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Module · 5 lessons
NumPy for Data Engineering
Why arrays are the foundation of fast, memory-lean numeric work: how NumPy's contiguous, single-dtype arrays beat Python lists, how to pick the smallest safe dtype, and how vectorization and broadcasting replace slow loops — all measured on the real NYC taxi data.
At a glance
Level
Beginner to Intermediate
Lessons
5 lessons
Time to complete
1 week
Cost
Free forever · no sign-up
Welcome to NumPy for Data Engineering, the second module. Under every fast data tool — pandas included — sits NumPy: contiguous arrays of a single type that the CPU can crunch at C speed. If you want to process data efficiently, this is the layer to understand first.
You’ll start by seeing exactly why NumPy arrays are faster and far smaller than Python lists for numeric data, then learn to choose the smallest safe dtype so your arrays don’t waste memory (and how to avoid the overflow trap when you do). You’ll replace slow Python loops with vectorized array operations and measure the speedup on real trip calculations, and use broadcasting to combine arrays of different shapes without copying. The module closes with a guided project that computes real per-trip metrics — duration, speed, fare-per-mile, tip rate — on the taxi data, vectorized and memory-lean.
Start with Lesson 1, where a Python list and a NumPy array do the same job at very different speeds.
Lessons in this module
1
Why NumPy
Measure on a real taxi column why a NumPy array uses a quarter of the memory of a Python list and sums it about 50x faster.
2
Dtypes, Precision & Memory
Choose the narrowest safe NumPy dtype for each real taxi column, measure the memory saved, and see the int8 overflow trap corrupt 265-zone IDs on the live NYC taxi data.
3
Vectorization: Loops to Array Ops
Replace a Python loop with a single NumPy array operation to compute trip duration in minutes, and measure the real speedup on 200,000 taxi trips.
4
Broadcasting
Use NumPy broadcasting to combine arrays of different shapes without loops or copies: convert taxi miles to kilometres, compute fare-per-mile, and center four money columns on the real NYC taxi sample.
5
Guided Project: Trip Metrics with NumPy
Compute real per-trip metrics on 200,000 NYC taxi trips with pure NumPy: duration, speed, fare-per-mile, and tip rate, fully vectorized, then prove the vectorized version is hundreds of times faster than a loop and returns identical numbers.
Achievement
Complete all 5 lessons to finish the NumPy for Data Engineering module.
