Python lessons
pandas for data work: loading, cleaning, reshaping and plotting. 21 lessons across 5 tracks, each ending with a short self-check.
21lessons
119mtotal
5tracks
42checks
Python basics for data
Just enough language to be productive with data.
Setup and Jupyter notebooksGetting an environment running and knowing how notebooks actually execute.Variables and data typesStrings, numbers, booleans, and the type errors that catch people out.Lists and dictionariesThe two structures you actually need, and how they map to DataFrames.Conditions and loopsif/elif/else and for loops — and why you will use them less than you expect.
Working with DataFrames
Loading, inspecting, selecting and filtering.
Loading dataread_csv and read_excel, and the arguments that save you an hour.Inspecting a DataFramehead, info, describe and shape — the four commands to run every time.Selecting and renaming columnsPicking columns, dropping them, and renaming without breaking anything.Filtering rowsBoolean masks, combining conditions, and loc versus iloc.Creating and modifying columnsCalculated columns, conditional values, and the SettingWithCopyWarning.
Wrangling & cleaning
Missing values, duplicates, text and types.
Handling missing valuesFinding NaN, deciding what to do about it, and what the choice costs.Removing duplicatesFinding duplicate rows, choosing which to keep, and deduplicating on a key.Text operationsThe .str accessor, and the cleaning steps worth doing on every import.Converting data typesastype, to_datetime, to_numeric, and handling values that will not convert.
Aggregation & reshaping
groupby, sorting, merging and pivoting.
Grouping and aggregatinggroupby, agg, and getting a clean DataFrame back rather than a strange index.Sorting and rankingsort_values, nlargest, rank, and ranking within groups.Merging and joiningmerge, join types, and validating that the merge did what you expected.Pivoting and reshapingpivot_table and melt — moving between wide and long.
Visualisation & export
Charts with matplotlib and seaborn, then getting results out.
Plotting basicsmatplotlib, the pandas .plot shortcut, and seaborn — when to use each.Choosing a chart typeLine, bar, histogram and scatter — which question each one answers.Statistical charts with seabornBetter defaults, and the charts that are painful in raw matplotlib.Exporting data and chartsto_csv, to_excel, multiple sheets, and saving figures properly.