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Pandas or Polars? Which Python library is right for you?

Data scientists require DataFrame libraries for their projects that are efficient, flexible, compatible with various data formats, and easy to use. In this project, we compare the performance of two popular Pandas and Polars in Python.

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Guided Project

Data Science

84 Enrolled
4.7
(14 Reviews)

At a Glance

Data scientists require DataFrame libraries for their projects that are efficient, flexible, compatible with various data formats, and easy to use. In this project, we compare the performance of two popular Pandas and Polars in Python.

In this project, we will examine the strengths and limitations of Pandas and Polars, as well as the scenarios in which they are most useful.


The project will provide insights into the different use cases for each library and highlight their strengths and weaknesses. Ultimately, this project will help data scientists, engineers, and analysts determine which library is the best fit for their specific data visualization and analysis needs.

A Look at the Project Ahead


In this project,  we will do the following content to compare Polars and Panda:
  • Provide an overview of the basic functions of Pandas and Polars
  • Demonstrate how they can be used to visualize and analyze different types of data
  • Compare the features and performance of Polars and Pandas when working with large datasets
  • Explore how they can be combined to effectively preprocess and visualize data.
If you are looking to conduct a real project in Jupyter notebook or continue to improve your skills in data science, give the IBM Skill Network Labs a try at no charge!

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What You'll Need


This is a very simple project, beginners are welcome. We are using Python code in the Jupyter notebook in this project.
Remember that the IBM Skills Network Labs environment comes with many things pre-installed (e.g. Docker) to save you the hassle of setting everything up. Also note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Level

Beginner

Industries

Information Technology

Skills You Will Learn

Data Analysis, Data Science, Machine Learning, Python

Language

English

Course Code

GPXX0BM6EN

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