Some more errors from online Doctests cleared

Author: hamedgibago

pandas/core/algorithms.py

@@ -346,7 +346,7 @@ def unique(values):
  array([2, 1, 3])
 
  >>> pd.unique(pd.Series([2] + [1] * 5))
- array([2, 1], dtype=int64)
+ array([2, 1])
 
  >>> pd.unique(pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")]))
  array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
@@ -714,7 +714,7 @@ def factorize(
 
  >>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
  >>> codes
- array([0, 1, 0, 2], dtype=int64)
+ array([0, 1, 0, 2])
  >>> uniques
  array([ 1., 2., nan])
  """

pandas/core/arrays/categorical.py

@@ -1831,7 +1831,7 @@ def argsort(self, ascending=True, kind="quicksort", **kwargs):
  Examples
  --------
  >>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()
- array([2, 0, 1, 3], dtype=int64)
+ array([2, 0, 1, 3])
 
  >>> cat = pd.Categorical(['b', 'b', 'a', 'c'],
  ... categories=['c', 'b', 'a'],
@@ -2227,29 +2227,28 @@ def _validate_listlike(self, value):
 
  def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
  """
- Compute the inverse of a categorical, returning
- a dict of categories -> indexers.
-
- *This is an internal function*
-
- Returns
- -------
- Dict[Hashable, np.ndarray[np.intp]]
- dict of categories -> indexers
-
- Examples
- --------
- >>> c = pd.Categorical(list('aabca'))
- >>> c
- ['a', 'a', 'b', 'c', 'a']
- Categories (3, object): ['a', 'b', 'c']
- >>> c.categories
- Index(['a', 'b', 'c'], dtype='object')
- >>> c.codes
- array([0, 0, 1, 2, 0], dtype=int8)
- >>> c._reverse_indexer()
- {'a': array([0, 1, 4], dtype=int64), 'b':
- array([2], dtype=int64), 'c': array([3], dtype=int64)}
+ Compute the inverse of a categorical, returning
+ a dict of categories -> indexers.
+
+ *This is an internal function*
+
+ Returns
+ -------
+ Dict[Hashable, np.ndarray[np.intp]]
+ dict of categories -> indexers
+
+ Examples
+ --------
+ >>> c = pd.Categorical(list('aabca'))
+ >>> c
+ ['a', 'a', 'b', 'c', 'a']
+ Categories (3, object): ['a', 'b', 'c']
+ >>> c.categories
+ Index(['a', 'b', 'c'], dtype='object')
+ >>> c.codes
+ array([0, 0, 1, 2, 0], dtype=int8)
+ >>> c._reverse_indexer()
+ {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
 
  """
  categories = self.categories

pandas/core/arrays/masked.py

@@ -377,7 +377,7 @@ def to_numpy(
  >>> pd.array([True, False], dtype="boolean").to_numpy(dtype="bool")
  array([ True, False])
  >>> pd.array([1, 2], dtype="Int64").to_numpy("int64")
- array([1, 2], dtype=int64)
+ array([1, 2])
 
  However, requesting such dtype will raise a ValueError if
  missing values are present and the default missing value :attr:`NA`

pandas/core/arrays/sparse/array.py

@@ -356,7 +356,7 @@ class SparseArray(OpsMixin, PandasObject, ExtensionArray):
  [0, 0, 1, 2]
  Fill: 0
  IntIndex
- Indices: array([2, 3])
+ Indices: array([2, 3], dtype=int32)
  """
 
  _subtyp = "sparse_array" # register ABCSparseArray
@@ -639,7 +639,7 @@ def sp_values(self) -> np.ndarray:
  --------
  >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
  >>> s.sp_values
- array([1, 2], dtype=int64)
+ array([1, 2])
  """
  return self._sparse_values
 
@@ -1277,7 +1277,7 @@ def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
  [0, 0, 1, 2]
  Fill: 0
  IntIndex
- Indices: array([2, 3])
+ Indices: array([2, 3], dtype=int32)
 
  >>> arr.astype(SparseDtype(np.dtype('int32')))
  [0, 0, 1, 2]
@@ -1355,7 +1355,7 @@ def map(self: SparseArrayT, mapper) -> SparseArrayT:
  [10, 11, 12]
  Fill: 10
  IntIndex
- Indices: array([1, 2])
+ Indices: array([1, 2], dtype=int32)
 
  >>> arr.map({0: 10, 1: 11, 2: 12})
  [10, 11, 12]

pandas/core/arrays/sparse/dtype.py

@@ -373,7 +373,7 @@ def _subtype_with_str(self):
  Returns
  -------
  >>> SparseDtype(int, 1)._subtype_with_str
- dtype('int32')
+ dtype('int64')
 
  >>> SparseDtype(object, 1)._subtype_with_str
  dtype('O')

pandas/core/base.py

@@ -1213,10 +1213,10 @@ def factorize(
  array([0, 3])
 
  >>> ser.searchsorted([1, 3], side='left')
- array([0, 2], dtype=int64)
+ array([0, 2])
 
  >>> ser.searchsorted([1, 3], side='right')
- array([1, 3], dtype=int64)
+ array([1, 3])
 
  >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000']))
  >>> ser

pandas/core/construction.py

@@ -439,7 +439,7 @@ def extract_array(
  To extract all the way down to the ndarray, pass ``extract_numpy=True``.
 
  >>> extract_array(pd.Series([1, 2, 3]), extract_numpy=True)
- array([1, 2, 3], dtype=int64)
+ array([1, 2, 3])
  """
  if isinstance(obj, (ABCIndex, ABCSeries)):
  if isinstance(obj, ABCRangeIndex):

pandas/core/dtypes/cast.py

@@ -884,7 +884,7 @@ def infer_dtype_from_array(
  Examples
  --------
  >>> np.asarray([1, '1'])
- array(['1', '1'], dtype='<U11')
+ array(['1', '1'], dtype='<U21')
 
  >>> infer_dtype_from_array([1, '1'])
  (dtype('O'), [1, '1'])

pandas/core/dtypes/dtypes.py

@@ -669,7 +669,7 @@ class DatetimeTZDtype(PandasExtensionDtype):
  datetime64[ns, UTC]
 
  >>> pd.DatetimeTZDtype(tz='dateutil/US/Central')
- datetime64[ns, tzfile('US/Central')]
+ datetime64[ns, tzfile('/usr/share/zoneinfo/US/Central')]
  """
 
  type: type[Timestamp] = Timestamp

pandas/core/groupby/grouper.py

@@ -203,7 +203,7 @@ class Grouper:
  2000-10-02 00:12:00 18
  2000-10-02 00:19:00 21
  2000-10-02 00:26:00 24
- Freq: 7T, dtype: int32
+ Freq: 7T, dtype: int64
 
  >>> ts.groupby(pd.Grouper(freq='17min')).sum()
  2000-10-01 23:14:00 0