Add pre-commit hooks with pyink and mypy (#91)

This commit sets up automated code quality checks using pre-commit:

1. Pre-commit configuration (.pre-commit-config.yaml):
   - Added pyink for code formatting (reads config from pyproject.toml)
   - Added mypy for type checking
- Added standard pre-commit hooks (trailing whitespace, end-of-file,
etc.)

2. Updated pyproject.toml:
   - Added mypy and pre-commit to dev dependencies
   - Added pandas-stubs for better type checking
   - Configured mypy with per-module ignores for libraries without stubs
   - Set python_version to 3.11 to match .python-version file

3. Fixed type errors in xarray_sql/df.py:
   - Fixed tuple concatenation type issues in block_slices()
   - Fixed Callable type annotation syntax in from_map_batched()
   - Added type ignore for xarray return type in pivot()

4. Updated CONTRIBUTING.md:
   - Documented pre-commit setup steps
   - Updated formatting/type-checking instructions

5. Updated GitHub Actions lint workflow:
   - Added mypy type checking step
   - Changed to use uv run for consistency

6. Auto-fixed formatting issues:
   - Removed trailing whitespace
   - Fixed end-of-file newlines
   - Applied pyink formatting

All hooks pass successfully and type checking works correctly.

---------

Co-authored-by: jayendra <jayendra0parmar@gmail.com>

Author: jayendra-info

.github/workflows/ci.yml

@@ -48,4 +48,4 @@ jobs:
       - name: Install xarray_sql
         run: uv sync --dev
       - name: Run unit tests
-        run: uv run pytest -m "not integration"
+        run: uv run pytest -m "not integration"

.github/workflows/lint.yml

@@ -39,5 +39,5 @@ jobs:
         uses: astral-sh/setup-uv@v5
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Lint with pyink
-        run: uvx pyink --check .
+      - name: Run pre-commit (with uvx)
+        run: uvx pre-commit run --all-files

.github/workflows/publish.yml

@@ -94,4 +94,4 @@ jobs:
         with:
           user: __token__
           password: ${{ secrets.PYPI_TOKEN }}
-          verbose: true
+          verbose: true

.gitignore

@@ -5,4 +5,4 @@ dist
 __pycache__
 .pytest_cache
 .venv
-.idea
+.idea

.pre-commit-config.yaml

@@ -0,0 +1,23 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+      - id: check-json
+      - id: check-toml
+
+  - repo: https://github.com/google/pyink
+    rev: 24.10.1
+    hooks:
+      - id: pyink
+        # Configuration is read from pyproject.toml [tool.pyink]
+
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.11.2
+    hooks:
+      - id: mypy
+        additional_dependencies: [types-setuptools]
+        args: [--ignore-missing-imports]

.python-version

@@ -1 +1 @@
-3.11
+3.11

CONTRIBUTING.md

@@ -15,20 +15,28 @@ reading [Xarray's contributing guide](https://docs.xarray.dev/en/stable/contribu
 0. We use `uv` to manage the project: https://docs.astral.sh/uv/getting-started/installation/
 1. Clone the repository (bonus: [via SSH](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account))
    and `cd xarray_sql` (the project root).
-2. Install dev dependencies via: `uv sync --dev` 
+2. Install dev dependencies via: `uv sync --dev`
+3. Install pre-commit hooks: `uv run pre-commit install`
+
+   This will automatically run code formatting (pyink) and type checking (mypy) before each commit.
+   You can also run the hooks manually with: `uv run pre-commit run --all-files`
 
 
 ## Before submitting a pull request...
 
 Thanks so much for your contribution! For a volunteer led project, we so
 appreciate your help. A few things to keep in mind:
 - Please be nice. We assume good intent from you, and we ask you to do the same for us.
-- Development in this project will be slow if not sporadic. Reviews will come 
+- Development in this project will be slow if not sporadic. Reviews will come
   as time allows.
-- Every contribution, big or small, matters and deserves credit. 
+- Every contribution, big or small, matters and deserves credit.
 
 Here are a few requests for your development process:
-- We require all code to be formatted with `pyink` (i.e., please run `uvx pyink .`). 
+- We require all code to be formatted with `pyink` and type-checked with `mypy`.
+  These checks run automatically via pre-commit hooks (see Developer setup above).
+  If you need to run them manually:
+  - Formatting: `uv run pre-commit run pyink --all-files` or `uvx pyink .`
+  - Type checking: `uv run pre-commit run mypy --all-files` or `uv run mypy xarray_sql/`
 - Please include unit tests, if possible, and performance tests when you touch the core functionality (see `perf_tests/`).
 - It's polite to do a self review before asking for one from a maintainer. Don't stress if you forget; we all do sometimes.
 - Please add (or update) documentation when adding new code. We use [Google Style docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html).

LICENSE

@@ -199,4 +199,4 @@
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
-   limitations under the License.
+   limitations under the License.

README.md

@@ -42,8 +42,8 @@ ctx.from_dataset('air', ds, chunks=dict(time=24))  # the dataset needs to be chu
 result = ctx.sql('''
   SELECT
     "lat", "lon", AVG("air") as air_total
-  FROM 
-    "air" 
+  FROM
+    "air"
   GROUP BY
    "lat", "lon"
 ''')
@@ -63,7 +63,7 @@ result = ctx.sql('''
 # | 72.5 | 275.0 | 257.30633219178037 |
 # +------+-------+--------------------+
 # Data truncated.
-# 
+#
 
 # A table of the average temperature for each location across time.
 df = result.to_pandas()
@@ -104,7 +104,7 @@ All chunks in a Xarray Dataset are transformed into a Dask DataFrame via
 `from_map()` and `to_dataframe()`. For SQL support, we just use `dask-sql`.
 That's it!
 
-_2025 update_: This library now implements a Dask-like `from_map` interface in 
+_2025 update_: This library now implements a Dask-like `from_map` interface in
 pure DataFusion and PyArrow, but works with the same principle!
 
 ## Why does this work?
@@ -113,13 +113,13 @@ Underneath Xarray, Dask, and Pandas, there are NumPy arrays. These are paged in
 chunks and represented contiguously in memory. It is only a matter of metadata
 that breaks them up into ndarrays. `to_dataframe()`
 just changes this metadata (via a `ravel()`/`reshape()`), back into a column
-amenable to a DataFrame. We take advantage of this light weight metadata change to 
+amenable to a DataFrame. We take advantage of this light weight metadata change to
 make chunked information scannable by a DB engine (DataFusion).
 
 ## What are the current limitations?
 
 _2025 update_: TBD, DataFusion provides a whole new world! Currently, we're looking for
-early users – "tire kickers", if you will. We'd love your input to shape the direction of this 
+early users – "tire kickers", if you will. We'd love your input to shape the direction of this
 project! Please, give this a try and [file issues](https://github.com/alxmrs/xarray-sql/issues) as
 you see fit. Check out our [contributing guide](CONTRIBUTING.md), too 😉.
 

perf_tests/README.md

@@ -1,6 +1,6 @@
 # Performance testing & profiling
 
-So far, this includes statistical profiles via py-spy. 
+So far, this includes statistical profiles via py-spy.
 
 ## Dev Process
 
@@ -16,4 +16,3 @@ So far, this includes statistical profiles via py-spy.
 2. After tuning code in xarray-sql, run another profile to generate a SVG.
 
 3. Please commit the "after" profile SVG along with the performance improvements.
-