diff --git a/asv_bench/benchmarks/indexing_engines.py b/asv_bench/benchmarks/indexing_engines.py
index 5e3c593e269cb..da0e7de585391 100644
--- a/asv_bench/benchmarks/indexing_engines.py
+++ b/asv_bench/benchmarks/indexing_engines.py
@@ -67,6 +67,14 @@ class NumericEngineIndexing:
def setup(self, engine_and_dtype, index_type, unique, N):
engine, dtype = engine_and_dtype
+ if (
+ index_type == "non_monotonic"
+ and dtype in [np.int16, np.int8, np.uint8]
+ and unique
+ ):
+ # Values overflow
+ raise NotImplementedError
+
if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
@@ -115,6 +123,14 @@ def setup(self, engine_and_dtype, index_type, unique, N):
engine, dtype = engine_and_dtype
dtype = dtype.lower()
+ if (
+ index_type == "non_monotonic"
+ and dtype in ["int16", "int8", "uint8"]
+ and unique
+ ):
+ # Values overflow
+ raise NotImplementedError
+
if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
diff --git a/doc/source/getting_started/comparison/comparison_with_r.rst b/doc/source/getting_started/comparison/comparison_with_r.rst
index d9d7d916b0238..cc7add87b5935 100644
--- a/doc/source/getting_started/comparison/comparison_with_r.rst
+++ b/doc/source/getting_started/comparison/comparison_with_r.rst
@@ -383,7 +383,7 @@ In Python, since ``a`` is a list, you can simply use list comprehension.
.. ipython:: python
- a = np.array(list(range(1, 24)) + [np.NAN]).reshape(2, 3, 4)
+ a = np.array(list(range(1, 24)) + [np.nan]).reshape(2, 3, 4)
pd.DataFrame([tuple(list(x) + [val]) for x, val in np.ndenumerate(a)])
meltlist
@@ -402,7 +402,7 @@ In Python, this list would be a list of tuples, so
.. ipython:: python
- a = list(enumerate(list(range(1, 5)) + [np.NAN]))
+ a = list(enumerate(list(range(1, 5)) + [np.nan]))
pd.DataFrame(a)
For more details and examples see :ref:`the Intro to Data Structures
diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index ffd7a2ad7bb01..d8ef9899a07c9 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -2064,12 +2064,12 @@ different numeric dtypes will **NOT** be combined. The following example will gi
.. ipython:: python
- df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
+ df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float64")
df1
df1.dtypes
df2 = pd.DataFrame(
{
- "A": pd.Series(np.random.randn(8), dtype="float16"),
+ "A": pd.Series(np.random.randn(8), dtype="float32"),
"B": pd.Series(np.random.randn(8)),
"C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"), # [0,255] (range of uint8)
}
diff --git a/doc/source/user_guide/enhancingperf.rst b/doc/source/user_guide/enhancingperf.rst
index e55a6cda47ac2..647b0f760f4d4 100644
--- a/doc/source/user_guide/enhancingperf.rst
+++ b/doc/source/user_guide/enhancingperf.rst
@@ -171,6 +171,7 @@ can be improved by passing an ``np.ndarray``.
In [4]: %%cython
...: cimport numpy as np
...: import numpy as np
+ ...: np.import_array()
...: cdef double f_typed(double x) except? -2:
...: return x * (x - 1)
...: cpdef double integrate_f_typed(double a, double b, int N):
@@ -225,6 +226,7 @@ and ``wraparound`` checks can yield more performance.
