DOC: Validator + converting array_like to array-like in docstrings (#41295)

* Converting array_like to array-like in docstrings & comments

* Add docstring validation check for array_like vs. array-like

* Add unit test for arraylike validator, and fix example in core/generic.py

* make method of PandasDocstring

Co-authored-by: Marco Gorelli <[email protected]>

Author: kemcbride

pandas/core/algorithms.py

@@ -1530,13 +1530,13 @@ def searchsorted(arr, value, side="left", sorter=None) -> np.ndarray:
         Input array. If `sorter` is None, then it must be sorted in
         ascending order, otherwise `sorter` must be an array of indices
         that sort it.
-    value : array_like
+    value : array-like
         Values to insert into `arr`.
     side : {'left', 'right'}, optional
         If 'left', the index of the first suitable ___location found is given.
         If 'right', return the last such index.  If there is no suitable
         index, return either 0 or N (where N is the length of `self`).
-    sorter : 1-D array_like, optional
+    sorter : 1-D array-like, optional
         Optional array of integer indices that sort array a into ascending
         order. They are typically the result of argsort.
 

pandas/core/array_algos/replace.py

@@ -45,21 +45,21 @@ def compare_or_regex_search(
     a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: np.ndarray
 ) -> ArrayLike | bool:
     """
-    Compare two array_like inputs of the same shape or two scalar values
+    Compare two array-like inputs of the same shape or two scalar values
 
     Calls operator.eq or re.search, depending on regex argument. If regex is
     True, perform an element-wise regex matching.
 
     Parameters
     ----------
-    a : array_like
+    a : array-like
     b : scalar or regex pattern
     regex : bool
     mask : np.ndarray[bool]
 
     Returns
     -------
-    mask : array_like of bool
+    mask : array-like of bool
     """
     if isna(b):
         return ~mask

pandas/core/arrays/base.py

@@ -826,13 +826,13 @@ def searchsorted(self, value, side="left", sorter=None):
 
         Parameters
         ----------
-        value : array_like
+        value : array-like
             Values to insert into `self`.
         side : {'left', 'right'}, optional
             If 'left', the index of the first suitable ___location found is given.
             If 'right', return the last such index.  If there is no suitable
             index, return either 0 or N (where N is the length of `self`).
-        sorter : 1-D array_like, optional
+        sorter : 1-D array-like, optional
             Optional array of integer indices that sort array a into ascending
             order. They are typically the result of argsort.
 

pandas/core/base.py

@@ -1133,13 +1133,13 @@ def factorize(self, sort: bool = False, na_sentinel: int | None = -1):
 
         Parameters
         ----------
-        value : array_like
+        value : array-like
             Values to insert into `self`.
         side : {{'left', 'right'}}, optional
             If 'left', the index of the first suitable ___location found is given.
             If 'right', return the last such index.  If there is no suitable
             index, return either 0 or N (where N is the length of `self`).
-        sorter : 1-D array_like, optional
+        sorter : 1-D array-like, optional
             Optional array of integer indices that sort `self` into ascending
             order. They are typically the result of ``np.argsort``.
 

pandas/core/generic.py

@@ -7285,11 +7285,11 @@ def clip(
 
         Parameters
         ----------
-        lower : float or array_like, default None
+        lower : float or array-like, default None
             Minimum threshold value. All values below this
             threshold will be set to it. A missing
             threshold (e.g `NA`) will not clip the value.
-        upper : float or array_like, default None
+        upper : float or array-like, default None
             Maximum threshold value. All values above this
             threshold will be set to it. A missing
             threshold (e.g `NA`) will not clip the value.
@@ -7889,8 +7889,8 @@ def resample(
 
         Pass a custom function via ``apply``
 
-        >>> def custom_resampler(array_like):
-        ...     return np.sum(array_like) + 5
+        >>> def custom_resampler(arraylike):
+        ...     return np.sum(arraylike) + 5
         ...
         >>> series.resample('3T').apply(custom_resampler)
         2000-01-01 00:00:00     8

pandas/core/indexes/base.py

@@ -6025,8 +6025,8 @@ def any(self, *args, **kwargs):
 
         Returns
         -------
-        any : bool or array_like (if axis is specified)
-            A single element array_like may be converted to bool.
+        any : bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
 
         See Also
         --------
@@ -6069,8 +6069,8 @@ def all(self, *args, **kwargs):
 
         Returns
         -------
-        all : bool or array_like (if axis is specified)
-            A single element array_like may be converted to bool.
+        all : bool or array-like (if axis is specified)
+            A single element array-like may be converted to bool.
 
         See Also
         --------

pandas/core/indexes/multi.py

@@ -3877,7 +3877,7 @@ def maybe_droplevels(index: Index, key) -> Index:
 
 def _coerce_indexer_frozen(array_like, categories, copy: bool = False) -> np.ndarray:
     """
-    Coerce the array_like indexer to the smallest integer dtype that can encode all
+    Coerce the array-like indexer to the smallest integer dtype that can encode all
     of the given categories.
 
