From 658c4d5c55279093accec07ceed1fbe20d361c1b Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 22 Feb 2025 16:22:18 -0500
Subject: [PATCH 01/13] MNT: Bump dev pin on NumPy
---
environment.yml | 2 +-
requirements-dev.txt | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/environment.yml b/environment.yml
index 69647a436e3ad..194611346d8de 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/requirements-dev.txt b/requirements-dev.txt
index fb4d9cdb589ca..8e370c37a9c9b 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
From e424a9681024566dc85a1e99b469e10dc2ce46ad Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 1 Mar 2025 15:05:43 -0500
Subject: [PATCH 02/13] Fix type-hints
---
pandas/compat/numpy/__init__.py | 4 ++--
pandas/core/common.py | 3 ++-
pandas/core/internals/managers.py | 7 ++++++-
pandas/tests/extension/date/array.py | 2 +-
4 files changed, 11 insertions(+), 5 deletions(-)
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/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/internals/managers.py b/pandas/core/internals/managers.py
index a3738bb25f56c..2e6701916a8d4 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -572,7 +572,12 @@ def setitem(self, indexer, value) -> Self:
0, blk_loc, values
)
# first block equals values
- self.blocks[0].setitem((indexer[0], np.arange(len(blk_loc))), value)
+ col_indexer: slice | np.ndarray
+ if isinstance(indexer[1], slice) and indexer[1] == slice(None):
+ col_indexer = slice(None)
+ else:
+ col_indexer = np.arange(len(blk_loc))
+ self.blocks[0].setitem((indexer[0], col_indexer), value)
return self
# No need to split if we either set all columns or on a single block
# manager
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)
From e75abfecd1f0b739a2c66ffeb0df81443f82e096 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 1 Mar 2025 15:07:48 -0500
Subject: [PATCH 03/13] Docs fixup
---
doc/source/getting_started/comparison/comparison_with_r.rst | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
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
From 856a52f22124795f74de3e4070e533d129c6a4b8 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 1 Mar 2025 17:20:09 -0500
Subject: [PATCH 04/13] fixups
---
doc/source/user_guide/basics.rst | 1 +
doc/source/user_guide/enhancingperf.rst | 2 ++
doc/source/whatsnew/v0.11.0.rst | 1 +
3 files changed, 4 insertions(+)
diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index ffd7a2ad7bb01..ce5d16c604136 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -2063,6 +2063,7 @@ or a passed ``Series``), then it will be preserved in DataFrame operations. Furt
different numeric dtypes will **NOT** be combined. The following example will give you a taste.
.. ipython:: python
+ :okwarning:
df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
df1
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..73228513bfffc 100644
--- a/doc/source/whatsnew/v0.11.0.rst
+++ b/doc/source/whatsnew/v0.11.0.rst
@@ -73,6 +73,7 @@ Dtypes
Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.
.. ipython:: python
+ :okwarning:
df1 = pd.DataFrame(np.random.randn(8, 1), columns=['A'], dtype='float32')
df1
From a8304b6d01521e53a7dc9c0aaca3974f8def3379 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 1 Mar 2025 17:31:24 -0500
Subject: [PATCH 05/13] Fixup
---
asv_bench/benchmarks/indexing_engines.py | 4 ++++
1 file changed, 4 insertions(+)
diff --git a/asv_bench/benchmarks/indexing_engines.py b/asv_bench/benchmarks/indexing_engines.py
index 5e3c593e269cb..c65124063b2f2 100644
--- a/asv_bench/benchmarks/indexing_engines.py
+++ b/asv_bench/benchmarks/indexing_engines.py
@@ -67,6 +67,10 @@ 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 ["int16", "int8", "uint8"]:
+ # Values overflow
+ raise NotImplementedError
+
if index_type == "monotonic_incr":
if unique:
arr = np.arange(N * 3, dtype=dtype)
From 41265f3a54c7c77b024d8e42df50bf4f16c3bf51 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 1 Mar 2025 17:33:31 -0500
Subject: [PATCH 06/13] Fixup
---
pandas/core/internals/managers.py | 7 +------
1 file changed, 1 insertion(+), 6 deletions(-)
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 2e6701916a8d4..a3738bb25f56c 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -572,12 +572,7 @@ def setitem(self, indexer, value) -> Self:
0, blk_loc, values
)
# first block equals values
- col_indexer: slice | np.ndarray
- if isinstance(indexer[1], slice) and indexer[1] == slice(None):
- col_indexer = slice(None)
- else:
- col_indexer = np.arange(len(blk_loc))
- self.blocks[0].setitem((indexer[0], col_indexer), value)
+ self.blocks[0].setitem((indexer[0], np.arange(len(blk_loc))), value)
