Are For-Loops in Pandas Always Inefficient? When Should I Iterate Instead of Vectorizing?

Are for-loops in pandas really bad? When should I care?

For loops have been conventionally seen as "bad" in pandas, but this is not always accurate. There are specific cases when iteration may be more efficient than using vectorized approaches:

Small Data: For small datasets, iteration (via list comprehensions) can be faster than vectorized functions, as they avoid certain overheads related to handling index alignment, mixed data types, etc.

Mixed/Object dtypes: Pandas has difficulty working efficiently with mixed data types, including objects, lists, and dictionaries. Iteration offers significant performance benefits in such scenarios, especially for operations like dictionary value extraction, list indexing, and nested list flattening.

Regex Operations: Vectorized string operations in pandas (e.g., str.contains, str.extract) are often slower than iteration with regular expressions. Pre-compiling patterns and using list comprehensions can yield much better performance, especially for complex or repeated regular expression operations.

In general, while vectorization is a powerful feature of pandas, it may not always be the optimal approach. By understanding these cases where iteration is more suitable, you can optimize the performance of your pandas code.

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