Mastering Polars: High-Performance Data Analysis In Python

Published 2/2025
MP4 | Video: h264, 1920×1080 | 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!

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