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DOC: fix doctests for pandas/core/base.py for new string dtype #61905

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Jul 22, 2025
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4 changes: 2 additions & 2 deletions pandas/core/algorithms.py
Original file line number Diff line number Diff line change
Expand Up @@ -751,7 +751,7 @@ def factorize(
array([0, 0, 1])
>>> uniques
['a', 'c']
Categories (3, object): ['a', 'b', 'c']
Categories (3, str): [a, b, c]
Notice that ``'b'`` is in ``uniques.categories``, despite not being
present in ``cat.values``.
Expand All @@ -764,7 +764,7 @@ def factorize(
>>> codes
array([0, 0, 1])
>>> uniques
Index(['a', 'c'], dtype='object')
Index(['a', 'c'], dtype='str')
If NaN is in the values, and we want to include NaN in the uniques of the
values, it can be achieved by setting ``use_na_sentinel=False``.
Expand Down
20 changes: 10 additions & 10 deletions pandas/core/base.py
Original file line number Diff line number Diff line change
Expand Up @@ -323,12 +323,12 @@ def transpose(self, *args, **kwargs) -> Self:
0 Ant
1 Bear
2 Cow
dtype: object
dtype: str
>>> s.T
0 Ant
1 Bear
2 Cow
dtype: object
dtype: str

For Index:

Expand Down Expand Up @@ -383,7 +383,7 @@ def ndim(self) -> int:
0 Ant
1 Bear
2 Cow
dtype: object
dtype: str
>>> s.ndim
1

Expand Down Expand Up @@ -452,9 +452,9 @@ def nbytes(self) -> int:
0 Ant
1 Bear
2 Cow
dtype: object
dtype: str
>>> s.nbytes
24
34

For Index:

Expand Down Expand Up @@ -487,7 +487,7 @@ def size(self) -> int:
0 Ant
1 Bear
2 Cow
dtype: object
dtype: str
>>> s.size
3

Expand Down Expand Up @@ -567,7 +567,7 @@ def array(self) -> ExtensionArray:
>>> ser = pd.Series(pd.Categorical(["a", "b", "a"]))
>>> ser.array
['a', 'b', 'a']
Categories (2, object): ['a', 'b']
Categories (2, str): [a, b]
"""
raise AbstractMethodError(self)

Expand Down Expand Up @@ -1076,15 +1076,15 @@ def value_counts(

>>> df.dtypes
a category
b object
b str
c category
d category
dtype: object

>>> df.dtypes.value_counts()
category 2
category 1
object 1
str 1
Name: count, dtype: int64
"""
return algorithms.value_counts_internal(
Expand Down Expand Up @@ -1386,7 +1386,7 @@ def factorize(
... )
>>> ser
['apple', 'bread', 'bread', 'cheese', 'milk']
Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
Categories (4, str): [apple < bread < cheese < milk]

>>> ser.searchsorted('bread')
np.int64(1)
Expand Down
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