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<section id="numpy-array2string">
<h1>numpy.array2string<a class="headerlink" href="#numpy-array2string" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="numpy.array2string">
<span class="sig-prename descclassname"><span class="pre">numpy.</span></span><span class="sig-name descname"><span class="pre">array2string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_line_width</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suppress_small</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">separator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'</span> <span class="pre">'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="keyword-only-separator o"><abbr title="Keyword-only parameters separator (PEP 3102)"><span class="pre">*</span></abbr></span></em>, <em class="sig-param"><span class="n"><span class="pre">formatter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edgeitems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">floatmode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legacy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/main/numpy/_core/arrayprint.py#L643-L810"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#numpy.array2string" title="Link to this definition">#</a></dt>
<dd><p>Return a string representation of an array.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">ndarray</span></dt><dd><p>Input array.</p>
</dd>
<dt><strong>max_line_width</strong><span class="classifier">int, optional</span></dt><dd><p>Inserts newlines if text is longer than <em class="xref py py-obj">max_line_width</em>.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['linewidth']</span></code>.</p>
</dd>
<dt><strong>precision</strong><span class="classifier">int or None, optional</span></dt><dd><p>Floating point precision.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['precision']</span></code>.</p>
</dd>
<dt><strong>suppress_small</strong><span class="classifier">bool, optional</span></dt><dd><p>Represent numbers “very close” to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['suppress']</span></code>.</p>
</dd>
<dt><strong>separator</strong><span class="classifier">str, optional</span></dt><dd><p>Inserted between elements.</p>
</dd>
<dt><strong>prefix</strong><span class="classifier">str, optional</span></dt><dd></dd>
<dt><strong>suffix</strong><span class="classifier">str, optional</span></dt><dd><p>The length of the prefix and suffix strings are used to respectively
align and wrap the output. An array is typically printed as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">prefix</span> <span class="o">+</span> <span class="n">array2string</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="o">+</span> <span class="n">suffix</span>
</pre></div>
</div>
<p>The output is left-padded by the length of the prefix string, and
wrapping is forced at the column <code class="docutils literal notranslate"><span class="pre">max_line_width</span> <span class="pre">-</span> <span class="pre">len(suffix)</span></code>.
It should be noted that the content of prefix and suffix strings are
not included in the output.</p>
</dd>
<dt><strong>formatter</strong><span class="classifier">dict of callables, optional</span></dt><dd><p>If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:</p>
<ul class="simple">
<li><p>‘bool’</p></li>
<li><p>‘int’</p></li>
<li><p>‘timedelta’ : a <a class="reference internal" href="../arrays.scalars.html#numpy.timedelta64" title="numpy.timedelta64"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.timedelta64</span></code></a></p></li>
<li><p>‘datetime’ : a <a class="reference internal" href="../arrays.scalars.html#numpy.datetime64" title="numpy.datetime64"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.datetime64</span></code></a></p></li>
<li><p>‘float’</p></li>
<li><p>‘longfloat’ : 128-bit floats</p></li>
<li><p>‘complexfloat’</p></li>
<li><p>‘longcomplexfloat’ : composed of two 128-bit floats</p></li>
<li><p>‘void’ : type <a class="reference internal" href="../arrays.scalars.html#numpy.void" title="numpy.void"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.void</span></code></a></p></li>
<li><p>‘numpystr’ : types <a class="reference internal" href="../arrays.scalars.html#numpy.bytes_" title="numpy.bytes_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.bytes_</span></code></a> and <a class="reference internal" href="../arrays.scalars.html#numpy.str_" title="numpy.str_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.str_</span></code></a></p></li>
</ul>
<p>Other keys that can be used to set a group of types at once are:</p>
<ul class="simple">
<li><p>‘all’ : sets all types</p></li>
<li><p>‘int_kind’ : sets ‘int’</p></li>
<li><p>‘float_kind’ : sets ‘float’ and ‘longfloat’</p></li>
<li><p>‘complex_kind’ : sets ‘complexfloat’ and ‘longcomplexfloat’</p></li>
<li><p>‘str_kind’ : sets ‘numpystr’</p></li>
</ul>
</dd>
<dt><strong>threshold</strong><span class="classifier">int, optional</span></dt><dd><p>Total number of array elements which trigger summarization
rather than full repr.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['threshold']</span></code>.</p>
</dd>
<dt><strong>edgeitems</strong><span class="classifier">int, optional</span></dt><dd><p>Number of array items in summary at beginning and end of
each dimension.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['edgeitems']</span></code>.</p>
</dd>
<dt><strong>sign</strong><span class="classifier">string, either ‘-’, ‘+’, or ‘ ‘, optional</span></dt><dd><p>Controls printing of the sign of floating-point types. If ‘+’, always
print the sign of positive values. If ‘ ‘, always prints a space
(whitespace character) in the sign position of positive values. If
‘-’, omit the sign character of positive values.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['sign']</span></code>.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 2.0: </span>The sign parameter can now be an integer type, previously
types were floating-point types.</p>
</div>
</dd>
<dt><strong>floatmode</strong><span class="classifier">str, optional</span></dt><dd><p>Controls the interpretation of the <em class="xref py py-obj">precision</em> option for
floating-point types.
