Mastering Polars: Fast Data Processing & Analysis of Big Data

Ready to accelerate your data analysis? In this course, you'll learn about Polars, a high-performance and fast library that is meant to work efficiently with large datasets. If you've encountered performance issues with Pandas or need to accelerate your work with big data, this course is ideal for you. I'll take you through it all - from the basics of loading and working with data to advanced methods such as lazy evaluation and chunk processing. You'll see how to contrast Polars with Pandas in real-world situations, and why Polars may be the breakthrough tool you've been looking for. By the end of this course, you’ll be able to process millions of rows in a fraction of the time it takes with traditional methods. Join me and learn how to optimize your workflow and elevate your data analysis skills.
In this course, we have real-world datasets several gigabytes in size and more than 100 million rows in number. These large CSV files can be downloaded from the links below to work through the examples in this course.

Here is the link to download the dataset from GitHub, utilized in the project:
======> git hub Data csv
The second CSV file utilized in the project is obtained from Kaggle. Following is the link:
======> kaggle Data

You will learn:
Introduction to Polars: Why It's Faster and How It Differs from Pandas
Polars Installation, Data Frame Loading, and Efficient Column Access
Data Manipulation in Polars: Arithmetic Operations, Column Management, Filtering
Mastering Polars Data Frames: Slicing, Stats, and Data Exploration
Polars Data Frame Methods: Flags, Schema, Column Operations, and Conversion
Advanced Data Manipulation: Grouping, Aggregation, Sorting, and Transformation
Advanced Polars Operations: write csv, Pivot Tables, and Join Strategies
Eager vs Lazy Execution in Polars: Speed Comparison with Pandas
Data Visualization in Polars. Benefits, Limitations, and Comparison.