merge main

.github/workflows/publish_to_pypi.yml

@@ -30,7 +30,7 @@ jobs:
   publish-to-pypi:
     name: >-
       Publish Python 🐍 distribution 📦 to PyPI
-    if: startsWith(github.ref, 'refs/tags/')  # only publish to PyPI on tag pushes
+    if: startsWith(github.ref, 'refs/tags/v')  # only publish to PyPI on tag pushes
     needs:
     - build
     runs-on: ubuntu-latest

docs/extending.md

@@ -26,14 +26,16 @@ Make sure that, in addition to the public Narwhals API, you also define:
     from `Narwhals.DataFrame`
   - `DataFrame.__narwhals_namespace__`: return an object which implements public top-level
     functions from `narwhals` (e.g. `narwhals.col`, `narwhals.concat`, ...)
+  - `DataFrame.__native_namespace__`: return a native namespace object which must have a
+    `from_dict` method
   - `LazyFrame.__narwhals_lazyframe__`: return an object which implements public methods
     from `Narwhals.LazyFrame`
   - `LazyFrame.__narwhals_namespace__`: return an object which implements public top-level
     functions from `narwhals` (e.g. `narwhals.col`, `narwhals.concat`, ...)
+  - `LazyFrame.__native_namespace__`: return a native namespace object which must have a
+    `from_dict` method
   - `Series.__narwhals_series__`: return an object which implements public methods
     from `Narwhals.Series`
-  - `Series.__narwhals_namespace__`: return an object which implements public top-level
-    functions from `narwhals` (e.g. `narwhals.col`, `narwhals.concat`, ...)
 
   If your library doesn't distinguish between lazy and eager, then it's OK for your dataframe
   object to implement both `__narwhals_dataframe__` and `__narwhals_lazyframe__`. In fact,

docs/how_it_works.md

@@ -88,7 +88,7 @@ The `_call` method gives us that function! Let's see it in action.
 
 Note: the following examples use `PandasLikeDataFrame` and `PandasLikeSeries`. These are backed
 by actual `pandas.DataFrame`s and `pandas.Series` respectively and are Narwhals-compliant. We can access the 
-underlying pandas objects via `PandasLikeDataFrame._native_dataframe` and `PandasLikeSeries._native_series`.
+underlying pandas objects via `PandasLikeDataFrame._native_frame` and `PandasLikeSeries._native_series`.
 
 ```python exec="1" result="python" session="pandas_impl" source="above"
 import narwhals as nw
@@ -222,10 +222,10 @@ df_compliant = df._compliant_frame
 result = df_compliant.select(expr)
 ```
 
-We can then view the underlying pandas Dataframe which was produced by calling `._native_dataframe`:
+We can then view the underlying pandas Dataframe which was produced by calling `._native_frame`:
 
 ```python exec="1" result="python" session="pandas_api_mapping" source="above"
-print(result._native_dataframe)
+print(result._native_frame)
 ```
 which is the same as we'd have obtained by just using the Narwhals API directly:
 

docs/installation.md

@@ -11,6 +11,6 @@ Then, if you start the Python REPL and see the following:
 ```python
 >>> import narwhals
 >>> narwhals.__version__
-'1.3.0'
+'1.4.0'
 ```
 then installation worked correctly!

narwhals/__init__.py

@@ -48,7 +48,7 @@
 from narwhals.utils import maybe_convert_dtypes
 from narwhals.utils import maybe_set_index
 
-__version__ = "1.3.0"
+__version__ = "1.4.0"
 
 __all__ = [
     "selectors",