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Built site for AMR@2.1.1.9133: 9520977
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@@ -29,7 +29,7 @@
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9125</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9133</small>
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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@@ -200,6 +200,129 @@ the standard name, “Ciprofloxacin”.</p></li>
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</div>
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</div>
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<div class="section level3">
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<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
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</h3>
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<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> AMR</span>
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<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
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<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
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<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
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<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
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<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
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<pre><code>[0.59555556]</code></pre>
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</div>
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<div class="section level3">
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<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
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</h3>
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<p>One of the core functions of the <code>AMR</code> package is
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generating an antibiogram, a table that summarises the antimicrobial
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susceptibility of bacterial isolates. Here’s how you can generate an
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antibiogram from Python:</p>
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<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" 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>
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<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="22%">
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<col width="22%">
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<col width="22%">
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<col width="33%">
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</colgroup>
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<thead><tr class="header">
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<th>Pathogen</th>
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<th>Amoxicillin</th>
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<th>Ciprofloxacin</th>
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<th>Piperacillin/tazobactam</th>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td>CoNS</td>
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<td>7% (10/142)</td>
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<td>73% (183/252)</td>
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<td>30% (10/33)</td>
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</tr>
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<tr class="even">
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<td>E. coli</td>
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<td>50% (196/392)</td>
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<td>88% (399/456)</td>
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<td>94% (393/416)</td>
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</tr>
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<tr class="odd">
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<td>K. pneumoniae</td>
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<td>0% (0/58)</td>
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<td>96% (53/55)</td>
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<td>89% (47/53)</td>
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</tr>
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<tr class="even">
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<td>P. aeruginosa</td>
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<td>0% (0/30)</td>
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<td>100% (30/30)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>P. mirabilis</td>
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<td>None</td>
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<td>94% (34/36)</td>
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<td>None</td>
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</tr>
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<tr class="even">
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<td>S. aureus</td>
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<td>6% (8/131)</td>
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<td>90% (171/191)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>S. epidermidis</td>
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<td>1% (1/91)</td>
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<td>64% (87/136)</td>
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<td>None</td>
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</tr>
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<tr class="even">
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<td>S. hominis</td>
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<td>None</td>
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<td>80% (56/70)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>S. pneumoniae</td>
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<td>100% (112/112)</td>
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<td>None</td>
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<td>100% (112/112)</td>
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</tr>
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</tbody>
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</table>
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<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" 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>
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<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="20%">
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<col width="22%">
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<col width="23%">
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<col width="33%">
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</colgroup>
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<thead><tr class="header">
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<th>Pathogen</th>
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<th>Amoxicillin</th>
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<th>Ciprofloxacin</th>
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<th>Piperacillin/tazobactam</th>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td>Gram-negative</td>
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<td>36% (226/631)</td>
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<td>91% (621/684)</td>
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<td>88% (565/641)</td>
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</tr>
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<tr class="even">
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<td>Gram-positive</td>
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<td>43% (305/703)</td>
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<td>77% (560/724)</td>
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<td>86% (296/345)</td>
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</tr>
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</tbody>
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</table>
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<p>In this example, we generate an antibiogram by selecting various
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antibiotics.</p>
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</div>
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<div class="section level3">
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<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>
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</h3>
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<p>As a Python user, you might like that the most important data sets of
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@@ -207,7 +330,7 @@ the <code>AMR</code> R package, <code>microorganisms</code>,
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<code>antibiotics</code>, <code>clinical_breakpoints</code>, and
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<code>example_isolates</code>, are now available as regular Python data
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frames:</p>
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<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>
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<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="11%">
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@@ -342,7 +465,7 @@ frames:</p>
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</tr>
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</tbody>
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</table>
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<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>
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<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.antibiotics</span></code></pre></div>
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<table style="width:100%;" class="table">
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<colgroup>
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<col width="4%">
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@@ -478,129 +601,6 @@ frames:</p>
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</tbody>
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</table>
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</div>
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<div class="section level3">
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<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
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</h3>
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<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>
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<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
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<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
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<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
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<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>
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<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
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<pre><code>[0.59555556]</code></pre>
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</div>
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<div class="section level3">
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<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
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</h3>
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<p>One of the core functions of the <code>AMR</code> package is
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generating an antibiogram, a table that summarises the antimicrobial
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susceptibility of bacterial isolates. Here’s how you can generate an
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antibiogram from Python:</p>
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<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>
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<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="22%">
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<col width="22%">
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<col width="22%">
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<col width="33%">
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</colgroup>
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<thead><tr class="header">
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<th>Pathogen</th>
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<th>Amoxicillin</th>
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<th>Ciprofloxacin</th>
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<th>Piperacillin/tazobactam</th>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td>CoNS</td>
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<td>7% (10/142)</td>
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<td>73% (183/252)</td>
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<td>30% (10/33)</td>
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</tr>
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<tr class="even">
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<td>E. coli</td>
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<td>50% (196/392)</td>
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<td>88% (399/456)</td>
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<td>94% (393/416)</td>
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</tr>
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<tr class="odd">
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<td>K. pneumoniae</td>
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<td>0% (0/58)</td>
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<td>96% (53/55)</td>
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<td>89% (47/53)</td>
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</tr>
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<tr class="even">
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<td>P. aeruginosa</td>
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<td>0% (0/30)</td>
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<td>100% (30/30)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>P. mirabilis</td>
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<td>None</td>
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<td>94% (34/36)</td>
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<td>None</td>
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</tr>
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<tr class="even">
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<td>S. aureus</td>
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<td>6% (8/131)</td>
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<td>90% (171/191)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>S. epidermidis</td>
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<td>1% (1/91)</td>
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<td>64% (87/136)</td>
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<td>None</td>
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</tr>
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<tr class="even">
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<td>S. hominis</td>
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<td>None</td>
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<td>80% (56/70)</td>
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<td>None</td>
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</tr>
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<tr class="odd">
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<td>S. pneumoniae</td>
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<td>100% (112/112)</td>
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<td>None</td>
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<td>100% (112/112)</td>
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</tr>
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</tbody>
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</table>
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<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>
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<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
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<table class="table">
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<colgroup>
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<col width="20%">
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<col width="22%">
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<col width="23%">
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<col width="33%">
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</colgroup>
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<thead><tr class="header">
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<th>Pathogen</th>
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<th>Amoxicillin</th>
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<th>Ciprofloxacin</th>
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<th>Piperacillin/tazobactam</th>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td>Gram-negative</td>
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<td>36% (226/631)</td>
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<td>91% (621/684)</td>
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<td>88% (565/641)</td>
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</tr>
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<tr class="even">
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<td>Gram-positive</td>
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<td>43% (305/703)</td>
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<td>77% (560/724)</td>
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<td>86% (296/345)</td>
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</tr>
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</tbody>
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</table>
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<p>In this example, we generate an antibiogram by selecting various
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antibiotics.</p>
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</div>
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</div>
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<div class="section level2">
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<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
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