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<img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
<div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
</div>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>The <code>AMR</code> package for R is a powerful tool for
antimicrobial resistance (AMR) analysis. It provides extensive features
for handling microbial and antimicrobial data. However, for those who
work primarily in Python, we now have a more intuitive option available:
the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
package</a>.</p>
<p>This Python package is a wrapper around the <code>AMR</code> R
package. It uses the <code>rpy2</code> package internally. Despite the
need to have R installed, Python users can now easily work with AMR data
directly through Python code.</p>
</div>
<div class="section level2">
<h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
</h2>
<p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
(venv)</a>. You can set up such an environment by running:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
<p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
the environment</a>, after which the venv is ready to work with.</p>
</div>
<div class="section level2">
<h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
</h2>
<ol style="list-style-type: decimal">
<li>
<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
just run:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
</li>
<li>
<p>Make sure you have R installed. There is <strong>no need to
install the <code>AMR</code> R package</strong>, as it will be installed
automatically.</p>
<p>For Linux:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
<p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
download page</a> to download and install R.</p>
</li>
</ol>
</div>
<div class="section level2">
<h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
</h2>
<div class="section level3">
<h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
</h3>
<p>Here’s an example that demonstrates how to clean microorganism and
drug names using the <code>AMR</code> Python package:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
<table class="table">
<thead><tr class="header">
<th>MOs</th>
<th>Drug</th>
<th>MO_clean</th>
<th>Drug_clean</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>E. coli</td>
<td>Cipro</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="even">
<td>ESCCOL</td>
<td>CIP</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="odd">
<td>esco</td>
<td>J01MA02</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
<tr class="even">
<td>Esche coli</td>
<td>Ciproxin</td>
<td>Escherichia coli</td>
<td>Ciprofloxacin</td>
</tr>
</tbody>
</table>
<div class="section level4">
<h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
</h4>
<ul>
<li><p><strong>mo_name:</strong> This function standardises
microorganism names. Here, different variations of <em>Escherichia
coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
all converted into the correct, standardised form, “Escherichia
coli”.</p></li>
<li><p><strong>ab_name</strong>: Similarly, this function standardises
antimicrobial names. The different representations of ciprofloxacin
(e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
the standard name, “Ciprofloxacin”.</p></li>
</ul>
</div>
</div>
<div class="section level3">
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<pre><code>[0.59555556]</code></pre>
</div>
<div class="section level3">
<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
</h3>
<p>One of the core functions of the <code>AMR</code> package is
generating an antibiogram, a table that summarises the antimicrobial
susceptibility of bacterial isolates. Here’s how you can generate an
antibiogram from Python:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="22%">
<col width="22%">
<col width="22%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>CoNS</td>
<td>7% (10/142)</td>
<td>73% (183/252)</td>
<td>30% (10/33)</td>
</tr>
<tr class="even">
<td>E. coli</td>
<td>50% (196/392)</td>
<td>88% (399/456)</td>
<td>94% (393/416)</td>
</tr>
<tr class="odd">
<td>K. pneumoniae</td>
<td>0% (0/58)</td>
<td>96% (53/55)</td>
<td>89% (47/53)</td>
</tr>
<tr class="even">
<td>P. aeruginosa</td>
<td>0% (0/30)</td>
<td>100% (30/30)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>P. mirabilis</td>
<td>None</td>
<td>94% (34/36)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. aureus</td>
<td>6% (8/131)</td>
<td>90% (171/191)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. epidermidis</td>
<td>1% (1/91)</td>
<td>64% (87/136)</td>
<td>None</td>
</tr>
<tr class="even">
<td>S. hominis</td>
<td>None</td>
