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LearnPandas Fundamentals

Pandas Fundamentals
for Data Science.

Master pandas the right way — hands-on with real datasets, not toy examples. From DataFrames to time series to the performance patterns that separate hobbyists from professionals.

Course Overview

Modules

7

Duration

~5 hours

Interactive AnimationsLive Pyodide PlaygroundReal B2B SaaS DatasetVectorization Mastery

Learning Modules

Each module combines animated explanations, hands-on practice, and real-world applications. Run pandas directly in your browser against the Acme SaaS B2B dataset.

DataFrames & Series — The Building Blocks

The two pandas primitives — DataFrames and Series. Build them, inspect them, and reason about copies vs views with the Acme SaaS B2B running dataset.

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

Selecting & Filtering — loc, iloc, query

Master row and column selection with loc, iloc, boolean masks, and query(). Pick the right tool for every situation.

Sections

1Column Selection — The Three Ways

2loc — Label-Based Selection

3iloc — Position-Based Selection

4Boolean Masks — Filtering by Condition

5query() — Readable Filter Syntax

Aggregation & GroupBy — Split-Apply-Combine

Internalize split-apply-combine. Once it's in your bones, 80% of pandas analysis problems become trivial.

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

Joining DataFrames — merge with validate

Combine DataFrames with merge, concat, and join. Use validate to catch cardinality bugs the moment they happen.

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

Reshaping Data — Wide ⇄ Long

Toggle between wide and long format with melt, pivot, and pivot_table. Same data, two shapes.

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

Time Series Essentials

DatetimeIndex unlocks pandas' time-series superpowers — resample, rolling, and aligned arithmetic.

Sections

1Parsing Dates — to_datetime() and Pitfalls

2DatetimeIndex — The Time-Aware Index

3resample() — Bucketizing by Period

4rolling() — Windows Over Time

5Timezones — Naive vs Aware

Common Pitfalls & Performance

The bugs every pandas user hits — and the fixes that turn 100-second scripts into 1-second scripts.

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

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