Turn messy tables into trustworthy analysis.
Learn DataFrames, selection, filtering, groupby, merge, reshape, time series, and performance with real pandas running in your browser.
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
Acme SaaS, a connected dataset of users, subscriptions, product activity, and business metrics.
Basic Python syntax is helpful. Python Fundamentals provides the full on-ramp.
Open any module to review its exact sections. Progress and completion follow you through the course.
Topics include Why Pandas for Data Science, The Acme SaaS Running Dataset, DataFrame Anatomy, Rows, Columns, Index, and more.
Topics include Column selection, [], dot access, and the gotchas, loc, Label-based access, iloc, Position-based access, and more.
Topics include The split-apply-combine mental model, groupby() with single and multiple keys, agg(), multiple aggregations at once, and more.
Topics include merge(), inner, left, right, outer, on, left_on, right_on, controlling the keys, validate='one_to_many', catching cardinality bugs early, and more.
Topics include Wide format vs long format, same data, two shapes, melt(), flatten wide to long, pivot() and pivot_table(), widen long to wide, and more.
Topics include Parsing dates, pd.to_datetime() and pitfalls, DatetimeIndex, the unlock for resample and rolling, resample(), period bucketization, and more.
Topics include SettingWithCopyWarning, what it really means, Vectorize, don't loop, the 100× speedup, dtypes, when pandas guesses wrong, and more.
Python learners moving into data work
Spreadsheet users adopting reproducible analysis
Analysts preparing for pandas interviews
These are independent LDS courses that extend the closest skills. Choose the direction that matches what you want to build next.
Learn to read, reason about, and write Python.
Build statistical intuition before formulas.
Train models the way working data scientists do.
The first module establishes the language and example used throughout the rest of the course.
