DOC: use new numpy doc url

doc/source/development/contributing.rst

@@ -110,7 +110,7 @@ version control to allow many people to work together on the project.
 Some great resources for learning Git:
 
 * the `GitHub help pages <https://help.github.com/>`_.
-* the `NumPy's documentation <https://docs.scipy.org/doc/numpy/dev/index.html>`_.
+* the `NumPy's documentation <https://numpy.org/doc/stable/dev/index.html>`_.
 * Matthew Brett's `Pydagogue <https://matthew-brett.github.com/pydagogue/>`_.
 
 Getting started with Git
@@ -974,7 +974,7 @@ it is worth getting in the habit of writing tests ahead of time so this is never
 Like many packages, pandas uses `pytest
 <https://docs.pytest.org/en/latest/>`_ and the convenient
 extensions in `numpy.testing
-<https://docs.scipy.org/doc/numpy/reference/routines.testing.html>`_.
+<https://numpy.org/doc/stable/reference/routines.testing.html>`_.
 
 .. note::
 

doc/source/development/extending.rst

@@ -219,7 +219,7 @@ and re-boxes it if necessary.
 
 If applicable, we highly recommend that you implement ``__array_ufunc__`` in your
 extension array to avoid coercion to an ndarray. See
-`the numpy documentation <https://docs.scipy.org/doc/numpy/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html>`__
+`the numpy documentation <https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html>`__
 for an example.
 
 As part of your implementation, we require that you defer to pandas when a pandas

doc/source/user_guide/basics.rst

@@ -2385,7 +2385,7 @@ For example, to select ``bool`` columns:
    df.select_dtypes(include=[bool])
 
 You can also pass the name of a dtype in the `NumPy dtype hierarchy
-<https://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__:
+<https://numpy.org/doc/stable/reference/arrays.scalars.html>`__:
 
 .. ipython:: python
 

doc/source/user_guide/dsintro.rst

@@ -273,7 +273,7 @@ pandas object. Like Series, DataFrame accepts many different kinds of input:
 * Dict of 1D ndarrays, lists, dicts, or Series
 * 2-D numpy.ndarray
 * `Structured or record
-  <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`__ ndarray
+  <https://numpy.org/doc/stable/user/basics.rec.html>`__ ndarray
 * A ``Series``
 * Another ``DataFrame``
 
@@ -632,7 +632,7 @@ union of the column and row labels.
 
 When doing an operation between DataFrame and Series, the default behavior is
 to align the Series **index** on the DataFrame **columns**, thus `broadcasting
-<https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`__
+<https://numpy.org/doc/stable/user/basics.broadcasting.html>`__
 row-wise. For example:
 
 .. ipython:: python
@@ -718,7 +718,7 @@ indexing semantics and data model are quite different in places from an n-dimens
 array.
 
 :class:`Series` implements ``__array_ufunc__``, which allows it to work with NumPy's
-`universal functions <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_.
+`universal functions <https://numpy.org/doc/stable/reference/ufuncs.html>`_.
 
 The ufunc is applied to the underlying array in a Series.
 

doc/source/user_guide/gotchas.rst

@@ -339,5 +339,5 @@ constructors using something similar to the following:
    s = pd.Series(newx)
 
 See `the NumPy documentation on byte order
-<https://docs.scipy.org/doc/numpy/user/basics.byteswapping.html>`__ for more
+<https://numpy.org/doc/stable/user/basics.byteswapping.html>`__ for more
 details.

doc/source/user_guide/sparse.rst

@@ -139,7 +139,7 @@ See :ref:`api.frame.sparse` for more.
 Sparse calculation
 ------------------
 
-You can apply NumPy `ufuncs <https://docs.scipy.org/doc/numpy/reference/ufuncs.html>`_
+You can apply NumPy `ufuncs <https://numpy.org/doc/stable/reference/ufuncs.html>`_
 to ``SparseArray`` and get a ``SparseArray`` as a result.
 
