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Module · 5 lessons
Pandas at Scale
Make pandas lean and fast: downcast numeric columns, use categoricals for low-cardinality data, avoid needless copies, and aggregate efficiently — executing Module 1's reduction plan on the real NYC taxi data to cut its memory by more than half.
At a glance
Level
Beginner to Intermediate
Lessons
5 lessons
Time to complete
1 week
Cost
Free forever · no sign-up
Welcome to Pandas at Scale, the third module — where the reduction plan you wrote in Module 1 becomes real, working code. Pandas is where most data engineers actually live, and a few habits are the difference between a DataFrame that fits comfortably and one that crashes your kernel.
You’ll learn to downcast numeric columns to the smallest safe type, convert low-cardinality columns like payment type and zone to categoricals (often a huge saving), transform data without triggering the silent copies that double your memory, and write group-by aggregations that stay fast on millions of rows. The module closes with a guided project that packages everything into a reusable load_taxi() function — and measures the real memory it saves versus a naive load.
Start with Lesson 1, where a single downcast call quietly halves a column.
Lessons in this module
1
Downcasting Numerics
Shrink pandas int64/float64 columns to the smallest type that safely holds their real values with pd.to_numeric downcast and astype, then measure the exact memory saved on the NYC taxi sample.
2
Categoricals
Convert low-cardinality taxi columns like payment_type and pickup borough to the category dtype for large, measured memory savings — and see the case where a near-unique column makes category bigger, not smaller.
3
Efficient Operations & Avoiding Copies
Learn which pandas operations copy your data, why stacked copies quietly double a pipeline's memory, and how method chaining rebuilds the same per-zone taxi stats with 2.7x less peak memory.
4
Fast Group-By & Aggregation
Write fast pandas group-by aggregations on real NYC taxi data with named aggregations, use observed=True on categorical keys, avoid slow .apply(), and broadcast with transform.
5
Guided Project: A Memory-Efficient Taxi Loader
Build a reusable load_taxi() function that applies the whole Module 3 reduction plan to the full month of NYC taxi trips, then measure the real memory it saves versus a naive load.
Achievement
Complete all 5 lessons to finish the Pandas at Scale module.
