A module-by-module concept outline. Open the course to learn each topic with animated explanations, in-browser code, practice challenges, and a knowledge check.

Module 1. DataFrames & Series — The Building Blocks

Topics

Why Pandas for Data ScienceThe Acme SaaS Running DatasetDataFrame Anatomy — Rows, Columns, IndexSeries — The Underlying Building BlockInspecting Data — head, info, describe

Sections

1Why Pandas & The Acme SaaS Dataset
2DataFrame Anatomy — Rows, Columns, Index
3Series — The Underlying Building Block
4Inspecting Data — head, info, describe
5Mutability vs. Copies — Avoiding the Common Trap

Module 2. Selecting & Filtering — loc, iloc, query

Topics

Column selection — [], dot access, and the gotchasloc — Label-based accessiloc — Position-based accessBoolean masks — vectorized filteringquery() — readable, expressive filters

Sections

1Column Selection — The Three Ways
2loc — Label-Based Selection
3iloc — Position-Based Selection
4Boolean Masks — Filtering by Condition
5query() — Readable Filter Syntax

Module 3. Aggregation & GroupBy — Split-Apply-Combine

Topics

The split-apply-combine mental modelgroupby() with single and multiple keysagg() — multiple aggregations at oncetransform() vs aggregate() vs apply()Named aggregations — readable result columns

Sections

1The Split-Apply-Combine Mental Model
2groupby() — Single and Multi-Key Grouping
3agg() — Multiple Aggregations at Once
4transform vs aggregate vs apply
5Named Aggregations & Result Hygiene

Module 4. Joining DataFrames — merge with validate

Topics

merge() — inner, left, right, outeron, left_on, right_on — controlling the keysvalidate='one_to_many' — catching cardinality bugs earlyconcat() — stacking vs aligning framesjoin() — index-based merging

Sections

1merge() — The Four How Modes
2Controlling Keys — on, left_on, right_on
3Cardinality — validate Saves Lives
4concat() — Stacking and Aligning
5join() — Merging by Index

Module 5. Reshaping Data — Wide ⇄ Long

Topics

Wide format vs long format — same data, two shapesmelt() — flatten wide to longpivot() and pivot_table() — widen long to widestack() and unstack() — toggle layers of a MultiIndexWhen to use which — practical guidance

Sections

1Wide vs Long — Two Shapes, Same Data
2melt() — Flattening Wide to Long
3pivot() and pivot_table() — Widening to Wide
4stack() and unstack() — MultiIndex Toggles
5Choosing the Right Shape for the Job

Module 6. Time Series Essentials

Topics

Parsing dates — pd.to_datetime() and pitfallsDatetimeIndex — the unlock for resample and rollingresample() — period bucketizationrolling() — windows over timeTimezones — naive vs aware, the conversion pattern

Sections

1Parsing Dates — to_datetime() and Pitfalls
2DatetimeIndex — The Time-Aware Index
3resample() — Bucketizing by Period
4rolling() — Windows Over Time
5Timezones — Naive vs Aware

Module 7. Common Pitfalls & Performance

Topics

SettingWithCopyWarning — what it really meansVectorize, don't loop — the 100× speedupdtypes — when pandas guesses wrongMissing data — NaN, None, and the type promotion trapProfiling — finding the slow line

Sections

1SettingWithCopyWarning — Understanding & Fixing
2Vectorization — Why apply() Is Often Wrong
3dtypes — When Pandas Guesses Wrong
4Missing Data — NaN, None, and Promotions
5Profiling — Finding the Slow Line

Pandas Fundamentals

What this course covers

Module 1. DataFrames & Series — The Building Blocks

Module 2. Selecting & Filtering — loc, iloc, query

Module 3. Aggregation & GroupBy — Split-Apply-Combine

Module 4. Joining DataFrames — merge with validate

Module 5. Reshaping Data — Wide ⇄ Long

Module 6. Time Series Essentials

Module 7. Common Pitfalls & Performance

Ready to start Pandas Fundamentals?

All course syllabi