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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 Index</a>.</p>
<p>This Python package is a wrapper round 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="install">Install<a class="anchor" aria-label="anchor" href="#install"></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="cb1"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" 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="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
<span id="cb2-6"><a href="#cb2-6" 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="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" 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="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
<span id="cb4-6"><a href="#cb4-6" 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="cb4-7"><a href="#cb4-7" 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="cb4-8"><a href="#cb4-8" tabindex="-1"></a>}</span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a></span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
<span id="cb4-12"><a href="#cb4-12" 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="cb4-13"><a href="#cb4-13" 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="cb4-14"><a href="#cb4-14" tabindex="-1"></a></span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
<span id="cb4-16"><a href="#cb4-16" 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="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>antibiotics</code>, <code>clinical_breakpoints</code>, and
<code>example_isolates</code>, are now available as regular Python data
frames:</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>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="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>AMR.antibiotics</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 class="section level3">
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
</h3>
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
<span id="cb7-6"><a href="#cb7-6" 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="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" 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="cb9-2"><a href="#cb9-2" 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="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" 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="cb10-2"><a href="#cb10-2" 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>
<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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