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numpy_2_0_migration_guide.html
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<section id="numpy-2-0-migration-guide">
<span id="numpy-2-migration-guide"></span><h1>NumPy 2.0 migration guide<a class="headerlink" href="#numpy-2-0-migration-guide" title="Link to this heading">#</a></h1>
<p>This document contains a set of instructions on how to update your code to
work with NumPy 2.0. It covers changes in NumPy’s Python and C APIs.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Note that NumPy 2.0 also breaks binary compatibility - if you are
distributing binaries for a Python package that depends on NumPy’s C API,
please see <a class="reference internal" href="dev/depending_on_numpy.html#numpy-2-abi-handling"><span class="std std-ref">NumPy 2.0-specific advice</span></a>.</p>
</div>
<section id="ruff-plugin">
<h2>Ruff plugin<a class="headerlink" href="#ruff-plugin" title="Link to this heading">#</a></h2>
<p>Many of the changes covered in the 2.0 release notes and in this migration
guide can be automatically adapted to in downstream code with a dedicated
<a class="reference external" href="https://docs.astral.sh/ruff/">Ruff</a> rule, namely rule
<a class="reference external" href="https://docs.astral.sh/ruff/rules/numpy2-deprecation/">NPY201</a>.</p>
<p>You should install <code class="docutils literal notranslate"><span class="pre">ruff>=0.2.0</span></code> and add the <code class="docutils literal notranslate"><span class="pre">NPY201</span></code> rule to your
<code class="docutils literal notranslate"><span class="pre">pyproject.toml</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="n">tool</span><span class="o">.</span><span class="n">ruff</span><span class="o">.</span><span class="n">lint</span><span class="p">]</span>
<span class="n">select</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"NPY201"</span><span class="p">]</span>
</pre></div>
</div>
<p>You can also apply the NumPy 2.0 rule directly from the command line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ ruff check path/to/code/ --select NPY201
</pre></div>
</div>
</section>
<section id="changes-to-numpy-data-type-promotion">
<span id="migration-promotion-changes"></span><h2>Changes to NumPy data type promotion<a class="headerlink" href="#changes-to-numpy-data-type-promotion" title="Link to this heading">#</a></h2>
<p>NumPy 2.0 changes promotion (the result of combining dissimilar data types)
as per <a class="reference external" href="https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50" title="(in NumPy Enhancement Proposals)"><span class="xref std std-ref">NEP 50</span></a>. Please see the NEP for details on this change.
It includes a table of example changes and a backwards compatibility section.</p>
<p>The largest backwards compatibility change of this is that it means that
the precision of scalars is now preserved consistently.
Two examples are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">np.float32(3)</span> <span class="pre">+</span> <span class="pre">3.</span></code> now returns a float32 when it previously returned
a float64.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">np.array([3],</span> <span class="pre">dtype=np.float32)</span> <span class="pre">+</span> <span class="pre">np.float64(3)</span></code> will now return a float64
array. (The higher precision of the scalar is not ignored.)</p></li>
</ul>
<p>For floating point values, this can lead to lower precision results when
working with scalars. For integers, errors or overflows are possible.</p>
<p>To solve this, you may cast explicitly. Very often, it may also be a good
solution to ensure you are working with Python scalars via <code class="docutils literal notranslate"><span class="pre">int()</span></code>,
<code class="docutils literal notranslate"><span class="pre">float()</span></code>, or <code class="docutils literal notranslate"><span class="pre">numpy_scalar.item()</span></code>.</p>
<p>To track down changes, you can enable emitting warnings for changed behavior
(use <code class="docutils literal notranslate"><span class="pre">warnings.simplefilter</span></code> to raise it as an error for a traceback):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">_set_promotion_state</span><span class="p">(</span><span class="s2">"weak_and_warn"</span><span class="p">)</span>
</pre></div>
</div>
<p>which is useful during testing. Unfortunately,
running this may flag many changes that are irrelevant in practice.</p>
</section>
<section id="windows-default-integer">
<span id="migration-windows-int64"></span><h2>Windows default integer<a class="headerlink" href="#windows-default-integer" title="Link to this heading">#</a></h2>
<p>The default integer used by NumPy is now 64bit on all 64bit systems (and
32bit on 32bit system). For historic reasons related to Python 2 it was
previously equivalent to the C <code class="docutils literal notranslate"><span class="pre">long</span></code> type.
