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<h1>Integrating Arrow, Python, and R</h1>
<small class="dont-index">Source: <a href="https://github.com/apache/arrow/blob/main/r/vignettes/python.Rmd" class="external-link"><code>vignettes/python.Rmd</code></a></small>
<div class="d-none name"><code>python.Rmd</code></div>
</div>
<p>The arrow package provides <a href="https://rstudio.github.io/reticulate/" class="external-link">reticulate</a> methods for
passing data between R and Python within the same process. This article
provides a brief overview.</p>
<p>Code in this article assumes arrow and reticulate are both
loaded:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/apache/arrow/" class="external-link">arrow</a></span>, warn.conflicts <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://rstudio.github.io/reticulate/" class="external-link">reticulate</a></span>, warn.conflicts <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
<div class="section level2">
<h2 id="motivation">Motivation<a class="anchor" aria-label="anchor" href="#motivation"></a>
</h2>
<p>One reason you might want to use PyArrow in R is to take advantage of
functionality that is better supported in Python than in R at the
current state of development. For example, at one point in time the R
arrow package didn’t support <code><a href="../reference/concat_arrays.html">concat_arrays()</a></code> but PyArrow
did, so this would have been a good use case at that time. At the time
of current writing PyArrow has more comprehensive support for <a href="https://arrow.apache.org/docs/format/Flight.html" class="external-link">Arrow Flight</a>
than the R package – but see <a href="./flight.html">the article on
Flight support in arrow</a> – so that would be another instance in which
PyArrow would be of benefit to R users.</p>
<p>A second reason that R users may want to use PyArrow is to
efficiently pass data objects between R and Python. With large data
sets, it can be quite costly – in terms of time and CPU cycles – to
perform the copy and covert operations required to translate a native
data structure in R (e.g., a data frame) to an analogous structure in
Python (e.g., a Pandas DataFrame) and vice versa. Because Arrow data
objects such as Tables have the same in-memory format in R and Python,
it is possible to perform “zero-copy” data transfers, in which only the
metadata needs to be passed between languages. As illustrated later,
this drastically improves performance.</p>
</div>
<div class="section level2">
<h2 id="installing-pyarrow">Installing PyArrow<a class="anchor" aria-label="anchor" href="#installing-pyarrow"></a>
</h2>
<p>To use Arrow in Python, the <code>pyarrow</code> library needs to be
installed. For example, you may wish to create a Python <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual
environment</a> containing the <code>pyarrow</code> library. A virtual
environment is a specific Python installation created for one project or
purpose. It is a good practice to use specific environments in Python so
that updating a package doesn’t impact packages in other projects.</p>
<p>You can perform the set up from within R. Let’s suppose you want to
call your virtual environment something like
<code>my-pyarrow-env</code>. Your setup code would look like this:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rstudio.github.io/reticulate/reference/virtualenv-tools.html" class="external-link">virtualenv_create</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span>
<span><span class="fu"><a href="../reference/install_pyarrow.html">install_pyarrow</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span></code></pre></div>
<p>If you want to install a development version of <code>pyarrow</code>
to the virtual environment, add <code>nightly = TRUE</code> to the
<code><a href="../reference/install_pyarrow.html">install_pyarrow()</a></code> command:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/install_pyarrow.html">install_pyarrow</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span>, nightly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
<p>Note that you don’t have to use virtual environments. If you prefer
<a href="https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html" class="external-link">conda
environments</a>, you can use this setup code:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rstudio.github.io/reticulate/reference/conda-tools.html" class="external-link">conda_create</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span>
<span><span class="fu"><a href="../reference/install_pyarrow.html">install_pyarrow</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span></code></pre></div>
<p>To learn more about installing and configuring Python from R, see the
<a href="https://rstudio.github.io/reticulate/articles/python_packages.html" class="external-link">reticulate
documentation</a>, which discusses the topic in more detail.</p>
</div>
<div class="section level2">
<h2 id="importing-pyarrow">Importing PyArrow<a class="anchor" aria-label="anchor" href="#importing-pyarrow"></a>
</h2>
<p>Assuming that arrow and reticulate are both loaded in R, your first
step is to make sure that the correct Python environment is being used.
