Why pandas?
pandas is the most widely-used data analytics library in Python. It's free, runs everywhere (including in your browser with our tutorials), and its syntax maps cleanly to the 6 core operations you need for any data analytics task.
Using the Minimal Viable Analytics (MVA) approach, you focus on just 6 skills instead of trying to memorise hundreds of functions. Think of it like building a house — you need a solid foundation (your data), 6 pillars (core skills), and a roof (the decisions you make).
The 6 Pillars — pandas Tutorials
Work through each pillar in order. Every tutorial is interactive — edit the code and run it directly in your browser. No Python installation needed.
Grouping & Aggregation
Group rows by category and summarise with counts, sums, or averages. The backbone of any analysis.
Filtering & Slicing
Select subsets of data based on conditions. Zero in on exactly the rows you need.
Sorting
Order data by one or more columns to find top and bottom values quickly.
Merging
Combine datasets from different sources. Join tables together like pieces of a puzzle.
Creating Columns
Derive new columns with calculations, binning, and encoding. Transform raw data into useful features.
Creating Graphs
Visualise patterns and trends. A good graph makes the data speak for itself.
How to Use These Tutorials
- Start with Pillar 1 (Grouping) — it introduces the dataset and core patterns
- Work through Pillars 2–5 — each builds on the same fundamentals
- Finish with Pillar 6 (Graphs) — bring your analysis to life visually
Each tutorial uses the same Australian petrol station dataset so concepts build naturally. By the end, you'll have the skills to tackle real data analytics problems with confidence.
Every tutorial runs in the browser using Pyodide. You write real Python with real pandas — the same code works if you paste it into Jupyter, VS Code, or any Python environment.
What You'll Learn
groupby(),agg(),reset_index()— aggregate data by category- Boolean indexing,
.loc[],.iloc[]— filter and slice DataFrames sort_values(),nlargest(),nsmallest()— order datapd.merge()with inner, left, right, outer, and anti joinspd.cut(),pd.get_dummies(),apply()— create and transform columnsmatplotlib,seaborn, andDataFrame.plot()— visualise your data
Same Skills, Other Languages
The MVA 6 pillars apply to every data tool. If you prefer a different language, we have the same tutorials for:
Ready to start?
Begin with the first pillar — grouping and aggregation in pandas.
Start Pillar 1: Grouping →