The default integer is now equivalent to <code class="docutils literal notranslate"><span class="pre">np.intp</span></code>.</p>
<p>Most end-users should not be affected by this change. Some operations will
use more memory, but some operations may actually become faster.
If you experience issues due to calling a library written in a compiled
language it may help to explicitly cast to a <code class="docutils literal notranslate"><span class="pre">long</span></code>, for example with:
<code class="docutils literal notranslate"><span class="pre">arr</span> <span class="pre">=</span> <span class="pre">arr.astype("long",</span> <span class="pre">copy=False)</span></code>.</p>
<p>Libraries interfacing with compiled code that are written in C, Cython, or
a similar language may require updating to accommodate user input if they
are using the <code class="docutils literal notranslate"><span class="pre">long</span></code> or equivalent type on the C-side.
In this case, you may wish to use <code class="docutils literal notranslate"><span class="pre">intp</span></code> and cast user input or support
both <code class="docutils literal notranslate"><span class="pre">long</span></code> and <code class="docutils literal notranslate"><span class="pre">intp</span></code> (to better support NumPy 1.x as well).
When creating a new integer array in C or Cython, the new <code class="docutils literal notranslate"><span class="pre">NPY_DEFAULT_INT</span></code>
macro will evaluate to either <code class="docutils literal notranslate"><span class="pre">NPY_LONG</span></code> or <code class="docutils literal notranslate"><span class="pre">NPY_INTP</span></code> depending on the
NumPy version.</p>
<p>Note that the NumPy random API is not affected by this change.</p>
</section>
<section id="c-api-changes">
<h2>C-API Changes<a class="headerlink" href="#c-api-changes" title="Link to this heading">#</a></h2>
<p>Some definitions were removed or replaced due to being outdated or
unmaintainable. Some new API definition will evaluate differently at
runtime between NumPy 2.0 and NumPy 1.x.
Some are defined in <code class="docutils literal notranslate"><span class="pre">numpy/_core/include/numpy/npy_2_compat.h</span></code>
(for example <code class="docutils literal notranslate"><span class="pre">NPY_DEFAULT_INT</span></code>) which can be vendored in full or part
to have the definitions available when compiling against NumPy 1.x.</p>
<p>If necessary, <code class="docutils literal notranslate"><span class="pre">PyArray_RUNTIME_VERSION</span> <span class="pre">>=</span> <span class="pre">NPY_2_0_API_VERSION</span></code> can be
used to explicitly implement different behavior on NumPy 1.x and 2.0.
(The compat header defines it in a way compatible with such use.)</p>
<p>Please let us know if you require additional workarounds here.</p>
<section id="the-pyarray-descr-struct-has-been-changed">
<span id="migration-c-descr"></span><h3>The <code class="docutils literal notranslate"><span class="pre">PyArray_Descr</span></code> struct has been changed<a class="headerlink" href="#the-pyarray-descr-struct-has-been-changed" title="Link to this heading">#</a></h3>
<p>One of the most impactful C-API changes is that the <code class="docutils literal notranslate"><span class="pre">PyArray_Descr</span></code> struct
is now more opaque to allow us to add additional flags and have
itemsizes not limited by the size of <code class="docutils literal notranslate"><span class="pre">int</span></code> as well as allow improving
structured dtypes in the future and not burdon new dtypes with their fields.</p>
<p>Code which only uses the type number and other initial fields is unaffected.
Most code will hopefull mainly access the <code class="docutils literal notranslate"><span class="pre">->elsize</span></code> field, when the
dtype/descriptor itself is attached to an array (e.g. <code class="docutils literal notranslate"><span class="pre">arr->descr->elsize</span></code>)
this is best replaced with <code class="docutils literal notranslate"><span class="pre">PyArray_ITEMSIZE(arr)</span></code>.</p>
<p>Where not possible, new accessor functions are required:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">PyDataType_ELSIZE</span></code> and <code class="docutils literal notranslate"><span class="pre">PyDataType_SET_ELSIZE</span></code> (note that the result
is now <code class="docutils literal notranslate"><span class="pre">npy_intp</span></code> and not <code class="docutils literal notranslate"><span class="pre">int</span></code>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">PyDataType_ALIGNENT</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">PyDataType_FIELDS</span></code>, <code class="docutils literal notranslate"><span class="pre">PyDataType_NAMES</span></code>, <code class="docutils literal notranslate"><span class="pre">PyDataType_SUBARRAY</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">PyDataType_C_METADATA</span></code></p></li>
</ul>
<p>Cython code should use Cython 3, in which case the change is transparent.
