MNT: Bump dev pin on NumPy

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)

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

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

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):

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

environment.yml

@@ -23,7 +23,7 @@ dependencies:
 
   # required dependencies
   - python-dateutil
-  - numpy<2
+  - numpy<3
 
   # optional dependencies
   - beautifulsoup4>=4.11.2

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

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

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

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)

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