To do that with a virtual environment, use a command like this:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rstudio.github.io/reticulate/reference/use_python.html" class="external-link">use_virtualenv</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span></code></pre></div>
<p>For a conda environment use the following:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rstudio.github.io/reticulate/reference/use_python.html" class="external-link">use_condaenv</a></span><span class="op">(</span><span class="st">"my-pyarrow-env"</span><span class="op">)</span></span></code></pre></div>
<p>Once you have done this, the next step is to import
<code>pyarrow</code> into the Python session as shown below:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">pa</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rstudio.github.io/reticulate/reference/import.html" class="external-link">import</a></span><span class="op">(</span><span class="st">"pyarrow"</span><span class="op">)</span></span></code></pre></div>
<p>Executing this command in R is the equivalent of the following import
in Python:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pyarrow <span class="im">as</span> pa</span></code></pre></div>
<p>It may be a good idea to check your <code>pyarrow</code> version too,
as shown below:</p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">pa</span><span class="op">$</span><span class="va">`__version__`</span></span></code></pre></div>
<pre><code><span><span class="co">## [1] "8.0.0"</span></span></code></pre>
<p>Support for passing data to and from R is included in
<code>pyarrow</code> versions 0.17 and greater.</p>
</div>
<div class="section level2">
<h2 id="using-pyarrow">Using PyArrow<a class="anchor" aria-label="anchor" href="#using-pyarrow"></a>
</h2>
<p>You can use the reticulate function <code><a href="https://rstudio.github.io/reticulate/reference/r-py-conversion.html" class="external-link">r_to_py()</a></code> to pass
objects from R to Python, and similarly you can use
<code><a href="https://rstudio.github.io/reticulate/reference/r-py-conversion.html" class="external-link">py_to_r()</a></code> to pull objects from the Python session into R.
To illustrate this, let’s create two objects in R:
<code>df_random</code> is an R data frame containing 100 million rows of
random data, and <code>tb_random</code> is the same data stored as an
Arrow Table:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span>
<span><span class="va">nrows</span> <span class="op">&lt;-</span> <span class="fl">10</span><span class="op">^</span><span class="fl">8</span></span>
<span><span class="va">df_random</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
<span> x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">nrows</span><span class="op">)</span>, </span>
<span> y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">nrows</span><span class="op">)</span>,</span>
<span> subset <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fl">10</span>, <span class="va">nrows</span>, replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="op">)</span></span>
<span><span class="va">tb_random</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/table.html">arrow_table</a></span><span class="op">(</span><span class="va">df_random</span><span class="op">)</span></span></code></pre></div>
<p>Transferring the data from R to Python without Arrow is a
time-consuming process because the underlying object has to be copied
and converted to a Python data structure:</p>
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="op">{</span></span>
<span> <span class="va">df_py</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rstudio.github.io/reticulate/reference/r-py-conversion.html" class="external-link">r_to_py</a></span><span class="op">(</span><span class="va">df_random</span><span class="op">)</span></span>
<span><span class="op">}</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## user system elapsed </span></span>
<span><span class="co">## 0.307 5.172 5.529 </span></span></code></pre>
<p>In contrast, sending the Arrow Table across happens almost
instantaneously:</p>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="op">{</span></span>
<span> <span class="va">tb_py</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rstudio.github.io/reticulate/reference/r-py-conversion.html" class="external-link">r_to_py</a></span><span class="op">(</span><span class="va">tb_random</span><span class="op">)</span></span>
<span><span class="op">}</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## user system elapsed </span></span>
