Pendulum vs. built-in datetime Python comparison
The execution speed comparison between Pendulum and the default Python datetime module is generally straightforward: The built-in datetime module is significantly faster than Pendulum for basic operations.
This difference is due to how the two libraries are implemented and what they prioritize.
Performance Comparison​
For nearly all fundamental operations—such as instantiating a date, retrieving fields (year, month), or simple addition/subtraction—the native datetime module will outperform Pendulum.
Why datetime is Faster​
The Python standard library's datetime module benefits from its low-level implementation:
- C Implementation: The core logic of the
datetimemodule (for the CPython interpreter) is implemented in C code. This means its operations run very close to the hardware, minimizing Python overhead and making it extremely efficient for single operations and bulk processing. - Simplicity: It primarily deals with a simplified model (e.g., it ignores leap seconds and requires manual timezone handling), which allows its core arithmetic to be highly optimized.
Why Pendulum is Slower​
Pendulum is a wrapper built on top of the native datetime class. While it inherits from datetime, its core value comes from adding layers of logic and complexity to improve usability and reliability.
- Wrapper Overhead: Every time you call a Pendulum method (e.g.,
pendulum.now()), it executes Python code to handle the timezone defaulting, parameter parsing, and complex arithmetic logic before invoking the underlyingdatetimefunctions. This Python layer adds overhead. - Complex Logic: Pendulum's main goal is correctness, especially around timezones and DST transitions (which the standard library is poor at). Solving these complex problems requires more checks and calculations, which naturally takes more time.
- Parsing: Historically, for complex date string parsing, libraries like Pendulum were much slower than highly-optimized specialized parsers (like
ciso8601orudatetime). However, Pendulum has continually improved its internal parsing engine to be more competitive for ISO 8601 formats.
⚖️ The Trade-Off: Speed vs. Correctness/Usability​
The decision to use Pendulum should almost never be based on raw speed. Instead, it is a choice between micro-optimization and development efficiency/correctness.
| Feature | datetime (Speed Priority) | Pendulum (Correctness/Usability Priority) |
|---|---|---|
| Execution Speed | Fastest (Due to C implementation). | Slower (Due to Python wrapper and complex logic). |
| Timezone Awareness | Naive by default (Prone to bugs). Requires manual use of pytz or zoneinfo. | Aware by default (pendulum.now() is safe). |
| Arithmetic | Error-prone for months/years; does not handle DST boundaries correctly. | Reliable and intuitive (dt.add(months=1) correctly handles end-of-month changes). |
| Ease of Use | Verbose and requires specific format strings (strftime/strptime). | Fluent API and automatic handling of common date formats (pendulum.parse()). |
Conclusion on Speed​
If your application's primary bottleneck is creating or manipulating millions of simple, naive date objects in a tight loop, stick with the native datetime.
If your application handles user-submitted data, scheduling, logs, or multi-region data where timezone shifts, daylight saving time, or complex relative calculations are required, use Pendulum. The small performance cost is a worthwhile trade-off for eliminating subtle, critical date-time bugs that are extremely difficult to track down.