...: cimport cython
...: cimport numpy as np
...: import numpy as np
+ ...: np.import_array()
...: cdef np.float64_t f_typed(np.float64_t x) except? -2:
...: return x * (x - 1)
...: cpdef np.float64_t integrate_f_typed(np.float64_t a, np.float64_t b, np.int64_t N):
diff --git a/doc/source/whatsnew/v0.11.0.rst b/doc/source/whatsnew/v0.11.0.rst
index dcb0d3229aa5d..28c9d46f21fd8 100644
--- a/doc/source/whatsnew/v0.11.0.rst
+++ b/doc/source/whatsnew/v0.11.0.rst
@@ -74,10 +74,10 @@ Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passe
.. ipython:: python
- df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
+ df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float64')
df1
df1.dtypes
- df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float16'),
+ df2 = pd.DataFrame({'A': pd.Series(np.random.randn(8), dtype='float32'),
'B': pd.Series(np.random.randn(8)),
'C': pd.Series(range(8), dtype='uint8')})
df2
diff --git a/environment.yml b/environment.yml
index a8c8b20e20fe4..ca8f1996c61cf 100644
--- a/environment.yml
+++ b/environment.yml
@@ -23,7 +23,7 @@ dependencies:
# required dependencies
- python-dateutil
- - numpy<2
+ - numpy<3
# optional dependencies
- beautifulsoup4>=4.11.2
diff --git a/pandas/compat/numpy/__init__.py b/pandas/compat/numpy/__init__.py
index 3306b36d71806..e95b44c879940 100644
--- a/pandas/compat/numpy/__init__.py
+++ b/pandas/compat/numpy/__init__.py
@@ -36,8 +36,8 @@
r".*In the future `np\.long` will be defined as.*",
FutureWarning,
)
- np_long = np.long # type: ignore[attr-defined]
- np_ulong = np.ulong # type: ignore[attr-defined]
+ np_long = np.long
+ np_ulong = np.ulong
except AttributeError:
np_long = np.int_
np_ulong = np.uint
diff --git a/pandas/core/accessor.py b/pandas/core/accessor.py
index 78684eacf2d66..0331c26c805b6 100644
--- a/pandas/core/accessor.py
+++ b/pandas/core/accessor.py
@@ -351,7 +351,7 @@ def register_dataframe_accessor(name: str) -> Callable[[TypeT], TypeT]:
AttributeError: The series must contain integer data only.
>>> df = pd.Series([1, 2, 3])
>>> df.int_accessor.sum()
-6"""
+np.int64(6)"""
@doc(_register_accessor, klass="Series", examples=_register_series_examples)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index dad38abccf4ee..42be07e03bad8 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -941,7 +941,7 @@ def argmin(self, skipna: bool = True) -> int:
--------
>>> arr = pd.array([3, 1, 2, 5, 4])
>>> arr.argmin()
- 1
+ np.int64(1)
"""
# Implementer note: You have two places to override the behavior of
# argmin.
@@ -975,7 +975,7 @@ def argmax(self, skipna: bool = True) -> int:
--------
>>> arr = pd.array([3, 1, 2, 5, 4])
>>> arr.argmax()
- 3
+ np.int64(3)
"""