<span><span class="co">## 0.004 0.000 0.003 </span></span></code></pre>
<p>“Send”, however, isn’t really the correct word. Internally, we’re
passing pointers to the data between the R and Python interpreters
running together in the same process, without copying anything. Nothing
is being sent: we’re sharing and accessing the same internal Arrow
memory buffers.</p>
<p>It’s possible to send data the other direction also. For example
let’s create an <code>Array</code> in pyarrow.</p>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">a</span> <span class="op">&lt;-</span> <span class="va">pa</span><span class="op">$</span><span class="fu">array</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="va">a</span></span></code></pre></div>
<pre><code><span><span class="co">## Array</span></span>
<span><span class="co">## &lt;double&gt;</span></span>
<span><span class="co">## [</span></span>
<span><span class="co">## 1,</span></span>
<span><span class="co">## 2,</span></span>
<span><span class="co">## 3</span></span>
<span><span class="co">## ]</span></span></code></pre>
<p>Notice that <code>a</code> is now an <code>Array</code> object in
your R session – even though you created it in Python – and you can
apply R methods on it:</p>
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">a</span><span class="op">[</span><span class="va">a</span> <span class="op">&gt;</span> <span class="fl">1</span><span class="op">]</span></span></code></pre></div>
<pre><code><span><span class="co">## Array</span></span>
<span><span class="co">## &lt;double&gt;</span></span>
<span><span class="co">## [</span></span>
<span><span class="co">## 2,</span></span>
<span><span class="co">## 3</span></span>
<span><span class="co">## ]</span></span></code></pre>
<p>Similarly, you can combine this object with Arrow objects created in
R, and you can use PyArrow methods like <code>pa$concat_arrays()</code>
to do so:</p>
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">b</span> <span class="op">&lt;-</span> <span class="va">Array</span><span class="op">$</span><span class="fu">create</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">6</span>, <span class="fl">7</span>, <span class="fl">8</span>, <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="va">a_and_b</span> <span class="op">&lt;-</span> <span class="va">pa</span><span class="op">$</span><span class="fu">concat_arrays</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">a</span>, <span class="va">b</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="va">a_and_b</span></span></code></pre></div>
<pre><code><span><span class="co">## Array</span></span>
<span><span class="co">## &lt;double&gt;</span></span>
<span><span class="co">## [</span></span>
<span><span class="co">## 1,</span></span>
<span><span class="co">## 2,</span></span>
<span><span class="co">## 3,</span></span>
<span><span class="co">## 5,</span></span>
<span><span class="co">## 6,</span></span>
<span><span class="co">## 7,</span></span>
<span><span class="co">## 8,</span></span>
<span><span class="co">## 9</span></span>
<span><span class="co">## ]</span></span></code></pre>
<p>Now you have a single Array in R.</p>
</div>
<div class="section level2">
<h2 id="further-reading">Further reading<a class="anchor" aria-label="anchor" href="#further-reading"></a>
</h2>
<ul>
<li>To learn more about installing and configuring Python from R, see
the <a href="https://rstudio.github.io/reticulate/articles/python_packages.html" class="external-link">reticulate
documentation</a>.</li>
<li>To learn PyArrow, see the official <a href="https://arrow.apache.org/docs/python/" class="external-link">PyArrow Documentation</a>
and <a href="https://arrow.apache.org/cookbook/py/" class="external-link">Apache Arrow Python
Cookbook</a>.</li>
<li>R/Python integration in Arrow is also discussed in the <a href="https://arrow.apache.org/docs/python/integration/python_r.html" class="external-link">PyArrow
Integrations Documentation</a>, and in this <a href="https://blog.djnavarro.net/posts/2022-09-09_reticulated-arrow/" class="external-link">blog
post about reticulate integration in Arrow</a>.</li>
<li>The integration between R Arrow and PyArrow is supported through the
<a href="https://arrow.apache.org/docs/format/CDataInterface.html#c-data-interface" class="external-link">Arrow
C data interface</a>.</li>
<li>To learn more about Arrow data objects, see the <a href="./data_objects.html">data objects article</a>.</li>
</ul>
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