Pandas Vs Polars - The Complete Hands-On Data Course
Published 5/2026
Created by Dario Festa
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 64 Lectures ( 34m ) | Size: 139 MB
⚡ Load, inspect, and export data in CSV, Parquet, and JSON formats using both Pandas and Polars.
⚡ Handle missing data, clean dirty datasets, aggregate data with groupby, pivot tables, and cross-tabulations.
⚡ Apply lazy evaluation and streaming to process datasets larger than RAM and write method-chained pipelines
⚡ Join multiple datasets (inner, left, outer, cross, anti, semi joins).
⚡ Use window functions, rolling calculations, and time-based resampling.
⚡ Visualize results with matplotlib and seaborn.
⚡ Build complete ETL pipelines from extraction to export.
Requirements
❗ Basic Python knowledge (variables, loops, functions, lists, dictionaries)
❗ Familiarity with the command line (installing packages with pip)
❗ No prior experience with Pandas or Polars is required - we start from zero
Description
This course contains the use of artificial intelligence.
This is a
100% hands-on coursethat teaches you real-world data manipulation
through direct side-by-side comparison of Pandas and Polars - the two most important Python DataFrame libraries today.
Every single exercise gives you the same task solved in BOTH libraries, so you can immediately see differences in API design, performance, and idiomatic style.
No long lectures. No slides. Just clear assignments, clean solutions, and reference material you can use on the job.
The course is structured in 5 progressive blocks
1. Fundamentals
Loading CSVs, selecting columns, filtering rows, data types, string operations, date/time handling, null management, and export to multiple formats.
2. Aggregations & Joins
GroupBy operations, pivot/melt, all types of joins, window functions, rolling aggregations, resampling, and ranking.
3. Advanced Patterns
Lazy evaluation, method chaining, user-defined functions, nested/struct data, multi-format interop, and performance optimization.
4. Extras
Categorical types & memory optimization, large file streaming, data visualization with matplotlib and seaborn, multi-file ingestion (glob patterns, partitioned datasets), and data validation pipelines
5. Real world Projects
- Data Cleaning & Normalization
- Sales Dashboard Report
- ETL Pipeline (multi-source)
- Customer Churn & Cohort Analysis
- Financial Risk & Portfolio Optimization
- ML Feature Engineering Pipeline
- Real-time Streaming Dashboard (2M+ rows)
Every project includes both a Pandas and a Polars solution so you always have two perspectives on the same problem.
Who this course is for
⭐ Anyone who wants a practical, no-nonsense reference for both libraries
⭐ Python developers who want to add data manipulation to their skillset
⭐ Data analysts transitioning from Excel/SQL to Python-based workflows
⭐ Data scientists who know Pandas but want to learn Polars (or vice versa)
⭐ Students and self-learners preparing for data engineering interviews
Pandas Vs Polars - The Complete Hands-On Data Course
