runtimecompare approaches one problem · two ways
Compare Approaches
The same problem solved different ways in Python, run on the same input, with what really differs and when to use each.
csv module vs pandas
Average order total per state, from the Online Store orders file.
what differs
csv gives every field as a string and leaves types to you. pandas infers column types, loads everything into memory, and provides vectorised operations.
at a glance
| approach | use it when |
|---|---|
| A · csv.DictReader | No dependencies, streaming large files row by row, or simple per-row logic. |
| B · pandas | Analysis: grouping, joining, reshaping, statistics on whole columns. |
Watch out: pandas reads empty fields as NaN and may turn ID-like columns with leading zeros into numbers; pass dtype={"zip": str}.
the outputs
Each approach runs on the same input.
concepts
csv moduleDataFrametype inference
learn it
Read CSV rows as dictionaries · Series and DataFrames
related comparisons
Python loops vs pandas · csv.reader (indexes) vs csv.DictReader (names)