# Implementer note: You have two places to override the behavior of
# argmax.
@@ -1959,10 +1959,10 @@ def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
--------
>>> class MyExtensionArray(pd.arrays.NumpyExtensionArray):
... def _formatter(self, boxed=False):
- ... return lambda x: "*" + str(x) + "*" if boxed else repr(x) + "*"
+ ... return lambda x: "*" + str(x) + "*"
>>> MyExtensionArray(np.array([1, 2, 3, 4]))
<MyExtensionArray>
- [1*, 2*, 3*, 4*]
+ [*1*, *2*, *3*, *4*]
Length: 4, dtype: int64
"""
if boxed:
@@ -2176,15 +2176,15 @@ def _reduce(
Examples
--------
>>> pd.array([1, 2, 3])._reduce("min")
- 1
+ np.int64(1)
>>> pd.array([1, 2, 3])._reduce("max")
- 3
+ np.int64(3)
>>> pd.array([1, 2, 3])._reduce("sum")
- 6
+ np.int64(6)
>>> pd.array([1, 2, 3])._reduce("mean")
- 2.0
+ np.float64(2.0)
>>> pd.array([1, 2, 3])._reduce("median")
- 2.0
+ np.float64(2.0)
"""
meth = getattr(self, name, None)
if meth is None:
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index eba738c926497..b27bf19f2f593 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -275,7 +275,7 @@ def _unbox_scalar(
--------
>>> arr = pd.array(np.array(["1970-01-01"], "datetime64[ns]"))
>>> arr._unbox_scalar(arr[0])
- numpy.datetime64('1970-01-01T00:00:00.000000000')
+ np.datetime64('1970-01-01T00:00:00.000000000')
"""
raise AbstractMethodError(self)
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 0bf2089df5f85..6cb79e915c78b 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1775,7 +1775,8 @@ def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
[(0, 1], (1, 2]]
Length: 2, dtype: interval[int64, right]
>>> idx.to_tuples()
- array([(0, 1), (1, 2)], dtype=object)
+ array([(np.int64(0), np.int64(1)), (np.int64(1), np.int64(2))],
+ dtype=object)
For :class:`pandas.IntervalIndex`:
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 708a3818bcbb7..07c875337e4f6 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -1378,25 +1378,25 @@ def any(
skips NAs):
>>> pd.array([True, False, True]).any()
- True
+ np.True_
>>> pd.array([True, False, pd.NA]).any()
- True
+ np.True_
>>> pd.array([False, False, pd.NA]).any()
- False
+ np.False_
>>> pd.array([], dtype="boolean").any()
- False
+ np.False_
>>> pd.array([pd.NA], dtype="boolean").any()
- False
+ np.False_
>>> pd.array([pd.NA], dtype="Float64").any()
- False
+ np.False_
With ``skipna=False``, the result can be NA if this is logically
required (whether ``pd.NA`` is True or False influences the result):
>>> pd.array([True, False, pd.NA]).any(skipna=False)
- True
+ np.True_
>>> pd.array([1, 0, pd.NA]).any(skipna=False)
- True
+ np.True_
>>> pd.array([False, False, pd.NA]).any(skipna=False)
<NA>
>>> pd.array([0, 0, pd.NA]).any(skipna=False)
@@ -1466,17 +1466,17 @@ def all(
skips NAs):
>>> pd.array([True, True, pd.NA]).all()
- True
+ np.True_
>>> pd.array([1, 1, pd.NA]).all()
- True
+ np.True_
>>> pd.array([True, False, pd.NA]).all()
- False
+ np.False_
>>> pd.array([], dtype="boolean").all()
- True
+ np.True_
>>> pd.array([pd.NA], dtype="boolean").all()
- True
+ np.True_
>>> pd.array([pd.NA], dtype="Float64").all()
- True
+ np.True_
With ``skipna=False``, the result can be NA if this is logically
required (whether ``pd.NA`` is True or False influences the result):
@@ -1486,9 +1486,9 @@ def all(
>>> pd.array([1, 1, pd.NA]).all(skipna=False)
<NA>
>>> pd.array([True, False, pd.NA]).all(skipna=False)
- False
+ np.False_
>>> pd.array([1, 0, pd.NA]).all(skipna=False)
- False
+ np.False_
"""
nv.validate_all((), kwargs)
diff --git a/pandas/core/arrays/sparse/accessor.py b/pandas/core/arrays/sparse/accessor.py
index eab8527eef526..7dde03b30cd6a 100644
--- a/pandas/core/arrays/sparse/accessor.py
+++ b/pandas/core/arrays/sparse/accessor.py
@@ -297,7 +297,7 @@ class SparseFrameAccessor(BaseAccessor, PandasDelegate):