 .. ipython:: python

doc/source/whatsnew/v0.19.0.rst

@@ -390,7 +390,7 @@ Fine-grained NumPy errstate
 
 Previous versions of pandas would permanently silence numpy's ufunc error handling when ``pandas`` was imported. Pandas did this in order to silence the warnings that would arise from using numpy ufuncs on missing data, which are usually represented as ``NaN`` s. Unfortunately, this silenced legitimate warnings arising in non-pandas code in the application. Starting with 0.19.0, pandas will use the ``numpy.errstate`` context manager to silence these warnings in a more fine-grained manner, only around where these operations are actually used in the pandas code base. (:issue:`13109`, :issue:`13145`)
 
-After upgrading pandas, you may see *new* ``RuntimeWarnings`` being issued from your code. These are likely legitimate, and the underlying cause likely existed in the code when using previous versions of pandas that simply silenced the warning. Use `numpy.errstate <http://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html>`__ around the source of the ``RuntimeWarning`` to control how these conditions are handled.
+After upgrading pandas, you may see *new* ``RuntimeWarnings`` being issued from your code. These are likely legitimate, and the underlying cause likely existed in the code when using previous versions of pandas that simply silenced the warning. Use `numpy.errstate <https://numpy.org/doc/stable/reference/generated/numpy.errstate.html>`__ around the source of the ``RuntimeWarning`` to control how these conditions are handled.
 
 .. _whatsnew_0190.get_dummies_dtypes:
 

pandas/_libs/testing.pyx

@@ -40,8 +40,7 @@ cdef bint is_dictlike(obj):
 
 cdef bint decimal_almost_equal(double desired, double actual, int decimal):
     # Code from
-    # http://docs.scipy.org/doc/numpy/reference/generated
-    # /numpy.testing.assert_almost_equal.html
+    # https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_almost_equal.html
     return abs(desired - actual) < (0.5 * 10.0 ** -decimal)
 
 

pandas/core/arrays/numpy_.py

@@ -214,8 +214,7 @@ def __array__(self, dtype=None) -> np.ndarray:
 
     def __array_ufunc__(self, ufunc, method: str, *inputs, **kwargs):
         # Lightly modified version of
-        # https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/\
-        # numpy.lib.mixins.NDArrayOperatorsMixin.html
+        # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
         # The primary modification is not boxing scalar return values
         # in PandasArray, since pandas' ExtensionArrays are 1-d.
         out = kwargs.get("out", ())

pandas/core/frame.py

@@ -3360,7 +3360,7 @@ def select_dtypes(self, include=None, exclude=None) -> "DataFrame":
         * To select strings you must use the ``object`` dtype, but note that
           this will return *all* object dtype columns
         * See the `numpy dtype hierarchy
-          <https://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
+          <https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
         * To select datetimes, use ``np.datetime64``, ``'datetime'`` or
           ``'datetime64'``
         * To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or

pandas/tests/indexes/multi/test_analytics.py

@@ -232,7 +232,7 @@ def test_map_dictlike(idx, mapper):
 )
 def test_numpy_ufuncs(idx, func):
     # test ufuncs of numpy. see:
-    # https://docs.scipy.org/doc/numpy/reference/ufuncs.html
+    # https://numpy.org/doc/stable/reference/ufuncs.html
 
     if _np_version_under1p17:
         expected_exception = AttributeError

pandas/tests/indexes/test_numpy_compat.py

@@ -46,7 +46,7 @@
 )
 def test_numpy_ufuncs_basic(indices, func):
     # test ufuncs of numpy, see:
-    # https://docs.scipy.org/doc/numpy/reference/ufuncs.html
+    # https://numpy.org/doc/stable/reference/ufuncs.html
 
     idx = indices
     if isinstance(idx, DatetimeIndexOpsMixin):
@@ -77,7 +77,7 @@ def test_numpy_ufuncs_basic(indices, func):
 )
 def test_numpy_ufuncs_other(indices, func):
     # test ufuncs of numpy, see:
-    # https://docs.scipy.org/doc/numpy/reference/ufuncs.html
+    # https://numpy.org/doc/stable/reference/ufuncs.html
 
     idx = indices
     if isinstance(idx, (DatetimeIndex, TimedeltaIndex)):

web/pandas/community/blog/extension-arrays.md

@@ -207,7 +207,7 @@ partners][partners] involved in the pandas community.
 [IntegerArray]: http://pandas.pydata.org/pandas-docs/version/0.24/reference/api/pandas.arrays.IntegerArray.html
 [fletcher]: https://github.com/xhochy/fletcher
 [arrow]: https://arrow.apache.org
-[ufunc]: https://docs.scipy.org/doc/numpy-1.13.0/neps/ufunc-overrides.html
+[ufunc]: https://numpy.org/neps/nep-0013-ufunc-overrides.html
 [nep18]: https://www.numpy.org/neps/nep-0018-array-function-protocol.html
 [ml]: https://mail.python.org/mailman/listinfo/pandas-dev
 [twitter]: https://twitter.com/pandas_dev