     Parameters

pandas/core/internals/blocks.py

@@ -1276,7 +1276,7 @@ def _unstack(self, unstacker, fill_value, new_placement):
         -------
         blocks : list of Block
             New blocks of unstacked values.
-        mask : array_like of bool
+        mask : array-like of bool
             The mask of columns of `blocks` we should keep.
         """
         new_values, mask = unstacker.get_new_values(

pandas/core/missing.py

@@ -524,11 +524,11 @@ def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
 
     Parameters
     ----------
-    xi : array_like
+    xi : array-like
         sorted 1D array of x-coordinates
-    yi : array_like or list of array-likes
+    yi : array-like or list of array-likes
         yi[i][j] is the j-th derivative known at xi[i]
-    order: None or int or array_like of ints. Default: None.
+    order: None or int or array-like of ints. Default: None.
         Specifies the degree of local polynomials. If not None, some
         derivatives are ignored.
     der : int or list
@@ -546,7 +546,7 @@ def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
 
     Returns
     -------
-    y : scalar or array_like
+    y : scalar or array-like
         The result, of length R or length M or M by R.
     """
     from scipy import interpolate
@@ -568,13 +568,13 @@ def _akima_interpolate(xi, yi, x, der=0, axis=0):
 
     Parameters
     ----------
-    xi : array_like
+    xi : array-like
         A sorted list of x-coordinates, of length N.
-    yi : array_like
+    yi : array-like
         A 1-D array of real values.  `yi`'s length along the interpolation
         axis must be equal to the length of `xi`. If N-D array, use axis
         parameter to select correct axis.
-    x : scalar or array_like
+    x : scalar or array-like
         Of length M.
     der : int, optional
         How many derivatives to extract; None for all potentially
@@ -590,7 +590,7 @@ def _akima_interpolate(xi, yi, x, der=0, axis=0):
 
     Returns
     -------
-    y : scalar or array_like
+    y : scalar or array-like
         The result, of length R or length M or M by R,
 
     """
@@ -609,14 +609,14 @@ def _cubicspline_interpolate(xi, yi, x, axis=0, bc_type="not-a-knot", extrapolat
 
     Parameters
     ----------
-    xi : array_like, shape (n,)
+    xi : array-like, shape (n,)
         1-d array containing values of the independent variable.
         Values must be real, finite and in strictly increasing order.
-    yi : array_like
+    yi : array-like
         Array containing values of the dependent variable. It can have
         arbitrary number of dimensions, but the length along ``axis``
         (see below) must match the length of ``x``. Values must be finite.
-    x : scalar or array_like, shape (m,)
+    x : scalar or array-like, shape (m,)
     axis : int, optional
         Axis along which `y` is assumed to be varying. Meaning that for
         ``x[i]`` the corresponding values are ``np.take(y, i, axis=axis)``.
@@ -644,7 +644,7 @@ def _cubicspline_interpolate(xi, yi, x, axis=0, bc_type="not-a-knot", extrapolat
         tuple `(order, deriv_values)` allowing to specify arbitrary
         derivatives at curve ends:
         * `order`: the derivative order, 1 or 2.
-        * `deriv_value`: array_like containing derivative values, shape must
+        * `deriv_value`: array-like containing derivative values, shape must
           be the same as `y`, excluding ``axis`` dimension. For example, if
           `y` is 1D, then `deriv_value` must be a scalar. If `y` is 3D with
           the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2D
@@ -661,7 +661,7 @@ def _cubicspline_interpolate(xi, yi, x, axis=0, bc_type="not-a-knot", extrapolat
 
     Returns
     -------
-    y : scalar or array_like
+    y : scalar or array-like
         The result, of shape (m,)
 
     References

pandas/plotting/_core.py

@@ -430,7 +430,7 @@ def hist_frame(
         y : label or position, optional
             Allows plotting of one column versus another. If not specified,
             all numerical columns are used.
-        color : str, array_like, or dict, optional
+        color : str, array-like, or dict, optional
             The color for each of the DataFrame's columns. Possible values are:
 
             - A single color string referred to by name, RGB or RGBA code,
@@ -1571,7 +1571,7 @@ def scatter(self, x, y, s=None, c=None, **kwargs):
         y : int or str
             The column name or column position to be used as vertical
             coordinates for each point.
-        s : str, scalar or array_like, optional
+        s : str, scalar or array-like, optional
             The size of each point. Possible values are:
 
             - A string with the name of the column to be used for marker's size.
@@ -1584,7 +1584,7 @@ def scatter(self, x, y, s=None, c=None, **kwargs):
 
               .. versionchanged:: 1.1.0
 
-        c : str, int or array_like, optional
+        c : str, int or array-like, optional
             The color of each point. Possible values are:
 
             - A single color string referred to by name, RGB or RGBA code,