(Struct access is available for elsize and alignment when compiling only for
NumPy 2.)</p>
<p>For compiling with both 1.x and 2.x if you use these new accessors it is
unfortunately necessary to either define them locally via a macro like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#if NPY_ABI_VERSION < 0x02000000</span>
<span class="c1">#define PyDataType_ELSIZE(descr) ((descr)->elsize)</span>
<span class="c1">#endif</span>
</pre></div>
</div>
<p>or adding <code class="docutils literal notranslate"><span class="pre">npy2_compat.h</span></code> into your code base and explicitly include it
when compiling with NumPy 1.x (as they are new API).
Including the file has no effect on NumPy 2.</p>
<p>Please do not hesitate to open a NumPy issue, if you require assistence or
the provided functions are not sufficient.</p>
<p><strong>Custom User DTypes:</strong>
Existing user dtypes must now use <code class="docutils literal notranslate"><span class="pre">PyArray_DescrProto</span></code> to define their
dtype and slightly modify the code. See note in <em class="xref py py-obj">PyArray_RegisterDataType</em>.</p>
</section>
<section id="functionality-moved-to-headers-requiring-import-array">
<h3>Functionality moved to headers requiring <code class="docutils literal notranslate"><span class="pre">import_array()</span></code><a class="headerlink" href="#functionality-moved-to-headers-requiring-import-array" title="Link to this heading">#</a></h3>
<p>If you previously included only <code class="docutils literal notranslate"><span class="pre">ndarraytypes.h</span></code> you may find that some
functionality is not available anymore and requires the inclusion of
<code class="docutils literal notranslate"><span class="pre">ndarrayobject.h</span></code> or similar.
This include is also needed when vendoring <code class="docutils literal notranslate"><span class="pre">npy_2_compat.h</span></code> into your own
codebase to allow use of the new definitions when compiling with NumPy 1.x.</p>
<p>Functionality which previously did not require import includes:</p>
<ul class="simple">
<li><p>Functions to access dtype flags: <code class="docutils literal notranslate"><span class="pre">PyDataType_FLAGCHK</span></code>,
<code class="docutils literal notranslate"><span class="pre">PyDataType_REFCHK</span></code>, and the related <code class="docutils literal notranslate"><span class="pre">NPY_BEGIN_THREADS_DESCR</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">PyArray_GETITEM</span></code> and <code class="docutils literal notranslate"><span class="pre">PyArray_SETITEM</span></code>.</p></li>
</ul>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>It is important that the <code class="docutils literal notranslate"><span class="pre">import_array()</span></code> mechanism is used to ensure
that the full NumPy API is accessible when using the <code class="docutils literal notranslate"><span class="pre">npy_2_compat.h</span></code>
header. In most cases your extension module probably already calls it.
However, if not we have added <code class="docutils literal notranslate"><span class="pre">PyArray_ImportNumPyAPI()</span></code> as a preferable
way to ensure the NumPy API is imported. This function is light-weight
when called multiple times so that you may insert it wherever it may be
needed (if you wish to avoid setting it up at module import).</p>
</div>
</section>
<section id="increased-maximum-number-of-dimensions">
<span id="migration-maxdims"></span><h3>Increased maximum number of dimensions<a class="headerlink" href="#increased-maximum-number-of-dimensions" title="Link to this heading">#</a></h3>
<p>The maximum number of dimensions (and arguments) was increased to 64, this
affects the <code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS</span></code> and <code class="docutils literal notranslate"><span class="pre">NPY_MAXARGS</span></code> macros.
It may be good to review their use, and we generally encourage you to
not use these macros (especially <code class="docutils literal notranslate"><span class="pre">NPY_MAXARGS</span></code>), so that a future version of
NumPy can remove this limitation on the number of dimensions.</p>
<p><code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS</span></code> was also used to signal <code class="docutils literal notranslate"><span class="pre">axis=None</span></code> in the C-API, including
the <code class="docutils literal notranslate"><span class="pre">PyArray_AxisConverter</span></code>.
