Comparing
Better dates and times in Python — timezone-aware by default, human-friendly API, and natural language parsing.
Data validation using Python type hints
| Metric | arrow | pydantic |
|---|---|---|
| Ecosystem | Python | Python |
| Difficulty | Beginner ✓ | Intermediate |
| Weekly downloads | 2.5M↑ | 0 |
| Latest version | v1.3.0 | v2.12.5 |
| Recipes | 1 | 2↑ |
arrow only
Shared
pydantic only
Any application dealing with dates across timezones, relative time displays ('posted 2 days ago'), date parsing from strings, date range iteration, or when you want less boilerplate than stdlib datetime.
Use Pydantic for parsing API responses, validating config files, defining request/response schemas in FastAPI, or any time you receive data from an external source and want guarantees about its shape and types.
High-frequency numerical date computations (use numpy datetime64 or pandas Timestamp which are faster). Arrow adds overhead per operation.
Pydantic adds overhead on every model instantiation — not ideal for tight inner loops parsing millions of small objects. For pure config files with no user-facing validation, dataclasses or simple TypedDicts may be lighter.
arrow
pydantic