return self
# No need to split if we either set all columns or on a single block
# manager
From f6a2330a2a2c4e1d8e6df0be2588dec72632004c Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 6 Mar 2025 17:33:49 -0500
Subject: [PATCH 07/13] doctest fixups
---
asv_bench/benchmarks/indexing_engines.py | 14 +++++++++++++-
pandas/core/accessor.py | 2 +-
pandas/core/algorithms.py | 2 +-
pandas/core/arrays/base.py | 10 +++++-----
pandas/core/arrays/datetimelike.py | 2 +-
pandas/core/arrays/interval.py | 3 ++-
pandas/core/arrays/masked.py | 12 ++++++------
pandas/core/arrays/numpy_.py | 2 +-
pandas/core/arrays/sparse/accessor.py | 4 ++--
pandas/core/base.py | 8 ++++----
pandas/core/construction.py | 2 +-
pandas/core/dtypes/missing.py | 6 +++---
pandas/core/generic.py | 4 ++--
pandas/core/groupby/groupby.py | 5 +++--
pandas/core/indexing.py | 16 ++++++++--------
pandas/core/nanops.py | 24 ++++++++++++------------
pandas/plotting/_misc.py | 2 +-
17 files changed, 66 insertions(+), 52 deletions(-)
diff --git a/asv_bench/benchmarks/indexing_engines.py b/asv_bench/benchmarks/indexing_engines.py
index c65124063b2f2..da0e7de585391 100644
--- a/asv_bench/benchmarks/indexing_engines.py
+++ b/asv_bench/benchmarks/indexing_engines.py
@@ -67,7 +67,11 @@ 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 ["int16", "int8", "uint8"]:
+ if (
+ index_type == "non_monotonic"
+ and dtype in [np.int16, np.int8, np.uint8]
+ and unique
+ ):
# Values overflow
raise NotImplementedError
@@ -119,6 +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/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/algorithms.py b/pandas/core/algorithms.py
index aafd802b827a5..77ec4286f05ee 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -415,7 +415,7 @@ def unique(values):
>>> pd.unique(pd.array([1 + 1j, 2, 3]))
<NumpyExtensionArray>
- [(1+1j), (2+0j), (3+0j)]
+ [np.complex128(1+1j), np.complex128(2+0j), np.complex128(3+0j)]
Length: 3, dtype: complex128
"""
return unique_with_mask(values)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 33745438e2aea..037abb3ff2ee8 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -1072,7 +1072,7 @@ def interpolate(
... limit_area="inside",
... )
<NumpyExtensionArray>
- [0.0, 1.0, 2.0, 3.0]
+ [np.float64(0.0), np.float64(1.0), np.float64(2.0), np.float64(3.0)]
Length: 4, dtype: float64
Interpolating values in a FloatingArray:
@@ -1962,7 +1962,7 @@ def _formatter(self, boxed: bool = False) -> Callable[[Any], str | None]:
... return lambda x: "*" + str(x) + "*" if boxed else repr(x) + "*"
>>> MyExtensionArray(np.array([1, 2, 3, 4]))
<MyExtensionArray>
- [1*, 2*, 3*, 4*]
+ [np.int64(1)*, np.int64(2)*, np.int64(3)*, np.int64(4)*]
Length: 4, dtype: int64
"""
if boxed:
@@ -2176,11 +2176,11 @@ 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
>>> pd.array([1, 2, 3])._reduce("median")
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 8a79ab53442c3..762d921fb5013 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..ae7362a5babef 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 f3a0cc0dccdb3..e5872368049aa 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -1470,17 +1470,17 @@ 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):
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index ac0823ed903b3..ab0029c3ae2ca 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -80,7 +80,7 @@ class NumpyExtensionArray( # type: ignore[misc]
--------
>>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
<NumpyExtensionArray>
- [0, 1, 2, 3]
+ [np.int64(0), np.int64(1), np.int64(2), np.int64(3)]
Length: 4, dtype: int64
"""
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..a7ae5e2f5301b 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -558,7 +558,7 @@ def array(self) -> ExtensionArray:
>>> pd.Series([1, 2, 3]).array
<NumpyExtensionArray>
- [1, 2, 3]
+ [np.int64(1), np.int64(2), np.int64(3)]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
@@ -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])
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index ada492787a179..a7f301fbb16af 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -177,7 +177,7 @@ def array(
>>> pd.array(["a", "b"], dtype=str)
<NumpyExtensionArray>
- ['a', 'b']
+ [np.str_('a'), np.str_('b')]
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
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..84e4397151d09 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:
@@ -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
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..2d872c72746bc 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.
