Pandas.
Data manipulation and analysis with Pandas — DataFrames, groupby, merging, time series, and cleaning pipelines.
Beginner
Start here — no prior experience needed
Pandas Learning Roadmap
A structured path from pandas beginner to production data engineer — with milestones, time estimates, and what to build at each stage.
Pandas DataFrames and Series
Master selection, filtering, column operations, and the core DataFrame API for daily data manipulation work.
Pandas Data Cleaning
Handle missing values, fix data types, remove duplicates, and standardize messy real-world data with Pandas.
Pandas Projects
Real-world data analysis projects that build pandas fluency — from data cleaning pipelines to production ETL systems.
Introduction to Pandas
Learn what Pandas is, when to use it, and build your first DataFrame from scratch.
Intermediate
For developers with core concepts down
Pandas Aggregation and GroupBy
Group data, compute aggregations, use pivot tables, and apply multi-level aggregation patterns for real-world analytics.
Pandas Joins and Merges
Combine DataFrames with merge, join, and concat — covering inner, outer, left, right joins, and handling key conflicts.
Pandas Time Series
Parse dates, resample time series, compute rolling statistics, and handle time zones for temporal data analysis.
Pandas Data Transformation
Reshape data with melt, pivot, stack, and unstack — and apply custom transformations using apply and map.
Advanced
Production-grade patterns for experienced engineers
Pandas Performance Optimization
Speed up pandas operations 10-100x — vectorization, chunked processing, efficient dtypes, and when to switch to Polars or DuckDB.
Advanced Pandas Operations
Master method chaining, window functions, MultiIndex, pipe(), and production-ready DataFrame patterns.