The latter will return <code class="docutils literal notranslate"><span class="pre">-2147483648</span></code> as an axis (the smallest integer value).
Other functions may error with
<code class="docutils literal notranslate"><span class="pre">AxisError:</span> <span class="pre">axis</span> <span class="pre">64</span> <span class="pre">is</span> <span class="pre">out</span> <span class="pre">of</span> <span class="pre">bounds</span> <span class="pre">for</span> <span class="pre">array</span> <span class="pre">of</span> <span class="pre">dimension</span></code> in which
case you need to pass <code class="docutils literal notranslate"><span class="pre">NPY_RAVEL_AXIS</span></code> instead of <code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS</span></code>.
<code class="docutils literal notranslate"><span class="pre">NPY_RAVEL_AXIS</span></code> is defined in the <code class="docutils literal notranslate"><span class="pre">npy_2_compat.h</span></code> header and runtime
dependent (mapping to 32 on NumPy 1.x and <code class="docutils literal notranslate"><span class="pre">-2147483648</span></code> on NumPy 2.x).</p>
</section>
<section id="complex-types-underlying-type-changes">
<h3>Complex types - Underlying type changes<a class="headerlink" href="#complex-types-underlying-type-changes" title="Link to this heading">#</a></h3>
<p>The underlying C types for all of the complex types have been changed to use
native C99 types. While the memory layout of those types remains identical
to the types used in NumPy 1.x, the API is slightly different, since direct
field access (like <code class="docutils literal notranslate"><span class="pre">c.real</span></code> or <code class="docutils literal notranslate"><span class="pre">c.imag</span></code>) is no longer possible.</p>
<p>It is recommended to use the functions <em class="xref py py-obj">npy_creal</em> and <em class="xref py py-obj">npy_cimag</em> (and the
corresponding float and long double variants) to retrieve
the real or imaginary part of a complex number, as these will work with both
NumPy 1.x and with NumPy 2.x. New functions <em class="xref py py-obj">npy_csetreal</em> and <em class="xref py py-obj">npy_csetimag</em>,
along with compatibility macros <em class="xref py py-obj">NPY_CSETREAL</em> and <em class="xref py py-obj">NPY_CSETIMAG</em> (and the
corresponding float and long double variants), have been
added for setting the real or imaginary part.</p>
<p>The underlying type remains a struct under C++ (all of the above still remains
valid).</p>
<p>This has implications for Cython. It is recommened to always use the native
typedefs <code class="docutils literal notranslate"><span class="pre">cfloat_t</span></code>, <code class="docutils literal notranslate"><span class="pre">cdouble_t</span></code>, <code class="docutils literal notranslate"><span class="pre">clongdouble_t</span></code> rather than the NumPy
types <code class="docutils literal notranslate"><span class="pre">npy_cfloat</span></code>, etc, unless you have to interface with C code written
using the NumPy types. You can still write cython code using the <code class="docutils literal notranslate"><span class="pre">c.real</span></code> and
<code class="docutils literal notranslate"><span class="pre">c.imag</span></code> attributes (using the native typedefs), but you can no longer use
in-place operators <code class="docutils literal notranslate"><span class="pre">c.imag</span> <span class="pre">+=</span> <span class="pre">1</span></code> in Cython’s c++ mode.</p>
</section>
</section>
<section id="changes-to-namespaces">
<h2>Changes to namespaces<a class="headerlink" href="#changes-to-namespaces" title="Link to this heading">#</a></h2>
<p>In NumPy 2.0 certain functions, modules, and constants were moved or removed
to make the NumPy namespace more user-friendly by removing unnecessary or
outdated functionality and clarifying which parts of NumPy are considered
private.
