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nishad shabbir
c7bbf1332a
`pyarrow.types.is_temporal()` returns True for interval types, but its docstring doesn't say so:
```python
>>> import pyarrow as pa, pyarrow.types as t
>>> t.is_temporal(pa.month_day_nano_interval())
True
>>> t.is_temporal.__doc__.strip().splitlines()[0]
'Return True if value is an instance of type: date, time, timestamp or duration.'
```
That's the documented contract being narrower than the behaviour, and since these docstrings are generated from the `@ doc(is_null, datatype=...)` decorator they're what ends up on the API docs page.
Including interval is deliberate rather than accidental — `_TEMPORAL_TYPES` is built as
```python
_TEMPORAL_TYPES = ({lib.Type_TIMESTAMP,
lib.Type_DURATION} | _TIME_TYPES | _DATE_TYPES |
_INTERVAL_TYPES)
```
and `test_is_temporal_date_time_timestamp` already asserts `types.is_temporal(pa.month_day_nano_interval())`. So this is a docs fix, not a behaviour question; I've only changed the `datatype` string.
After the change the rendered line is
```
Return True if value is an instance of type: date, time, timestamp, duration or interval.
```
which is exactly the set that returns True — I checked each one (date32, time32, timestamp, duration, interval all True; int32 False) by applying the same edit to an installed pyarrow and reading the generated docstring back.
I found this while comparing every predicate set in `types.py` against the corresponding `arrow::is_*()` in `cpp/src/arrow/type_traits.h`, the same way GH-50847 turned up. Worth noting the two `is_temporal` definitions genuinely differ — the C++ one covers only date/time/timestamp, while Python also counts duration and interval — but that's a semantic question rather than something to change quietly in a MINOR PR, so I've left it alone and only made the Python docstring match the Python behaviour.
Authored-by: nishad shabbir <nishadshabbir28@gmail.com>
Signed-off-by: Raúl Cumplido <raulcumplido@gmail.com>
Python library for Apache Arrow
This library provides a Python API for functionality provided by the Arrow C++ libraries, along with tools for Arrow integration and interoperability with pandas, NumPy, and other software in the Python ecosystem.
Installing
Across platforms, you can install a recent version of pyarrow with the conda package manager:
conda install pyarrow -c conda-forge
On Linux, macOS, and Windows, you can also install binary wheels from PyPI with pip:
pip install pyarrow
If you encounter any issues importing the pip wheels on Windows, you may need to install the latest Visual C++ Redistributable for Visual Studio.
Development
See Python Development in the documentation subproject.
Building the documentation
See documentation build instructions in the documentation subproject.