pandas

Tabular data loading, cleaning and aggregation.

pandas is taught here in 12 lessons, running from Series and DataFrame through to Debugging pandas: SettingWithCopy, dtype and index surprises. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.

Lessons

1. Series and DataFrameThe two data structures, what the index really is, and how to inspect a new dataset before touching it.
2. Reading and writing dataCSV, Excel, JSON and SQL in and out — plus the encoding, dtype and date problems that appear on real files.
3. Selecting, filtering and cleaningloc, iloc, boolean filters, missing values and type conversion — the routine work that makes up most of a data task.
4. Grouping, joining and reshapingsplit-apply-combine with groupby, merging tables like SQL, and moving between long and wide layouts.
5. The index in depth: MultiIndex, set_index and reindexHierarchical indexes, stack and unstack, reindex, and the alignment rules that quietly decide whether your numbers land on the right rows.
6. Dates, times and time seriesto_datetime, DatetimeIndex, resample, rolling, shift, asfreq, time zones and business calendars - working with data that moves through time.
7. Pivot tables and crosstabpivot_table with aggregators, crosstab with normalisation, margins, multi-level columns and flattening the result into something tableable.
8. String operations with .strThe .str accessor, regex extraction, contains and replace, splitting columns apart, and cleaning text columns without a Python loop.
9. apply, map and writing fast pandas codemap versus apply versus vectorised operations, transform for group-aligned results, pipe for readable chains, and why a row-wise loop is a last resort.
10. Categoricals, dtypes and memory optimisationThe category dtype, nullable integers and booleans, deliberate downcasting, and how to measure what a frame actually costs in memory.
11. Plotting and quick exploratory chartsdf.plot, the chart kinds worth knowing, subplots for comparisons, styling, and when to hand the figure over to Matplotlib or Seaborn.
12. Debugging pandas: SettingWithCopy, dtype and index surprisesThe three failures that waste the most time - chained assignment, silent dtype coercion and misaligned indexes - and how to catch them early.

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