Pandas Tutorial with Examples.
Pandas tutorial for data analysis: DataFrames, groupby, merging, time series and cleaning pipelines, each step shown with its output.
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.
Introduction to Pandas
Build your first Pandas DataFrame and Series, load a CSV with real options, and learn why info() prints None and how index alignment creates NaN.
Pandas DataFrames and Series
Select columns, pick rows with loc and iloc, filter with boolean masks and query(), add computed columns, and sort — every Pandas example run for real.
Pandas Data Cleaning
Fill missing values, fix text-typed numbers and dates, drop duplicates, and flag outliers in Pandas 3, including the inplace fillna that silently fails.
Pandas Projects
Real-world data analysis projects that build pandas fluency — from data cleaning pipelines to production ETL systems.
Intermediate
For developers with core concepts down
Chained Assignment and the Copy-vs-View Trap
Why a filtered Pandas DataFrame is a copy, not a view, and how chained assignment can silently fail to update your data — with the real fix.
Pandas Aggregation and GroupBy
Group Pandas data with groupby, named aggregations, transform, pivot tables, and rolling windows — plus the NaN group keys that groupby silently drops.
Pandas Joins and Merges
Combine DataFrames with merge, join, and concat: all four join types, the duplicate-key fan-out that inflates totals, and validate= to catch it.
MultiIndex: Hierarchical Indexing in Pandas
Select, slice, and reshape a Pandas DataFrame indexed by more than one level with .loc and .xs, and fix the UnsortedIndexError almost everyone hits first.
Pandas Time Series
Parse dates, slice by date strings, resample, fill gaps in a time grid, and handle time zones and DST in Pandas, with every output from a real run.
Pandas Data Transformation
Reshape Pandas data with melt, pivot, stack, and explode, transform values with map, apply, and .str, and measure how much slower row-wise apply really is.
Advanced
Production-grade patterns for experienced engineers
Pandas Performance Optimization
Shrink Pandas memory with category and downcast dtypes, replace iterrows with vectorized code, read CSVs in chunks, and time Pandas against Polars.
Advanced Pandas Operations
Write Pandas method chains with assign, query, and pipe, compare rolling and exponential windows, and measure whether eval() is faster on your machine.