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Pandas is the best and most popular Python library for machine learning. This library offers a wide variety of functions that will help you manipulate data, optimize your machine-learning algorithm, and much more. This tutorial will help you to get familiar with this library and master the most used functionalities with code samples and video tutorials that will help you to create your first data frame, clean a dataset of information, read CSV files, etc...
The exercises in this tutorial have been created in about 60 hours of development by many experts in machine learning and carefully reviewed by our contributors to make sure you have the most accurate and important information that will help to start your machine learning career.
In this tutorial, we will see the most important and basic functions provided by Pandas that will help you to work with data in machine learning, the following are some of the topics that will be covered in this tutorial.
|Exercise||Description of the topic|
|Install Pandas||These exercises cover how to install Pandas, how to import the Pandas library in a Python file, and how to create your first Python script.|
|DataSets||These exercises explain what datasets are and how to work with them.|
|Series||These exercises explain what series are in Pandas and how to use them.|
|DataFrames||These exercises explain how to create an information data frame and what functions can be used to work with them.|
|Clean DataSets||This class covers what data cleaning is, the functions Pandas offers to clean up a dataset, and the best practices to use when cleaning a dataset.|
There are two ways to initialize this tutorial, the first and easiest is to open the tutorial in a cloud environment such as Codespaces or Gitpod, and the second is to clone this repository in your local environment.
We recommend that you use Codespaces because it is the easiest en fastest way to start the tutorial.
Once you have opened the cloud environment either Codespaces or Gitpod, the LearPack exercises should start automatically. If the exercises do not start automatically you can open a terminal and type the command:
To start this tutorial in your local environment, follow the steps below:
We would like to express our deepest gratitude to the following contributors for their invaluable support in making this tutorial possible.