Please see the tables below for guidance on migration. For most changes this
means replacing it with a backwards compatible alternative.</p>
<p>Please refer to <a class="reference external" href="https://numpy.org/neps/nep-0052-python-api-cleanup.html">NEP 52</a> for more details.</p>
<section id="main-namespace">
<h3>Main namespace<a class="headerlink" href="#main-namespace" title="Link to this heading">#</a></h3>
<p>About 100 members of the main <code class="docutils literal notranslate"><span class="pre">np</span></code> namespace has been deprecated, removed, or
moved to a new place. It was done to reduce clutter and establish only one way to
access a given attribute. The table below shows members that have been removed:</p>
<table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>removed member</p></th>
<th class="head"><p>migration guideline</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>add_docstring</p></td>
<td><p>It’s still available as <code class="docutils literal notranslate"><span class="pre">np.lib.add_docstring</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p>add_newdoc</p></td>
<td><p>It’s still available as <code class="docutils literal notranslate"><span class="pre">np.lib.add_newdoc</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p>add_newdoc_ufunc</p></td>
<td><p>It’s an internal function and doesn’t have a replacement.</p></td>
</tr>
<tr class="row-odd"><td><p>asfarray</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code> with a float dtype instead.</p></td>
</tr>
<tr class="row-even"><td><p>byte_bounds</p></td>
<td><p>Now it’s available under <code class="docutils literal notranslate"><span class="pre">np.lib.array_utils.byte_bounds</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>cast</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.asarray(arr,</span> <span class="pre">dtype=dtype)</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>cfloat</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.complex128</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>clongfloat</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.clongdouble</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>compat</p></td>
<td><p>There’s no replacement, as Python 2 is no longer supported.</p></td>
</tr>
<tr class="row-odd"><td><p>complex_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.complex128</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>DataSource</p></td>
<td><p>It’s still available as <code class="docutils literal notranslate"><span class="pre">np.lib.npyio.DataSource</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p>deprecate</p></td>
<td><p>Emit <code class="docutils literal notranslate"><span class="pre">DeprecationWarning</span></code> with <code class="docutils literal notranslate"><span class="pre">warnings.warn</span></code> directly,
or use <code class="docutils literal notranslate"><span class="pre">typing.deprecated</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p>deprecate_with_doc</p></td>
<td><p>Emit <code class="docutils literal notranslate"><span class="pre">DeprecationWarning</span></code> with <code class="docutils literal notranslate"><span class="pre">warnings.warn</span></code> directly,
or use <code class="docutils literal notranslate"><span class="pre">typing.deprecated</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p>disp</p></td>
<td><p>Use your own printing function instead.</p></td>
</tr>
<tr class="row-even"><td><p>fastCopyAndTranspose</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">arr.T.copy()</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>find_common_type</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">numpy.promote_types</span></code> or <code class="docutils literal notranslate"><span class="pre">numpy.result_type</span></code> instead.
To achieve semantics for the <code class="docutils literal notranslate"><span class="pre">scalar_types</span></code> argument,
use <code class="docutils literal notranslate"><span class="pre">numpy.result_type</span></code> and pass the Python values <code class="docutils literal notranslate"><span class="pre">0</span></code>,
<code class="docutils literal notranslate"><span class="pre">0.0</span></code>, or <code class="docutils literal notranslate"><span class="pre">0j</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p>get_array_wrap</p></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>float_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.float64</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>geterrobj</p></td>
<td><p>Use the np.errstate context manager instead.</p></td>
</tr>
<tr class="row-odd"><td><p>Inf</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>Infinity</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>infty</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>issctype</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">issubclass(rep,</span> <span class="pre">np.generic)</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>issubclass_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">issubclass</span></code> builtin instead.</p></td>
</tr>
<tr class="row-even"><td><p>issubsctype</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.issubdtype</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>mat</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.asmatrix</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>maximum_sctype</p></td>
<td><p>Use a specific dtype instead. You should avoid relying
on any implicit mechanism and select the largest dtype of
a kind explicitly in the code.</p></td>
</tr>
<tr class="row-odd"><td><p>NaN</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.nan</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>nbytes</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.dtype(<dtype>).itemsize</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>NINF</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">-np.inf</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>NZERO</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">-0.0</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>longcomplex</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.clongdouble</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>longfloat</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.longdouble</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>lookfor</p></td>
<td><p>Search NumPy’s documentation directly.</p></td>
</tr>
<tr class="row-even"><td><p>obj2sctype</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.dtype(obj).type</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>PINF</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.inf</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>PZERO</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">0.0</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>recfromcsv</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.genfromtxt</span></code> with comma delimiter instead.</p></td>
</tr>
<tr class="row-even"><td><p>recfromtxt</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.genfromtxt</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>round_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.round</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>safe_eval</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">ast.literal_eval</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>sctype2char</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.dtype(obj).char</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>sctypes</p></td>
<td><p>Access dtypes explicitly instead.</p></td>
</tr>
<tr class="row-odd"><td><p>seterrobj</p></td>
<td><p>Use the np.errstate context manager instead.</p></td>
</tr>
<tr class="row-even"><td><p>set_numeric_ops</p></td>
<td><p>For the general case, use <code class="docutils literal notranslate"><span class="pre">PyUFunc_ReplaceLoopBySignature</span></code>.
