Merge branch 'main' into ci/tubular

.github/workflows/downstream_tests.yml

@@ -87,21 +87,16 @@ jobs:
       - name: show-deps
         run: uv pip freeze
       - name: Create assets directory, copy over index.html
+        continue-on-error: true
         run: |
             mkdir -p marimo/marimo/_static/assets
             cp marimo/frontend/index.html marimo/marimo/_static/index.html
             cp marimo/frontend/public/favicon.ico marimo/marimo/_static/favicon.ico
-      - name: Run tests with minimal dependencies
-        if: ${{ matrix.dependencies == 'core' }}
-        run: |
-            cd marimo
-            hatch run +py=${{ matrix.python-version }} test:test -v tests/ -k "not test_cli"
-        timeout-minutes: 15
-      - name: Run tests with optional dependencies
+      - name: Run tests with full dependencies
         if: ${{ matrix.dependencies == 'core,optional' }}
         run: |
             cd marimo
-            hatch run +py=${{ matrix.python-version }} test-optional:test -v tests/ -k "not test_cli"
+            hatch run +py=${{ matrix.python-version }} test-optional:test-narwhals
         timeout-minutes: 15
       - name: Run typechecks
         run: |

.pre-commit-config.yaml

@@ -40,7 +40,7 @@ repos:
   hooks:
     - id: nbstripout
 - repo: https://github.com/adamchainz/blacken-docs
-  rev: "1.18.0"  # replace with latest tag on GitHub
+  rev: "1.19.0"  # replace with latest tag on GitHub
   hooks:
   -   id: blacken-docs
       args: [--skip-errors]

docs/installation.md

@@ -2,20 +2,36 @@
 
 ## Installation
 
-First, make sure you have [created and activated](https://docs.python.org/3/library/venv.html) a Python3.8+ virtual environment.
+=== "UV"
 
-Then, run
-```console
-python -m pip install narwhals
-```
+    First, ensure you have installed [UV](https://github.com/astral-sh/uv), and make sure you have [created and activated](https://docs.astral.sh/uv/pip/environments/#python-environments) a Python 3.8+ virtual environment.
+
+    If you haven't, you can follow our [_setting up your environment_](https://github.com/narwhals-dev/narwhals/blob/main/CONTRIBUTING.md#option-1-use-uv-recommended) guide.
+    Then, run:
+
+    ```console
+    uv pip install narwhals
+    ```
+
+=== "Python's venv"
+
+    First, ensure you have [created and activated](https://docs.python.org/3/library/venv.html) a Python 3.8+ virtual environment.
+
+    Then, run:
+
+    ```console
+    python -m pip install narwhals
+    ```
+
+### Verifying the Installation
 
-Then, if you start the Python REPL and see the following:
+To verify the installation, start the Python REPL and execute:
 ```python
 >>> import narwhals
 >>> narwhals.__version__
 '1.9.4'
 ```
-then installation worked correctly!
+If you see the version number, then the installation was successful!
 
 ## Quick start
 

mkdocs.yml

@@ -26,6 +26,7 @@ nav:
     - Supported Expr methods: api-completeness/expr.md
     - Supported Series methods: api-completeness/series.md
   - API Reference:
+    - api-reference/index.md
     - api-reference/narwhals.md
     - api-reference/dataframe.md
     - api-reference/expr.md
@@ -42,7 +43,6 @@ nav:
     - api-reference/series_str.md
     - api-reference/dependencies.md
     - api-reference/dtypes.md
-    - api-reference/index.md
     - api-reference/selectors.md
     - api-reference/typing.md
 theme:

narwhals/expr.py

@@ -367,17 +367,19 @@ def mean(self) -> Self:
         Examples:
             >>> import polars as pl
             >>> import pandas as pd
+            >>> import pyarrow as pa
             >>> import narwhals as nw
             >>> df_pd = pd.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
             >>> df_pl = pl.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
+            >>> df_pa = pa.table({"a": [-1, 0, 1], "b": [2, 4, 6]})
 
             Let's define a dataframe-agnostic function:
 
             >>> @nw.narwhalify
             ... def func(df):
             ...     return df.select(nw.col("a", "b").mean())
 
-            We can then pass either pandas or Polars to `func`:
+            We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
             >>> func(df_pd)
                  a    b
@@ -391,6 +393,13 @@ def mean(self) -> Self:
             ╞═════╪═════╡
             │ 0.0 ┆ 4.0 │
             └─────┴─────┘
+            >>> func(df_pa)
+            pyarrow.Table
+            a: double
+            b: double
+            ----
+            a: [[0]]
+            b: [[4]]
         """
         return self.__class__(lambda plx: self._call(plx).mean())
 
@@ -4054,17 +4063,19 @@ def mean(*columns: str) -> Expr:
     Examples:
         >>> import pandas as pd
         >>> import polars as pl
+        >>> import pyarrow as pa
         >>> import narwhals as nw
         >>> df_pl = pl.DataFrame({"a": [1, 8, 3]})
         >>> df_pd = pd.DataFrame({"a": [1, 8, 3]})
+        >>> df_pa = pa.table({"a": [1, 8, 3]})
 
         We define a dataframe agnostic function:
 
         >>> @nw.narwhalify
         ... def func(df):
         ...     return df.select(nw.mean("a"))
 
-        We can then pass either pandas or Polars to `func`:
+        We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
         >>> func(df_pd)
              a
@@ -4078,6 +4089,11 @@ def mean(*columns: str) -> Expr:
         ╞═════╡
         │ 4.0 │
         └─────┘
+        >>> func(df_pa)
+        pyarrow.Table
+        a: double
+        ----
+        a: [[4]]
     """
 
     return Expr(lambda plx: plx.mean(*columns))

narwhals/stable/v1/__init__.py

@@ -1254,17 +1254,19 @@ def mean(*columns: str) -> Expr:
     Examples:
         >>> import pandas as pd
         >>> import polars as pl
+        >>> import pyarrow as pa
         >>> import narwhals.stable.v1 as nw
         >>> df_pl = pl.DataFrame({"a": [1, 8, 3]})
         >>> df_pd = pd.DataFrame({"a": [1, 8, 3]})
+        >>> df_pa = pa.table({"a": [1, 8, 3]})
 
         We define a dataframe agnostic function:
 
         >>> @nw.narwhalify
         ... def func(df):
         ...     return df.select(nw.mean("a"))
 
-        We can then pass either pandas or Polars to `func`:
+        We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
         >>> func(df_pd)
              a
@@ -1278,6 +1280,11 @@ def mean(*columns: str) -> Expr:
         ╞═════╡
         │ 4.0 │
         └─────┘
+        >>> func(df_pa)
+        pyarrow.Table
+        a: double
+        ----
+        a: [[4]]
     """
     return _stableify(nw.mean(*columns))