--------
>>> df = pd.DataFrame({"a": [1, 2, 0, 0], "b": [3, 0, 0, 4]}, dtype="Sparse[int]")
>>> df.sparse.density
- 0.5
+ np.float64(0.5)
"""
def _validate(self, data) -> None:
@@ -459,7 +459,7 @@ def density(self) -> float:
--------
>>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0, 1])})
>>> df.sparse.density
- 0.5
+ np.float64(0.5)
"""
tmp = np.mean([column.array.density for _, column in self._parent.items()])
return tmp
diff --git a/pandas/core/base.py b/pandas/core/base.py
index a64cd8633c1db..ea7e99f6f1879 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -804,9 +804,9 @@ def argmax(
dtype: float64
>>> s.argmax()
- 2
+ np.int64(2)
>>> s.argmin()
- 0
+ np.int64(0)
The maximum cereal calories is the third element and
the minimum cereal calories is the first element,
@@ -1360,7 +1360,7 @@ def factorize(
dtype: int64
>>> ser.searchsorted(4)
- 3
+ np.int64(3)
>>> ser.searchsorted([0, 4])
array([0, 3])
@@ -1379,7 +1379,7 @@ def factorize(
dtype: datetime64[s]
>>> ser.searchsorted('3/14/2000')
- 3
+ np.int64(3)
>>> ser = pd.Categorical(
... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True
@@ -1389,7 +1389,7 @@ def factorize(
Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
>>> ser.searchsorted('bread')
- 1
+ np.int64(1)
>>> ser.searchsorted(['bread'], side='right')
array([3])
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 100ad312bd839..75f8a56aac5db 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -246,7 +246,8 @@ def asarray_tuplesafe(values: Iterable, dtype: NpDtype | None = None) -> ArrayLi
with warnings.catch_warnings():
# Can remove warning filter once NumPy 1.24 is min version
if not np_version_gte1p24:
- warnings.simplefilter("ignore", np.VisibleDeprecationWarning)
+ # np.VisibleDeprecationWarning only in np.exceptions in 2.0
+ warnings.simplefilter("ignore", np.VisibleDeprecationWarning) # type: ignore[attr-defined]
result = np.asarray(values, dtype=dtype)
except ValueError:
# Using try/except since it's more performant than checking is_list_like
diff --git a/pandas/core/dtypes/missing.py b/pandas/core/dtypes/missing.py
index f20ca44728664..71fe0f6e4feb0 100644
--- a/pandas/core/dtypes/missing.py
+++ b/pandas/core/dtypes/missing.py
@@ -428,9 +428,9 @@ def array_equivalent(
Examples
--------
>>> array_equivalent(np.array([1, 2, np.nan]), np.array([1, 2, np.nan]))
- True
+ np.True_
>>> array_equivalent(np.array([1, np.nan, 2]), np.array([1, 2, np.nan]))
- False
+ np.False_
"""
left, right = np.asarray(left), np.asarray(right)
@@ -626,7 +626,7 @@ def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
>>> na_value_for_dtype(np.dtype("bool"))
False
>>> na_value_for_dtype(np.dtype("datetime64[ns]"))
- numpy.datetime64('NaT')
+ np.datetime64('NaT')
"""
if isinstance(dtype, ExtensionDtype):
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 0c3f535df9ce2..027f78f985953 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -887,7 +887,7 @@ def squeeze(self, axis: Axis | None = None) -> Scalar | Series | DataFrame:
dtype: int64
>>> even_primes.squeeze()
- 2
+ np.int64(2)
Squeezing objects with more than one value in every axis does nothing:
@@ -945,7 +945,7 @@ def squeeze(self, axis: Axis | None = None) -> Scalar | Series | DataFrame:
Squeezing all axes will project directly into a scalar:
>>> df_0a.squeeze()
- 1
+ np.int64(1)
"""
axes = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
result = self.iloc[
@@ -7954,7 +7954,7 @@ def asof(self, where, subset=None):
dtype: float64
>>> s.asof(20)
- 2.0
+ np.float64(2.0)
For a sequence `where`, a Series is returned. The first value is
NaN, because the first element of `where` is before the first
@@ -7969,7 +7969,7 @@ def asof(self, where, subset=None):
NaN, even though NaN is at the index location for ``30``.