<td>80% (56/70)</td>
<td>None</td>
</tr>
<tr class="odd">
<td>S. pneumoniae</td>
<td>100% (112/112)</td>
<td>None</td>
<td>100% (112/112)</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
<table class="table">
<colgroup>
<col width="20%">
<col width="22%">
<col width="23%">
<col width="33%">
</colgroup>
<thead><tr class="header">
<th>Pathogen</th>
<th>Amoxicillin</th>
<th>Ciprofloxacin</th>
<th>Piperacillin/tazobactam</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>Gram-negative</td>
<td>36% (226/631)</td>
<td>91% (621/684)</td>
<td>88% (565/641)</td>
</tr>
<tr class="even">
<td>Gram-positive</td>
<td>43% (305/703)</td>
<td>77% (560/724)</td>
<td>86% (296/345)</td>
</tr>
</tbody>
</table>
<p>In this example, we generate an antibiogram by selecting various
antibiotics.</p>
</div>
<div class="section level3">
<h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
</h3>
<p>As a Python user, you might like that the most important data sets of
the <code>AMR</code> R package, <code>microorganisms</code>,
<code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
<code>example_isolates</code>, are now available as regular Python data
frames:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
<table class="table">
<colgroup>
<col width="11%">
<col width="29%">
<col width="8%">
<col width="8%">
<col width="8%">
<col width="10%">
<col width="13%">
<col width="9%">
</colgroup>
<thead><tr class="header">
<th>mo</th>
<th>fullname</th>
<th>status</th>
<th>kingdom</th>
<th>gbif</th>
<th>gbif_parent</th>
<th>gbif_renamed_to</th>
<th>prevalence</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>B_GRAMN</td>
<td>(unknown Gram-negatives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_GRAMP</td>
<td>(unknown Gram-positives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ANAER-NEG</td>
<td>(unknown anaerobic Gram-negatives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ANAER-POS</td>
<td>(unknown anaerobic Gram-positives)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ANAER</td>
<td>(unknown anaerobic bacteria)</td>
<td>unknown</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
</tr>
<tr class="odd">
<td>B_ZYMMN_POMC</td>
<td>Zymomonas pomaceae</td>
<td>accepted</td>
<td>Bacteria</td>
<td>10744418</td>
<td>3221412</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ZYMPH</td>
<td>Zymophilus</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>9475166</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>B_ZYMPH_PCVR</td>
<td>Zymophilus paucivorans</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>B_ZYMPH_RFFN</td>
<td>Zymophilus raffinosivorans</td>
<td>synonym</td>
<td>Bacteria</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>2.0</td>
</tr>
<tr class="odd">
<td>F_ZYZYG</td>
<td>Zyzygomyces</td>
<td>unknown</td>
<td>Fungi</td>
<td>None</td>
<td>7581</td>
<td>None</td>
<td>2.0</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
<table style="width:100%;" class="table">
<colgroup>
<col width="4%">
<col width="12%">
<col width="20%">
<col width="25%">
<col width="9%">
<col width="11%">
<col width="7%">
<col width="9%">
</colgroup>
<thead><tr class="header">
<th>ab</th>
<th>cid</th>
<th>name</th>
<th>group</th>
<th>oral_ddd</th>
<th>oral_units</th>
<th>iv_ddd</th>
<th>iv_units</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>AMA</td>
<td>4649.0</td>
<td>4-aminosalicylic acid</td>
<td>Antimycobacterials</td>
<td>12.00</td>
<td>g</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ACM</td>
<td>6450012.0</td>
<td>Acetylmidecamycin</td>
<td>Macrolides/lincosamides</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>ASP</td>
<td>49787020.0</td>
<td>Acetylspiramycin</td>
<td>Macrolides/lincosamides</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ALS</td>
<td>8954.0</td>
<td>Aldesulfone sodium</td>
<td>Other antibacterials</td>
<td>0.33</td>
<td>g</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>AMK</td>
<td>37768.0</td>
<td>Amikacin</td>
<td>Aminoglycosides</td>
<td>NaN</td>
<td>None</td>
<td>1.0</td>
<td>g</td>
</tr>
<tr class="even">
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
<td>…</td>
</tr>
<tr class="odd">
<td>VIR</td>
<td>11979535.0</td>
<td>Virginiamycine</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>VOR</td>
<td>71616.0</td>
<td>Voriconazole</td>
<td>Antifungals/antimycotics</td>
<td>0.40</td>
<td>g</td>
<td>0.4</td>
<td>g</td>
</tr>
<tr class="odd">
<td>XBR</td>
<td>72144.0</td>
<td>Xibornol</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="even">
<td>ZID</td>
<td>77846445.0</td>
<td>Zidebactam</td>
<td>Other antibacterials</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
<tr class="odd">
<td>ZFD</td>
<td>NaN</td>
