Day 24 of 45 · Pro lesson

PySpark DataFrames

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: lazy evaluation, the 9 core DataFrame ops, predicate pushdown, bitwise filtering (&, |), avoiding withColumn loops, window deduplication

Build: E-commerce PySpark pipeline: raw CSV ingestion → data quality filtering → multi-metric aggregation → master data join.

•Complete 15 drills on column projections, multi-condition filters, aggregations, and joins.
•Run 6 Python modules comparing native expressions to UDFs and testing DataFrame invariants.
•Simulate 5 production outages: plan depth StackOverflow, ambiguous column collisions, and UDF bottlenecks.
•Defend 10 senior PySpark DataFrame questions and pass the blank-page challenge.

Never use Python 'and'/'or' on Spark Columns: use '&' and '|'. Avoid chaining withColumn in loops or using df.distinct() blindly.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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