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Genre: eLearning
Master both libraries side by side with exercises, real-world projects and production-ready patterns
What you’ll learn:
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.
This is a 100% hands-on course that 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
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