![]() |
Mastering Polars: High-Performance Data Analysis In Python - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials) +--- Thread: Mastering Polars: High-Performance Data Analysis In Python (/Thread-Mastering-Polars-High-Performance-Data-Analysis-In-Python) |
Mastering Polars: High-Performance Data Analysis In Python - BaDshaH - 02-11-2025 ![]() 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 |