with data.

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Vectorized Operations – Making Pandas Code Faster
In today's class, I want to show you how to improve your Pandas code, making it faster and more efficient, using vectorized operations.

The Pandas library is one of the most important in Python when facebook data we are working with data. It offers many features that allow us to increase the efficiency of the code and the speed with which we process data.

I'll show you how to use one of these features, vectorized operations, and demonstrate how they can increase the efficiency and speed of your code.

Are you curious? Do you want to learn how to speed up your Pandas codes and build data analyses much faster and more efficiently? Then download the available material and come with me and I'll show you how!

Database Analysis – Creating the DataFrame
Throughout this class, we will use a database that contains a company's commercial transactions.

In it, we will have information such as the customer ID, the transaction date, the product category, age, gender, the product price and the quantity purchased.

Let's create our DataFrame using Pandas and visualize the information from our database.

Note that this is a relatively small database , with 50 thousand rows. But it will be enough for me to show you the advantages of using vectorized operations with Pandas.

Many people are moving into the data field . Some have never had any contact with programming, while others already have some experience, but in different areas.

In these scenarios, people end up starting by studying Python and, when they come into contact with the data part and the Pandas library, they try to replicate the basic structures they learned with Python within Pandas.

This ends up resulting in code that, in practice, works, but is not as efficient and fast as it could be if it had been written in the most appropriate way.

Therefore, in this class, I will show you three different ways of writing the same code: one using the basic For structure , another using the Pandas Apply function and, finally, using vectorized structures .