docs: use from_native / to_native in series.py

.pre-commit-config.yaml

@@ -28,7 +28,7 @@ repos:
     hooks:
     -   id: flake8
         additional_dependencies: [darglint==1.8.1, Flake8-pyproject]
-        entry: flake8 --select DAR --ignore DAR402,DAR401
+        entry: flake8 --select DAR --ignore DAR101,DAR402,DAR401
         exclude: |
             (?x)^(
                 tests/.*|
@@ -38,10 +38,6 @@ repos:
                 narwhals/dataframe\.py$|
                 # TODO: gradually enable
                 narwhals/dependencies\.py$|
-                # some false positives in this one
-                narwhals/translate\.py$|
-                # some false positives in this one
-                narwhals/stable/v1/__init__\.py$|
                 # private, so less urgent to document too well
                 narwhals/_.*
             )$

narwhals/dataframe.py

@@ -388,6 +388,9 @@ def lazy(self) -> LazyFrame[Any]:
 
         If a library does not support lazy execution, then this is a no-op.
 
+        Returns:
+            A new LazyFrame.
+
         Examples:
             Construct pandas, Polars and PyArrow DataFrames:
 
@@ -873,6 +876,9 @@ def to_dict(
             as_series: If set to true ``True``, then the values are Narwhals Series,
                         otherwise the values are Any.
 
+        Returns:
+            A mapping from column name to values / Series.
+
         Examples:
             >>> import pandas as pd
             >>> import polars as pl
@@ -2244,6 +2250,9 @@ def join_asof(
     def is_duplicated(self: Self) -> Series:
         r"""Get a mask of all duplicated rows in this DataFrame.
 
+        Returns:
+            A new Series.
+
         Examples:
             >>> import narwhals as nw
             >>> import pandas as pd
@@ -2323,6 +2332,9 @@ def is_empty(self: Self) -> bool:
     def is_unique(self: Self) -> Series:
         r"""Get a mask of all unique rows in this DataFrame.
 
+        Returns:
+            A new Series.
+
         Examples:
             >>> import narwhals as nw
             >>> import pandas as pd

narwhals/expr.py

@@ -446,6 +446,7 @@ def ewm_mean(
             half_life: Specify decay in terms of half-life, $\tau$, with <br> $\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \tau } \right\} \forall \tau > 0$
             alpha: Specify smoothing factor alpha directly, $0 < \alpha \leq 1$.
             adjust: Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings
+
                 - When `adjust=True` (the default) the EW function is calculated
                   using weights $w_i = (1 - \alpha)^i$
                 - When `adjust=False` the EW function is calculated recursively by