>>> s.asof(30)
- 2.0
+ np.float64(2.0)
Take all columns into consideration
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index d0c0ed29b6d44..f9438b348c140 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -546,7 +546,8 @@ def groups(self) -> dict[Hashable, Index]:
2023-02-15 4
dtype: int64
>>> ser.resample("MS").groups
- {Timestamp('2023-01-01 00:00:00'): 2, Timestamp('2023-02-01 00:00:00'): 4}
+ {Timestamp('2023-01-01 00:00:00'): np.int64(2),
+ Timestamp('2023-02-01 00:00:00'): np.int64(4)}
"""
if isinstance(self.keys, list) and len(self.keys) == 1:
warnings.warn(
@@ -613,7 +614,7 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
toucan 1 5 6
eagle 7 8 9
>>> df.groupby(by=["a"]).indices
- {1: array([0, 1]), 7: array([2])}
+ {np.int64(1): array([0, 1]), np.int64(7): array([2])}
For Resampler:
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index bcb27d0320c91..bbbcc4da9fb39 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -265,7 +265,7 @@ def iloc(self) -> _iLocIndexer:
With scalar integers.
>>> df.iloc[0, 1]
- 2
+ np.int64(2)
With lists of integers.
@@ -375,7 +375,7 @@ def loc(self) -> _LocIndexer:
Single label for row and column
>>> df.loc["cobra", "shield"]
- 2
+ np.int64(2)
Slice with labels for row and single label for column. As mentioned
above, note that both the start and stop of the slice are included.
@@ -585,7 +585,7 @@ def loc(self) -> _LocIndexer:
Single tuple for the index with a single label for the column
>>> df.loc[("cobra", "mark i"), "shield"]
- 2
+ np.int64(2)
Slice from index tuple to single label
@@ -666,18 +666,18 @@ def at(self) -> _AtIndexer:
Get value at specified row/column pair
>>> df.at[4, "B"]
- 2
+ np.int64(2)
Set value at specified row/column pair
>>> df.at[4, "B"] = 10
>>> df.at[4, "B"]
- 10
+ np.int64(10)
Get value within a Series
>>> df.loc[5].at["B"]
- 4
+ np.int64(4)
"""
return _AtIndexer("at", self)
@@ -715,18 +715,18 @@ def iat(self) -> _iAtIndexer:
Get value at specified row/column pair
>>> df.iat[1, 2]
- 1
+ np.int64(1)
Set value at specified row/column pair
>>> df.iat[1, 2] = 10
>>> df.iat[1, 2]
- 10
+ np.int64(10)
Get value within a series
>>> df.loc[0].iat[1]
- 2
+ np.int64(2)
"""
return _iAtIndexer("iat", self)
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index d1dc0ff809497..25fb6e6181082 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -508,12 +508,12 @@ def nanany(
>>> from pandas.core import nanops
>>> s = pd.Series([1, 2])
>>> nanops.nanany(s.values)
- True
+ np.True_
>>> from pandas.core import nanops
>>> s = pd.Series([np.nan])
>>> nanops.nanany(s.values)
- False
+ np.False_
"""
if values.dtype.kind in "iub" and mask is None:
# GH#26032 fastpath
@@ -564,12 +564,12 @@ def nanall(
>>> from pandas.core import nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanall(s.values)
- True
+ np.True_
>>> from pandas.core import nanops
>>> s = pd.Series([1, 0])
>>> nanops.nanall(s.values)
- False
+ np.False_
"""
if values.dtype.kind in "iub" and mask is None:
# GH#26032 fastpath
@@ -625,7 +625,7 @@ def nansum(
>>> from pandas.core import nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nansum(s.values)
- 3.0
+ np.float64(3.0)
