Polars Essentials
What you will learn
Learn to efficiently manipulate large datasets, perform complex operations, and optimize your data workflows. From basic DataFrame creation to advanced techniques like lazy evaluation, you'll gain practical skills to handle real-world data challenges in this hands-on training. Whether you're a data scientist, analyst, or developer, this course will equip you with the tools to improve data processing capabilities and migrate workflows from pandas to Polars.
Audience
Prior experience in Python is required. This course is well-suited to anyone learning the basics of Polars.
Learning outcomes
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Learn about key concepts and best practices in Polars
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Use advanced techniques to efficiently manipulate large datasets
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Efficiently process time series data with Polars
Module 1
Polars basics
- Data structures
- Learn about the core data structures in Polars: DataFrames and Series.
- Input / Output
- Master various ways of reading and writing data in Polars, such as constructing DataFrames from scratch and importing data from various sources.
- Data manipulation
- Explore Polars' powerful and expressive API, including filtering, joining and transforming DataFrames to efficiently handle large-scale datasets.
Module 2
High performance data analysis
- Performance
- Learn about the concepts that make Polars so performant and how to use them correctly: lazy evaluation, multithreading, and streaming and out-of-core algorithms.
- Profile and debug your query to understand the performance bottlenecks
- Ecosystem
- Discover how Polars integrates with other data science tools and libraries in the ecosystem using Apache Arrow.
Module 3
Analyzing time series data
- Datetime operations
- Use robust datetime functionalities to efficiently create and manage time series data for temporal analyses.
- Time series analysis
- Apply specialized time series features to conduct advanced analyses, including resampling, rolling operations and more.