Defaults to <code class="docutils literal notranslate"><span class="pre">numpy.get_printoptions()['floatmode']</span></code>.
Can take the following values:</p>
<ul class="simple">
<li><p>‘fixed’: Always print exactly <em class="xref py py-obj">precision</em> fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.</p></li>
<li><p>‘unique’: Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
<em class="xref py py-obj">precision</em> option is ignored.</p></li>
<li><p>‘maxprec’: Print at most <em class="xref py py-obj">precision</em> fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.</p></li>
<li><p>‘maxprec_equal’: Print at most <em class="xref py py-obj">precision</em> fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.</p></li>
</ul>
</dd>
<dt><strong>legacy</strong><span class="classifier">string or <em class="xref py py-obj">False</em>, optional</span></dt><dd><p>If set to the string <code class="docutils literal notranslate"><span class="pre">'1.13'</span></code> enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
<em class="xref py py-obj">False</em>, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>array_str</strong><span class="classifier">str</span></dt><dd><p>String representation of the array.</p>
</dd>
</dl>
</dd>
<dt class="field-odd">Raises<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt>TypeError</dt><dd><p>if a callable in <em class="xref py py-obj">formatter</em> does not return a string.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.array_str.html#numpy.array_str" title="numpy.array_str"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_str</span></code></a>, <a class="reference internal" href="numpy.array_repr.html#numpy.array_repr" title="numpy.array_repr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_repr</span></code></a>, <a class="reference internal" href="numpy.set_printoptions.html#numpy.set_printoptions" title="numpy.set_printoptions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_printoptions</span></code></a>, <a class="reference internal" href="numpy.get_printoptions.html#numpy.get_printoptions" title="numpy.get_printoptions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_printoptions</span></code></a></dt><dd></dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>If a formatter is specified for a certain type, the <em class="xref py py-obj">precision</em> keyword is
ignored for that type.</p>
<p>This is a very flexible function; <a class="reference internal" href="numpy.array_repr.html#numpy.array_repr" title="numpy.array_repr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_repr</span></code></a> and <a class="reference internal" href="numpy.array_str.html#numpy.array_str" title="numpy.array_str"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array_str</span></code></a> are using
<a class="reference internal" href="#numpy.array2string" title="numpy.array2string"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array2string</span></code></a> internally so keywords with the same name should work
identically in all three functions.</p>
<p class="rubric">Examples</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1e-16</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">precision</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">separator</span><span class="o">=</span><span class="s1">','</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">suppress_small</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">'[0.,1.,2.,3.]'</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">3.</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">formatter</span><span class="o">=</span><span class="p">{</span><span class="s1">'float_kind'</span><span class="p">:</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s2">"</span><span class="si">%.2f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">x</span><span class="p">})</span>
<span class="go">'[0.00 1.00 2.00]'</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">array2string</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">formatter</span><span class="o">=</span><span class="p">{</span><span class="s1">'int'</span><span class="p">:</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">hex</span><span class="p">(</span><span class="n">x</span><span class="p">)})</span>
<span class="go">'[0x0 0x1 0x2]'</span>
</pre></div>
</div>
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