Update docstring of Extensionarray.interpolate

Author: ammar-qazi

pandas/core/arrays/base.py

@@ -999,24 +999,79 @@ def interpolate(
         **kwargs,
     ) -> Self:
         """
-        See DataFrame.interpolate.__doc__.
+        Fill NaN values using an interpolation method.
 
+        Parameters
+        ----------
+        method : str, default 'linear'
+            Interpolation technique to use. One of:
+            * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate given length of interval.
+            * 'index', 'values': use the actual numerical values of the index.
+            * 'pad': Fill in NaNs using existing values.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial': 
+            Passed to scipy.interpolate.interp1d, whereas 'spline' is passed to 
+            scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. 
+            Both 'polynomial' and 'spline' require that you also specify an order (int), 
+            e.g. arr.interpolate(method='polynomial', order=5).
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline': 
+            Wrappers around the SciPy interpolation methods of similar names. See Notes.
+            * 'from_derivatives': Refers to scipy.interpolate.BPoly.from_derivatives.
+        axis : int
+            Axis to interpolate along. For 1D NumpyExtensionArray, use 0.
+        index : Index
+            Index to use for interpolation.
+        limit : int or None
+            Maximum number of consecutive NaNs to fill. Must be greater than 0.
+        limit_direction : {'forward', 'backward', 'both'}
+            Consecutive NaNs will be filled in this direction.
+            * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
+            * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 'backward'.
+            Raises ValueError if limit_direction is 'forward' or 'both' and method is 'backfill' or 'bfill'.
+            Raises ValueError if limit_direction is 'backward' or 'both' and method is 'pad' or 'ffill'.
+        limit_area : {'inside', 'outside'} or None
+            If limit is specified, consecutive NaNs will be filled with this restriction.
+            * None: No fill restriction.
+            * 'inside': Only fill NaNs surrounded by valid values (interpolate).
+            * 'outside': Only fill NaNs outside valid values (extrapolate).
+        copy : bool
+            If True, a copy of the object is returned with interpolated values.
+        **kwargs : optional
+            Keyword arguments to pass on to the interpolating function.
+
+        Returns
+        -------
+        NumpyExtensionArray
+            A new NumpyExtensionArray with interpolated values.
+
+        See Also
+        --------
+        Series.interpolate : Interpolate values in a Series.
+        DataFrame.interpolate : Interpolate values in a DataFrame.
+
+        Notes
+        -----
+        - All parameters must be specified as keyword arguments.
+        - The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' 
+          methods are wrappers around the respective SciPy implementations of 
+          similar names. These use the actual numerical values of the index.
+        - For 1D NumpyExtensionArray, use 0 for the `axis` parameter.
+        
         Examples
         --------
-        >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, 1, np.nan, 3]))
+        >>> arr = pd.arrays.NumpyExtensionArray(np.array([0, np.nan, 2, np.nan, 4]))
         >>> arr.interpolate(
-        ...     method="linear",
-        ...     limit=3,
-        ...     limit_direction="forward",
-        ...     index=pd.Index([1, 2, 3, 4]),
-        ...     fill_value=1,
-        ...     copy=False,
+        ...     method='linear',
         ...     axis=0,
-        ...     limit_area="inside",
+        ...     index=pd.Index(range(len(arr))),
+        ...     limit=None,
+        ...     limit_direction='forward',
+        ...     limit_area=None,
+        ...     copy=True
         ... )
         <NumpyExtensionArray>
-        [0.0, 1.0, 2.0, 3.0]
-        Length: 4, dtype: float64
+        [0.0, 1.0, 2.0, 3.0, 4.0]
+        Length: 5, dtype: float64
         """
         # NB: we return type(self) even if copy=False
         raise NotImplementedError(