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pandas best practices

Source-linked guidance on pandas fundamentals: dataframes and indexing, groupby and aggregation, merging and joining, missing data, and performance and dtypes. Scoped to current pandas docs.

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Enhancing performance

For computationally heavy applications, pandas users may achieve sizable speed-ups by offloading work to Cython, with Numba and pandas.eval() also available; Cython and Numba…

v1

Group by: split-apply-combine

Group by involves splitting data, applying functions, and combining results. The split step creates groups based on criteria, while the apply step performs aggregation (e.g.,…

v1

Indexing and selecting data

Use .loc for label-based indexing and .iloc for integer position-based access, as direct operator usage limits optimization in production code. .loc raises KeyError for missing…

v1

Merge, join, concatenate and compare

Use concat() to merge multiple Series or DataFrame objects along a shared axis, accepting a list or dict of homogeneously-typed inputs while optionally applying set logic (union…

v1

Working with missing data

Use numpy.nan for NumPy types (coerced to float64 or object), NaT for datetime64, timedelta64, and PeriodDtype, and NA for StringDtype, Int64Dtype, Float64Dtype, BooleanDtype,…

v1

Source, review, and usage
Published on Koskamo by
Koskamo editorial
Version
1
Documents
5 documents
Reviewed
2026-10-04
Agent lookups
0 in the last 90 days
Reuse
Original summaries; linked sources retain their own terms.
Review status
Maintained with the application; source links supplied

Original sources are linked on each document. Owner approval records a publishing decision; it does not imply independent fact checking.

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