For ndarray subclasses, define the <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> method
and override the relevant ufunc.</p></td>
</tr>
<tr class="row-odd"><td><p>set_string_function</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.set_printoptions</span></code> instead with a formatter
for custom printing of NumPy objects.</p></td>
</tr>
<tr class="row-even"><td><p>singlecomplex</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.complex64</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>string_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.bytes_</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>source</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">inspect.getsource</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>tracemalloc_domain</p></td>
<td><p>It’s now available from <code class="docutils literal notranslate"><span class="pre">np.lib</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p>unicode_</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.str_</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>who</p></td>
<td><p>Use an IDE variable explorer or <code class="docutils literal notranslate"><span class="pre">locals()</span></code> instead.</p></td>
</tr>
</tbody>
</table>
<p>If the table doesn’t contain an item that you were using but was removed in <code class="docutils literal notranslate"><span class="pre">2.0</span></code>,
then it means it was a private member. You should either use the existing API or,
in case it’s infeasible, reach out to us with a request to restore the removed entry.</p>
<p>The next table presents deprecated members, which will be removed in a release after <code class="docutils literal notranslate"><span class="pre">2.0</span></code>:</p>
<table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>deprecated member</p></th>
<th class="head"><p>migration guideline</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>in1d</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.isin</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>row_stack</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.vstack</span></code> instead (<code class="docutils literal notranslate"><span class="pre">row_stack</span></code> was an alias for <code class="docutils literal notranslate"><span class="pre">vstack</span></code>).</p></td>
</tr>
<tr class="row-even"><td><p>trapz</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.trapezoid</span></code> or a <code class="docutils literal notranslate"><span class="pre">scipy.integrate</span></code> function instead.</p></td>
</tr>
</tbody>
</table>
<p>Finally, a set of internal enums has been removed. As they weren’t used in
downstream libraries we don’t provide any information on how to replace them:</p>
<p>[<code class="docutils literal notranslate"><span class="pre">FLOATING_POINT_SUPPORT</span></code>, <code class="docutils literal notranslate"><span class="pre">FPE_DIVIDEBYZERO</span></code>, <code class="docutils literal notranslate"><span class="pre">FPE_INVALID</span></code>, <code class="docutils literal notranslate"><span class="pre">FPE_OVERFLOW</span></code>,
<code class="docutils literal notranslate"><span class="pre">FPE_UNDERFLOW</span></code>, <code class="docutils literal notranslate"><span class="pre">UFUNC_BUFSIZE_DEFAULT</span></code>, <code class="docutils literal notranslate"><span class="pre">UFUNC_PYVALS_NAME</span></code>, <code class="docutils literal notranslate"><span class="pre">CLIP</span></code>, <code class="docutils literal notranslate"><span class="pre">WRAP</span></code>,
<code class="docutils literal notranslate"><span class="pre">RAISE</span></code>, <code class="docutils literal notranslate"><span class="pre">BUFSIZE</span></code>, <code class="docutils literal notranslate"><span class="pre">ALLOW_THREADS</span></code>, <code class="docutils literal notranslate"><span class="pre">MAXDIMS</span></code>, <code class="docutils literal notranslate"><span class="pre">MAY_SHARE_EXACT</span></code>,
<code class="docutils literal notranslate"><span class="pre">MAY_SHARE_BOUNDS</span></code>]</p>
</section>
<section id="numpy-lib-namespace">
<h3>numpy.lib namespace<a class="headerlink" href="#numpy-lib-namespace" title="Link to this heading">#</a></h3>
<p>Most of the functions available within <code class="docutils literal notranslate"><span class="pre">np.lib</span></code> are also present in the main
namespace, which is their primary location. To make it unambiguous how to access each
public function, <code class="docutils literal notranslate"><span class="pre">np.lib</span></code> is now empty and contains only a handful of specialized submodules,
classes and functions:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">array_utils</span></code>, <code class="docutils literal notranslate"><span class="pre">format</span></code>, <code class="docutils literal notranslate"><span class="pre">introspect</span></code>, <code class="docutils literal notranslate"><span class="pre">mixins</span></code>, <code class="docutils literal notranslate"><span class="pre">npyio</span></code>
