Handling Missing Data, Aggregations, Folder with Python | Rust Polars Data Frame
Part 6/8 - Handling Missing Data, Aggregations, Folder with Python | Rust Polars Data Frame
About The Ultimate Guide to Data Wrangling with Python | Rust Polars Data Frame
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Author: Amit Shukla
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The aim of this comprehensive user guide is to equip you with all the necessary knowledge and skills required to utilize Python Polars Data Frame effectively for financial and supply chain data science analytics.
It provides an in-depth overview of the most commonly used functions and capabilities of the package.
Table of content
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- Why another DataFrame
- Installation
- Finance and Supply chain Data Analytics
- Creating Polars DataFrame
- Data Types & Casting
- IO
- About Contexts
- selection
- filtering
- group by
- About Expressions
- Functions, User defined functions and Windows function
- Transformation
- Polar SQLs
- Lazy API | Eager execution
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