"""
dtype = values.dtype
values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
@@ -691,7 +691,7 @@ def nanmean(
>>> from pandas.core import nanops
>>> s = pd.Series([1, 2, np.nan])
>>> nanops.nanmean(s.values)
- 1.5
+ np.float64(1.5)
"""
dtype = values.dtype
values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
@@ -1061,7 +1061,7 @@ def nansem(
>>> from pandas.core import nanops
>>> s = pd.Series([1, np.nan, 2, 3])
>>> nanops.nansem(s.values)
- 0.5773502691896258
+ np.float64(0.5773502691896258)
"""
# This checks if non-numeric-like data is passed with numeric_only=False
# and raises a TypeError otherwise
@@ -1136,7 +1136,7 @@ def nanargmax(
>>> from pandas.core import nanops
>>> arr = np.array([1, 2, 3, np.nan, 4])
>>> nanops.nanargmax(arr)
- 4
+ np.int64(4)
>>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
>>> arr[2:, 2] = np.nan
@@ -1182,7 +1182,7 @@ def nanargmin(
>>> from pandas.core import nanops
>>> arr = np.array([1, 2, 3, np.nan, 4])
>>> nanops.nanargmin(arr)
- 0
+ np.int64(0)
>>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
>>> arr[2:, 0] = np.nan
@@ -1237,7 +1237,7 @@ def nanskew(
>>> from pandas.core import nanops
>>> s = pd.Series([1, np.nan, 1, 2])
>>> nanops.nanskew(s.values)
- 1.7320508075688787
+ np.float64(1.7320508075688787)
"""
mask = _maybe_get_mask(values, skipna, mask)
if values.dtype.kind != "f":
@@ -1325,7 +1325,7 @@ def nankurt(
>>> from pandas.core import nanops
>>> s = pd.Series([1, np.nan, 1, 3, 2])
>>> nanops.nankurt(s.values)
- -1.2892561983471076
+ np.float64(-1.2892561983471076)
"""
mask = _maybe_get_mask(values, skipna, mask)
if values.dtype.kind != "f":
@@ -1417,7 +1417,7 @@ def nanprod(
>>> from pandas.core import nanops
>>> s = pd.Series([1, 2, 3, np.nan])
>>> nanops.nanprod(s.values)
- 6.0
+ np.float64(6.0)
"""
mask = _maybe_get_mask(values, skipna, mask)
diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py
index 0e0fb23d924bc..75ff40f1eca90 100644
--- a/pandas/plotting/_misc.py
+++ b/pandas/plotting/_misc.py
@@ -68,7 +68,7 @@ def table(ax: Axes, data: DataFrame | Series, **kwargs) -> Table:
>>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
>>> fig, ax = plt.subplots()
>>> ax.axis("off")
- (0.0, 1.0, 0.0, 1.0)
+ (np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))
>>> table = pd.plotting.table(
... ax, df, loc="center", cellLoc="center", colWidths=[0.2, 0.2]
... )
diff --git a/pandas/tests/extension/date/array.py b/pandas/tests/extension/date/array.py
index 2306f5974ba18..0c51570189a7c 100644
--- a/pandas/tests/extension/date/array.py
+++ b/pandas/tests/extension/date/array.py
@@ -113,7 +113,7 @@ def __init__(
# error: "object_" object is not iterable
obj = np.char.split(dates, sep="-")
- for (i,), (y, m, d) in np.ndenumerate(obj): # type: ignore[misc]
+ for (i,), (y, m, d) in np.ndenumerate(obj):
self._year[i] = int(y)
self._month[i] = int(m)
self._day[i] = int(d)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 990901958cd9e..20fc21be75a06 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -14,7 +14,7 @@ pytest-localserver
PyQt5>=5.15.9
coverage
python-dateutil
-numpy<2
+numpy<3
beautifulsoup4>=4.11.2
blosc
bottleneck>=1.3.6