and <code class="docutils literal notranslate"><span class="pre">stride_tricks</span></code> submodules,</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Arrayterator</span></code> and <code class="docutils literal notranslate"><span class="pre">NumpyVersion</span></code> classes,</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">add_docstring</span></code> and <code class="docutils literal notranslate"><span class="pre">add_newdoc</span></code> functions,</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">tracemalloc_domain</span></code> constant.</p></li>
</ul>
<p>If you get an <code class="docutils literal notranslate"><span class="pre">AttributeError</span></code> when accessing an attribute from <code class="docutils literal notranslate"><span class="pre">np.lib</span></code> you should
try accessing it from the main <code class="docutils literal notranslate"><span class="pre">np</span></code> namespace then. If an item is also missing from
the main namespace, then you’re using a private member. You should either use the existing
API or, in case it’s infeasible, reach out to us with a request to restore the removed entry.</p>
</section>
<section id="numpy-core-namespace">
<h3>numpy.core namespace<a class="headerlink" href="#numpy-core-namespace" title="Link to this heading">#</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">np.core</span></code> namespace is now officially private and has been renamed to <code class="docutils literal notranslate"><span class="pre">np._core</span></code>.
The user should never fetch members from the <code class="docutils literal notranslate"><span class="pre">_core</span></code> directly - instead the main
namespace should be used to access the attribute in question. The layout of the <code class="docutils literal notranslate"><span class="pre">_core</span></code>
module might change in the future without notice, contrary to public modules which adhere
to the deprecation period policy. If an item is also missing from the main namespace,
then you should either use the existing API or, in case it’s infeasible, reach out to us
with a request to restore the removed entry.</p>
</section>
<section id="ndarray-and-scalar-methods">
<h3>ndarray and scalar methods<a class="headerlink" href="#ndarray-and-scalar-methods" title="Link to this heading">#</a></h3>
<p>A few methods from <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code> and <code class="docutils literal notranslate"><span class="pre">np.generic</span></code> scalar classes have been removed.
The table below provides replacements for the removed members:</p>
<table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>expired member</p></th>
<th class="head"><p>migration guideline</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>newbyteorder</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">arr.view(arr.dtype.newbyteorder(order))</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>ptp</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">np.ptp(arr,</span> <span class="pre">...)</span></code> instead.</p></td>
</tr>
<tr class="row-even"><td><p>setitem</p></td>
<td><p>Use <code class="docutils literal notranslate"><span class="pre">arr[index]</span> <span class="pre">=</span> <span class="pre">value</span></code> instead.</p></td>
</tr>
<tr class="row-odd"><td><p>…</p></td>
<td><p>…</p></td>
</tr>
</tbody>
</table>
</section>
<section id="numpy-strings-namespace">
<h3>numpy.strings namespace<a class="headerlink" href="#numpy-strings-namespace" title="Link to this heading">#</a></h3>
<p>A new <a class="reference internal" href="reference/routines.strings.html#module-numpy.strings" title="numpy.strings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.strings</span></code></a> namespace has been created, where most of the string
operations are implemented as ufuncs. The old <a class="reference internal" href="reference/routines.char.html#module-numpy.char" title="numpy.char"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.char</span></code></a> namespace still is
available, and, wherever possible, uses the new ufuncs for greater performance.
We recommend using the <a class="reference internal" href="reference/routines.strings.html#module-numpy.strings" title="numpy.strings"><code class="xref py py-obj docutils literal notranslate"><span class="pre">strings</span></code></a> functions going forward. The
<a class="reference internal" href="reference/routines.char.html#module-numpy.char" title="numpy.char"><code class="xref py py-obj docutils literal notranslate"><span class="pre">char</span></code></a> namespace may be deprecated in the future.</p>
</section>
</section>
<section id="other-changes">