@@ -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(5)
"""
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]
... )
From 83e26441c8260cb5d6a72134f741cbdf21994438 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Fri, 7 Mar 2025 18:03:39 -0500
Subject: [PATCH 08/13] More doc fixes
---
pandas/core/arrays/base.py | 8 ++++----
pandas/core/arrays/masked.py | 20 ++++++++++----------
pandas/core/base.py | 2 +-
pandas/core/construction.py | 2 +-
pandas/core/generic.py | 4 ++--
pandas/core/indexing.py | 4 ++--
6 files changed, 20 insertions(+), 20 deletions(-)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 037abb3ff2ee8..ca10bcfaf1ce6 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.
@@ -2182,9 +2182,9 @@ def _reduce(
>>> pd.array([1, 2, 3])._reduce("sum")
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/masked.py b/pandas/core/arrays/masked.py
index e5872368049aa..5e5ab2bb2d3fd 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -1382,25 +1382,25 @@ 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)
@@ -1490,9 +1490,9 @@ 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/base.py b/pandas/core/base.py
index a7ae5e2f5301b..780761003458c 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -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
diff --git a/pandas/core/construction.py b/pandas/core/construction.py
index a7f301fbb16af..72456ac3e1abc 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -186,7 +186,7 @@ def array(
>>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
<NumpyExtensionArray>
- ['a', 'b']
+ [np.str_('a'), np.str_('b')]
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 84e4397151d09..027f78f985953 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -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[
@@ -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/indexing.py b/pandas/core/indexing.py
index 2d872c72746bc..bbbcc4da9fb39 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -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
@@ -677,7 +677,7 @@ def at(self) -> _AtIndexer:
Get value within a Series
>>> df.loc[5].at["B"]
- np.int64(5)
+ np.int64(4)
"""
return _AtIndexer("at", self)
From f7ef882505cd09a0314dae63ba90176e6042d0b1 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Wed, 19 Mar 2025 15:04:44 -0400
Subject: [PATCH 09/13] Some reverts
---
pandas/core/algorithms.py | 2 +-
pandas/core/arrays/base.py | 2 +-
pandas/core/arrays/interval.py | 2 +-
pandas/core/arrays/numpy_.py | 2 +-
pandas/core/base.py | 4 ++--
pandas/core/construction.py | 4 ++--
6 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 7a92b152b8886..76f2fdad591ff 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -415,7 +415,7 @@ def unique(values):
>>> pd.unique(pd.array([1 + 1j, 2, 3]))
<NumpyExtensionArray>
- [np.complex128(1+1j), np.complex128(2+0j), np.complex128(3+0j)]
+ [(1+1j), (2+0j), (3+0j)]
Length: 3, dtype: complex128
"""
return unique_with_mask(values)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index e4926b32fe48d..bc5a5f6d68d95 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -1072,7 +1072,7 @@ def interpolate(
... limit_area="inside",
... )
<NumpyExtensionArray>
- [np.float64(0.0), np.float64(1.0), np.float64(2.0), np.float64(3.0)]
+ [0.0, 1.0, 2.0, 3.0]
Length: 4, dtype: float64
Interpolating values in a FloatingArray:
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index ae7362a5babef..6cb79e915c78b 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1776,7 +1776,7 @@ def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
Length: 2, dtype: interval[int64, right]
>>> idx.to_tuples()
array([(np.int64(0), np.int64(1)), (np.int64(1), np.int64(2))],
- dtype=object)
+ dtype=object)
For :class:`pandas.IntervalIndex`:
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
index b00bea160fe69..fd2c8c9d63362 100644
--- a/pandas/core/arrays/numpy_.py
+++ b/pandas/core/arrays/numpy_.py
@@ -83,7 +83,7 @@ class NumpyExtensionArray( # type: ignore[misc]
--------
>>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
<NumpyExtensionArray>
- [np.int64(0), np.int64(1), np.int64(2), np.int64(3)]
+ [0, 1, 2, 3]
Length: 4, dtype: int64
"""
diff --git a/pandas/core/base.py b/pandas/core/base.py
index 780761003458c..ea7e99f6f1879 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -558,7 +558,7 @@ def array(self) -> ExtensionArray:
>>> pd.Series([1, 2, 3]).array