<td>Zoliflodacin</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
<td>NaN</td>
<td>None</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section level2">
<h2 id="installation-channels">Installation Channels<a class="anchor" aria-label="anchor" href="#installation-channels"></a>
</h2>
<div class="section level3">
<h3 id="stable-release-cran">Stable Release (CRAN)<a class="anchor" aria-label="anchor" href="#stable-release-cran"></a>
</h3>
<p>The default <code>AMR</code> Python package uses the latest stable
version of the <code>AMR</code> R package, published on CRAN. After
running <code>pip install AMR</code>, import it as usual:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>AMR.example_isolates</span></code></pre></div>
</div>
<div class="section level3">
<h3 id="development-version-github">Development Version (GitHub)<a class="anchor" aria-label="anchor" href="#development-version-github"></a>
</h3>
<p>To use the latest development version of the <code>AMR</code> R
package (sourced directly from GitHub), import the <code>beta</code>
sub-package and alias it as <code>AMR</code>:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a><span class="im">import</span> AMR.beta <span class="im">as</span> AMR</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>AMR.example_isolates</span></code></pre></div>
<p>Aliasing with <code>as AMR</code> keeps all downstream code identical
to the stable import. Switching between the stable release and the
development version requires changing only the import line — nothing
else in your script needs to change.</p>
</div>
</div>
<div class="section level2">
<h2 id="sir-classification-with-as_sir">SIR Classification with <code>as_sir()</code><a class="anchor" aria-label="anchor" href="#sir-classification-with-as_sir"></a>
</h2>
<div class="section level3">
<h3 id="using-enforce_method">Using <code>enforce_method</code><a class="anchor" aria-label="anchor" href="#using-enforce_method"></a>
</h3>
<p>The <code>as_sir()</code> function in R uses S3 method dispatch to
select the correct calculation method based on the input class:
<code>&lt;mic&gt;</code> for MIC values and <code>&lt;disk&gt;</code>
for disk diffusion values. Because Python objects do not carry R class
attributes through the <code>rpy2</code> bridge, this automatic dispatch
may not resolve correctly.</p>
<p>To explicitly specify the input type, use the
<code>enforce_method</code> argument:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="co"># Treat the column as MIC values — maps to R's as.sir.mic()</span></span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>AMR.as_sir(df[<span class="st">"MIC_col"</span>], mo<span class="op">=</span><span class="st">"E. coli"</span>, ab<span class="op">=</span><span class="st">"AMX"</span>, guideline<span class="op">=</span><span class="st">"EUCAST"</span>, enforce_method<span class="op">=</span><span class="st">"mic"</span>)</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a></span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a><span class="co"># Treat the column as disk diffusion values — maps to R's as.sir.disk()</span></span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>AMR.as_sir(df[<span class="st">"disk_col"</span>], mo<span class="op">=</span><span class="st">"E. coli"</span>, ab<span class="op">=</span><span class="st">"AMX"</span>, guideline<span class="op">=</span><span class="st">"EUCAST"</span>, enforce_method<span class="op">=</span><span class="st">"disk"</span>)</span></code></pre></div>
<p>Without <code>enforce_method</code>, R falls back to class-based
dispatch on the raw Python input, which may fail or return unexpected
results. Always supply <code>enforce_method</code> when calling
<code>as_sir()</code> from Python.</p>
</div>
</div>
<div class="section level2">
<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
</h2>
<p>With the <code>AMR</code> Python package, Python users can now
effortlessly call R functions from the <code>AMR</code> R package. This
eliminates the need for complex <code>rpy2</code> configurations and
provides a clean, easy-to-use interface for antimicrobial resistance
analysis. The examples provided above demonstrate how this can be
applied to typical workflows, such as standardising microorganism and
antimicrobial names or calculating resistance.</p>
<p>By just running <code>import AMR</code>, users can seamlessly
integrate the robust features of the R <code>AMR</code> package into
Python workflows.</p>
<p>Whether you’re cleaning data or analysing resistance patterns, the
<code>AMR</code> Python package makes it easy to work with AMR data in
Python.</p>
</div>
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