Turn messy tables into trustworthy analysis.
Learn DataFrames, selection, filtering, groupby, merge, reshape, time series, and performance with real pandas running in your browser.
What you will leave with
You can take raw tabular data through a clear, reproducible pandas workflow and explain every transformation along the way.
What you will be able to do
The curriculum is organized around these 4 practical outcomes.
Build and inspect DataFrames confidently
Select, filter, and clean rows without silent mistakes
Aggregate and combine related tables
Reshape and analyze time-based data efficiently
Course map
Curriculum
Open a module to inspect every section before you start. Your progress follows you through the course.
Why Pandas for Data Science, The Acme SaaS Running Dataset, DataFrame Anatomy, Rows, Columns, Index, and more.
Column selection, [], dot access, and the gotchas, loc, Label-based access, iloc, Position-based access, and more.
The split-apply-combine mental model, groupby() with single and multiple keys, agg(), multiple aggregations at once, and more.
merge(), inner, left, right, outer, on, left_on, right_on, controlling the keys, validate='one_to_many', catching cardinality bugs early, and more.
Wide format vs long format, same data, two shapes, melt(), flatten wide to long, pivot() and pivot_table(), widen long to wide, and more.
Parsing dates, pd.to_datetime() and pitfalls, DatetimeIndex, the unlock for resample and rolling, resample(), period bucketization, and more.
SettingWithCopyWarning, what it really means, Vectorize, don't loop, the 100× speedup, dtypes, when pandas guesses wrong, and more.
Who this course is for
Start with the background you have. The prerequisite notes above tell you exactly what is assumed.
Python learners moving into data work
Spreadsheet users adopting reproducible analysis
Analysts preparing for pandas interviews
Continue learning
Each course stands on its own. Pick the one closest to the next task you need to handle.
Learn to read, reason about, and write Python.
Build statistical intuition before formulas.
Train models the way working data scientists do.
Start the course
Module 1 introduces the language and example used throughout the rest of the course.