<NumpyExtensionArray>
- [np.int64(1), np.int64(2), np.int64(3)]
+ [1, 2, 3]
Length: 3, dtype: int64
For extension types, like Categorical, the actual ExtensionArray
@@ -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/construction.py b/pandas/core/construction.py
index 72456ac3e1abc..ada492787a179 100644
--- a/pandas/core/construction.py
+++ b/pandas/core/construction.py
@@ -177,7 +177,7 @@ def array(
>>> pd.array(["a", "b"], dtype=str)
<NumpyExtensionArray>
- [np.str_('a'), np.str_('b')]
+ ['a', 'b']
Length: 2, dtype: str32
This would instead return the new ExtensionArray dedicated for string
@@ -186,7 +186,7 @@ def array(
>>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
<NumpyExtensionArray>
- [np.str_('a'), np.str_('b')]
+ ['a', 'b']
Length: 2, dtype: str32
Finally, Pandas has arrays that mostly overlap with NumPy
From 347b865acbdeba1e4c28f5346661181282c66f2f Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Wed, 19 Mar 2025 15:15:10 -0400
Subject: [PATCH 10/13] Avoid float16
---
doc/source/user_guide/basics.rst | 5 ++---
doc/source/whatsnew/v0.11.0.rst | 5 ++---
pandas/core/arrays/datetimelike.py | 4 ++++
3 files changed, 8 insertions(+), 6 deletions(-)
diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index ce5d16c604136..d8ef9899a07c9 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -2063,14 +2063,13 @@ or a passed ``Series``), then it will be preserved in DataFrame operations. Furt
different numeric dtypes will **NOT** be combined. The following example will give you a taste.
.. ipython:: python
- :okwarning:
- 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/whatsnew/v0.11.0.rst b/doc/source/whatsnew/v0.11.0.rst
index 73228513bfffc..28c9d46f21fd8 100644
--- a/doc/source/whatsnew/v0.11.0.rst
+++ b/doc/source/whatsnew/v0.11.0.rst
@@ -73,12 +73,11 @@ Dtypes
Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the ``dtype`` keyword, a passed ``ndarray``, or a passed ``Series``, then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will **NOT** be combined. The following example will give you a taste.
.. ipython:: python
- :okwarning:
- 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/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index b27bf19f2f593..f109db9458834 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -2520,6 +2520,10 @@ def _validate_inferred_freq(
freq : DateOffset or None
"""
if inferred_freq is not None:
+ offset1 = to_offset(freq)
+ offset2 = to_offset(inferred_freq)
+ print(offset1, offset2)
+ print(type(offset1), type(offset2))
if freq is not None and freq != inferred_freq:
raise ValueError(
f"Inferred frequency {inferred_freq} from passed "
From 17ec834b699a18dbf0441f48e345b479f8628020 Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Thu, 20 Mar 2025 16:30:31 -0400
Subject: [PATCH 11/13] Cleanup
---
pandas/core/arrays/datetimelike.py | 4 ----
1 file changed, 4 deletions(-)
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index f109db9458834..b27bf19f2f593 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -2520,10 +2520,6 @@ def _validate_inferred_freq(
freq : DateOffset or None
"""
if inferred_freq is not None:
- offset1 = to_offset(freq)
- offset2 = to_offset(inferred_freq)
- print(offset1, offset2)
- print(type(offset1), type(offset2))
if freq is not None and freq != inferred_freq:
raise ValueError(
f"Inferred frequency {inferred_freq} from passed "
From 65e1374d33f8037ddea2d161e5a5208465c9ef1b Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 29 Mar 2025 08:36:37 -0400
Subject: [PATCH 12/13] Remove repr from MyExtensionArray
---
pandas/core/arrays/base.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index bc5a5f6d68d95..19f377a232d5a 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -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>
- [np.int64(1)*, np.int64(2)*, np.int64(3)*, np.int64(4)*]
+ [1*, 2*, 3*, 4*]
Length: 4, dtype: int64
"""
if boxed:
From 14d34a750c2b546a089e96a9acfcefbe78e8f9ea Mon Sep 17 00:00:00 2001
From: Richard Shadrach <rhshadrach@gmail.com>
Date: Sat, 29 Mar 2025 15:31:32 -0400
Subject: [PATCH 13/13] Fixup
---
pandas/core/arrays/base.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 19f377a232d5a..42be07e03bad8 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -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)
+ ... 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: