DOC: Fixed documentation for few files (pandas-dev#40903)

Co-authored-by: ShaharNaveh <>

Author: ShaharNaveh

ci/code_checks.sh

@@ -110,10 +110,13 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
     pytest -q --doctest-modules \
       pandas/core/accessor.py \
       pandas/core/aggregation.py \
+      pandas/core/algorithms.py \
       pandas/core/base.py \
       pandas/core/construction.py \
       pandas/core/frame.py \
       pandas/core/generic.py \
+      pandas/core/indexers.py \
+      pandas/core/nanops.py \
       pandas/core/series.py \
       pandas/io/sql.py
     RET=$(($RET + $?)) ; echo $MSG "DONE"

pandas/core/algorithms.py

@@ -375,46 +375,60 @@ def unique(values):
     >>> pd.unique(pd.Series([2] + [1] * 5))
     array([2, 1])
 
-    >>> pd.unique(pd.Series([pd.Timestamp('20160101'),
-    ...                     pd.Timestamp('20160101')]))
+    >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
     array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
 
-    >>> pd.unique(pd.Series([pd.Timestamp('20160101', tz='US/Eastern'),
-    ...                      pd.Timestamp('20160101', tz='US/Eastern')]))
-    array([Timestamp('2016-01-01 00:00:00-0500', tz='US/Eastern')],
-          dtype=object)
-
-    >>> pd.unique(pd.Index([pd.Timestamp('20160101', tz='US/Eastern'),
-    ...                     pd.Timestamp('20160101', tz='US/Eastern')]))
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
+    <DatetimeArray>
+    ['2016-01-01 00:00:00-05:00']
+    Length: 1, dtype: datetime64[ns, US/Eastern]
+
+    >>> pd.unique(
+    ...     pd.Index(
+    ...         [
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...             pd.Timestamp("20160101", tz="US/Eastern"),
+    ...         ]
+    ...     )
+    ... )
     DatetimeIndex(['2016-01-01 00:00:00-05:00'],
-    ...           dtype='datetime64[ns, US/Eastern]', freq=None)
+            dtype='datetime64[ns, US/Eastern]',
+            freq=None)
 
-    >>> pd.unique(list('baabc'))
+    >>> pd.unique(list("baabc"))
     array(['b', 'a', 'c'], dtype=object)
 
     An unordered Categorical will return categories in the
     order of appearance.
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'))))
-    [b, a, c]
-    Categories (3, object): [b, a, c]
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
-    ...                                    categories=list('abc'))))
-    [b, a, c]
-    Categories (3, object): [b, a, c]
+    >>> pd.unique(pd.Series(pd.Categorical(list("baabc"), categories=list("abc"))))
+    ['b', 'a', 'c']
+    Categories (3, object): ['a', 'b', 'c']
 
     An ordered Categorical preserves the category ordering.
 
-    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
-    ...                                    categories=list('abc'),
-    ...                                    ordered=True)))
-    [b, a, c]
-    Categories (3, object): [a < b < c]
+    >>> pd.unique(
+    ...     pd.Series(
+    ...         pd.Categorical(list("baabc"), categories=list("abc"), ordered=True)
+    ...     )
+    ... )
+    ['b', 'a', 'c']
+    Categories (3, object): ['a' < 'b' < 'c']
 
     An array of tuples
 
-    >>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
+    >>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
     array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
     """
     values = _ensure_arraylike(values)

pandas/core/indexers.py

@@ -209,16 +209,24 @@ def validate_indices(indices: np.ndarray, n: int) -> None:
 
     Examples
     --------
-    >>> validate_indices([1, 2], 3)
-    # OK
-    >>> validate_indices([1, -2], 3)
-    ValueError
-    >>> validate_indices([1, 2, 3], 3)
-    IndexError
-    >>> validate_indices([-1, -1], 0)
-    # OK
-    >>> validate_indices([0, 1], 0)
-    IndexError
+    >>> validate_indices(np.array([1, 2]), 3) # OK
+
+    >>> validate_indices(np.array([1, -2]), 3)
+    Traceback (most recent call last):
+        ...
+    ValueError: negative dimensions are not allowed
+
+    >>> validate_indices(np.array([1, 2, 3]), 3)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
+
+    >>> validate_indices(np.array([-1, -1]), 0) # OK
+
+    >>> validate_indices(np.array([0, 1]), 0)
+    Traceback (most recent call last):
+        ...
+    IndexError: indices are out-of-bounds
     """
     if len(indices):
         min_idx = indices.min()

pandas/core/nanops.py

@@ -1056,7 +1056,7 @@ def nanargmax(
            [ 6.,  7., nan],
            [ 9., 10., nan]])
     >>> nanops.nanargmax(arr, axis=1)
-    array([2, 2, 1, 1], dtype=int64)
+    array([2, 2, 1, 1])
     """
     values, mask, _, _, _ = _get_values(values, True, fill_value_typ="-inf", mask=mask)
     # error: Need type annotation for 'result'
@@ -1102,7 +1102,7 @@ def nanargmin(
            [nan,  7.,  8.],
            [nan, 10., 11.]])
     >>> nanops.nanargmin(arr, axis=1)
-    array([0, 0, 1, 1], dtype=int64)
+    array([0, 0, 1, 1])
     """
     values, mask, _, _, _ = _get_values(values, True, fill_value_typ="+inf", mask=mask)
     # error: Need type annotation for 'result'