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Row iteration

As explained in Fast Python: High performance techniques for large datasets

Row iteration is the technique of processing a tabular dataset one record at a time through sequential procedural evaluation.

Row iteration processes a tabular dataset one record at a time through sequential procedural evaluation. In pandas, it offers a familiar baseline for computing statistics, but can become a dominant performance bottleneck. section 7.2 section 7.6

Common pandas approaches form a practical performance ladder:

  • Explicit indexing with iloc repeatedly retrieves each row and is generally slow. section 7.2.2
  • iterrows simplifies the loop but remains inefficient because it still evaluates rows individually. section 7.2.2
  • itertuples preserves the loop-based style while reducing the measured mean time to 18 seconds in the example. section 7.2.2
  • apply processes each row through a function; dictionary-style access took 9.5 seconds, while attribute access took 14 seconds in the reported tests. section 7.2.2
  • Vectorization replaces row-by-row evaluation with column operations and is the preferred direction when the computation permits it. section 7.2.2 section 7.6

Mean taxi-tip fraction

Load NYC trip records, then compute tip_amount divided by total_amount. A loop accumulates both columns, while the vectorized form evaluates df["tip_amount"] / df["total_amount"] and averages the resulting series. Benchmark competing approaches on sampled data rather than assuming a fixed ranking across pandas versions. section 7.2 section 7.2.2 section 7.6

How Fast Python: High performance techniques for large datasets covers it

How deep it goes
Compares explicit loops, iterrows, itertuples, apply, and vectorization with timings, but does not derive their internal implementations.
Examples
Demonstrates NYC taxi-tip calculations through explicit loops, pandas iterators, apply variants, and a vectorized expression.
What it assumes
Assumes familiarity with Python loops and pandas data frames while encouraging progression toward vectorized computation.
What you can do after
Diagnose row-wise bottlenecks and choose between itertuples, apply, and vectorization using measured performance.
How it connects
Connects row iteration to pandas indexing, vectorization, NumPy, Cython, NumExpr, and Apache Arrow.
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Related concepts

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