Udemy - Mastering Polars - Fast Data Processing and Big Data Analysis

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Udemy - Mastering Polars - Fast Data Processing and Big Data Analysis (Size: 552.1 MB)
  1 -Here is the link to access the dataset on GitHub, used in the project.url 102.4 B
  1 -Introduction to Polars Why It’s Faster and How It Differs from Pandas.mp4 48.4 MB
  1 -Introduction.mp4 23.5 MB
  1 -The second CSV file used in the project is downloaded from Kaggle.url 102.4 B
  2 -Polars Installation, DataFrame Loading, and Efficient Column Access.mp4 49.3 MB
  3 -Mastering Polars DataFrames Slicing, Stats, and Data Exploration.mp4 66.5 MB
  4 -Data Manipulation in Polars Arithmetic Operations, Column Management, Filtering.mp4 52.4 MB
  5 -Polars DataFrame Methods Flags, Schema, Column Operations, and Conversion.mp4 63.8 MB
  6 -Advanced Data Manipulation Grouping, Aggregation, Sorting, and Transformation.mp4 41 MB
  7 -Advanced Polars Operations write_csv, Pivot Tables, and Join Strategies.mp4 55.2 MB
  8 -Eager vs Lazy Execution in Polars Speed Comparison with Pandas.mp4 121.8 MB
  9 -Data Visualization in Polars. Benefits, Limitations, and Comparison.mp4 30.2 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B

Description


Mastering Polars: Fast Data Processing & Big Data Analysis

https://WebToolTip.com

Published 3/2025
Created by Olga Piliaieva
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 1h 15m ) | Size: 552 MB

Master Polars for Fast Data Manipulation: Work with Large Datasets, Lazy Execution, Performance Optimization, and More

What you'll learn
Master Data Manipulation – Learn to filter, group, and transform data efficiently using Polars' powerful functions.
Optimize Performance – Use lazy evaluation and parallel execution to handle large datasets faster.
Compare Polars and Pandas – Understand key differences to choose the best tool for your data tasks.
Process Large Files in Chunks – Load, process, and aggregate large datasets efficiently without running out of memory.

Requirements
A computer with Python installed.
Jupyter Notebook for running and testing code.
Polars and Pandas libraries must be installed.
Basic familiarity with Python is helpful but not required - beginners are welcome!

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