Mastering Polars: High-Performance Data Analysis In Python

[BaDshaH]

Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.04 GB | Duration: 5h 42m

Supercharge Your Data Processing with Polars - The Fastest Alternative to Pandas!

What you'll learn
Working with larger-than-memory data
Pandas Vs Polars over billion data
Taking advantage of parallel and optimised analysis with Polars
Using Polars expressions for analysis that is easy to read and write
Learn strategies to optimize memory usage and processing speed when dealing with massive datasets.
Combining data from different datasets using fast joins operations
Load data from various sources, including web-based files, CSV, JSON, and Parquet files.

Requirements
No prior experience is required! This course is designed for beginners, Basic knowledge of Python is good to have, and I'll guide you step by step. All you need is a computer with an internet connection and a willingness to learn."

Description
Unlock the power of Polars, the next-generation DataFrame library designed for speed, scalability, and efficiency. Whether you're a data scientist, analyst, or engineer, this course will teach you how to leverage Polars to process and analyze large datasets faster than traditional tools like Pandas.Through hands-on projects and real-world datasets, you'll gain a deep understanding of Polars' capabilities, from basic operations to advanced data transformations. By the end of this course, you'll be able to replace Pandas with Polars for high-performance data workflows.In this course, you'll master Polars from scratch-learning how to efficiently manipulate, analyze, and transform large datasets with ease. Whether you're dealing with millions of rows or complex queries, Polars' multi-threaded and lazy execution will supercharge your workflows.What You'll LearnPolars vs. Pandas - Why Polars is faster and how it works under the hoodPolars DataFrames & LazyFrames - Understanding efficient data structuresFiltering, Sorting, and Aggregations - Perform operations at blazing speedGroupBy and Joins - Handle complex data transformations seamlesslyTime Series & String Operations - Work with dates, timestamps, and text dataI/O Operations - Read and write CSV, Parquet, JSON, and morePolars Expressions & SQL-like Queries - Unlock powerful data processing techniquesParallel Processing & Lazy Evaluation - Optimize performance for large datasetsWho This Course Is ForPython users working with large datasetsData analysts & scientists looking for faster alternatives to pandasEngineers working with Big Data or ETL pipelinesAnyone who wants to future-proof their data skills with a high-performance libraryWhy Learn Polars?Blazing-fast performance - 10-100x faster than pandas in many casesBuilt for modern CPUs - Uses multi-threading and Rust-based optimizationsMemory-efficient - Works well even with limited RAMIdeal for Big Data & ETL - Perfect for processing large-scale datasetsBy the end of this course, you'll be confidently using Polars for real-world data analysis, optimizing your workflows, and handling massive datasets like a pro.

Overview
Section 1: Introduction
Lecture 1 Course Overview
Lecture 2 Introduction of Polars
Lecture 3 Pandas Vs. Polars
Lecture 4 Course Materials
Section 2: Polars Quckstart
Lecture 5 Mac: Installation of Python and Polars Library
Lecture 6 Apache Arrow & Polars: Overview
Section 3: Data Frames
Lecture 7 Create Data Frame using Multiple Methods
Lecture 8 Series and Data Frame Objects
Lecture 9 Conversion from Pandas or Numpy
Section 4: Play with Files
Lecture 10 Read Files using Polars
Lecture 11 Read JSON Files using Polars
Lecture 12 Write Files using Polars
Section 5: Select Columns
Lecture 13 Select Column
Lecture 14 Select 2 Columns
Lecture 15 Select Multiple Columns
Section 6: Columns Transformation
Lecture 16 Add Column: Using Constant Value
Lecture 17 Add Column: Multiple Columns at Once
Lecture 18 Transform Data Frame
Lecture 19 Iterating Data Frame
Section 7: Aggregate Functions, and Distinct
Lecture 20 Aggregate Functions
Lecture 21 Distinct Queries
Section 8: Filters or Where Clause
Lecture 22 Python Way: Square Brackets
Lecture 23 Integer Columns
Lecture 24 String Columns
Lecture 25 Date Columns
Lecture 26 Boolean Columns
Section 9: Group By, Case, and Sorting
Lecture 27 Group By Examples
Lecture 28 Group By with Having
Lecture 29 Iterating on Group By Object
Lecture 30 Case Conditions
Lecture 31 Quantiles & Histogram
Lecture 32 Sorting
Section 10: Handling Missing Values
Lecture 33 Finding Missing Values
Lecture 34 Replace Missing Values
Section 11: Concatenating & Joins
Lecture 35 Vertically & Horizontal Concatenating Data Frames
Lecture 36 Join Examples
Section 12: Database
Lecture 37 Polars with Sqlite & Postgres
Section 13: 1+ Billion Records Test
Lecture 38 Overview of New York Taxi Data
Lecture 39 Billion Records Test: Select
Lecture 40 Billion Records Test: Aggregate Functions
Lecture 41 Billion Records Test: Distinct Queries
Lecture 42 Billion Records Test: Case, When & Otherwise
Lecture 43 Billion Records Test: Filters
Lecture 44 Billion Records Test: Group By
Lecture 45 Billion Records Test: Handling Missing Data
Lecture 46 Billion Records Test: Slicing in Polars
Section 14: Pandas Vs Polars: On 1+ Billion Records
Lecture 47 Pandas Vs. Polars: Select
Lecture 48 Pandas Vs. Polars: Aggregate Functions
Lecture 49 Pandas Vs. Polars: Distinct
Lecture 50 Pandas Vs. Polars: Filters
Lecture 51 Pandas Vs. Polars: Group By
This course is perfect for beginners who want to learn Polars from scratch. Whether you're a student, a working professional, or simply curious about Polars, this course will provide you with a solid foundation. No prior experience is required!

Homepage

[To see links please register or login]

[To see links please register or login]