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<h1>Tugas 5 | Pipelines With PCA</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#web-orches-orches">Web Orches Orches</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dbms-ke-power-bi">DBMS ke Power BI</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#hasil-dari-postgresql-local-ke-power-bi">Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">PostgreSQL</span> <span class="pre">Local</span></code></strong> ke Power Bi</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#hasil-dari-sql-server-local-ke-power-bi">Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">SQL</span> <span class="pre">SERVER</span> <span class="pre">Local</span></code></strong> ke Power Bi</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#hasil-mysql-cloud">Hasil <code class="docutils literal notranslate"><span class="pre">MySQL</span> <span class="pre">Cloud</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#hasil-dari-mysql-local-ke-power-bi">Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">MySQL</span> <span class="pre">Local</span></code></strong> ke Power Bi</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#elephantsql-ke-python">Elephantsql ke Python</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#get-data-collecting-data">Get Data (<em>collecting data</em>)</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#mengumpulkan-data-menjadi-satu-dari-data-yang-terpisah-diatas">Mengumpulkan data menjadi satu dari data yang terpisah diatas</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#label-encoding">Label Encoding</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#inisialisasi-data-ke-orchest">Inisialisasi Data ke Orchest</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#prepocessing-dan-split-data">Prepocessing dan Split Data</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#perhitungan-menggunakan-berbagai-model">Perhitungan Menggunakan Berbagai Model</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-linear-reqression">Model Linear Reqression</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-decision-tree">Model Decision Tree</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#model-svm">Model SVM</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#collect-result">Collect Result</a></li>
</ul>
</nav>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="tugas-5-pipelines-with-pca">
<h1><strong>Tugas 5 | Pipelines With PCA</strong><a class="headerlink" href="#tugas-5-pipelines-with-pca" title="Permalink to this heading">#</a></h1>
<div class="section" id="web-orches-orches">
<h2>Web Orches <a class="reference external" href="https://www.orchest.io/">Orches</a><a class="headerlink" href="#web-orches-orches" title="Permalink to this heading">#</a></h2>
</div>
<div class="section" id="dbms-ke-power-bi">
<h2>DBMS ke Power BI<a class="headerlink" href="#dbms-ke-power-bi" title="Permalink to this heading">#</a></h2>
<p>Berikut Deskripsi Atau Langkah langkah untuk menggabungkan data dari berbagai sumber seperti : <strong>Mysql</strong>, <strong>SQL</strong> <strong>Server</strong>, <strong>PostgresSql</strong>, dan <em><strong>elephantsql</strong></em></p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># from google.colab import drive</span>
<span class="c1"># drive.mount('/content/drive')</span>
</pre></div>
</div>
</div>
</div>
<p>Pada tahap ini, mengumpulkan data dari berbagai <code class="docutils literal notranslate"><span class="pre">dbms</span></code> ke <code class="docutils literal notranslate"><span class="pre">power</span> <span class="pre">bi</span></code> untuk dijadikan file <code class="docutils literal notranslate"><span class="pre">CSV</span></code>. Pada Power Bi, select <code class="docutils literal notranslate"><span class="pre">Get</span> <span class="pre">Data</span></code> dan pilih connector seperti dibawah ini. Lalu masukkan server,hostname dan nama databasenya.</p>
<p><img alt="image.png" src="data:image/png;base64,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" />
<img alt="image.png" src="data:image/png;base64,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" />
<img alt="image.png" src="data:image/png;base64,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" /></p>
<div class="section" id="hasil-dari-postgresql-local-ke-power-bi">
<h3>Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">PostgreSQL</span> <span class="pre">Local</span></code></strong> ke Power Bi<a class="headerlink" href="#hasil-dari-postgresql-local-ke-power-bi" title="Permalink to this heading">#</a></h3>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.read_csv('/content/drive/MyDrive/prosaindata/tugas/Data/data_postgresSql.csv')</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'https://raw.githubusercontent.com/LALA09-erha/File-semester5/master/PSD/data_postgresSql.csv'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_html">
<div id="df-394365d0-af90-4e40-9de6-0b0f2c98f131">
<div class="colab-df-container">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sepallength</th>
<th>sepalwidth</th>
<th>petallength</th>
<th>petalwidth</th>
<th>class</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>5.1</td>
<td>3.5</td>
<td>1.4</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>1</th>
<td>4.9</td>
<td>3.0</td>
<td>1.4</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>2</th>
<td>4.7</td>
<td>3.2</td>
<td>1.3</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>3</th>
<td>4.6</td>
<td>3.1</td>
<td>1.5</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>4</th>
<td>5.0</td>
<td>3.6</td>
<td>1.4</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
</tbody>
</table>
</div>
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<style>
.colab-df-container {
display:flex;
flex-wrap:wrap;
gap: 12px;
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.colab-df-convert {
background-color: #E8F0FE;
border: none;
border-radius: 50%;
cursor: pointer;
display: none;
fill: #1967D2;
height: 32px;
padding: 0 0 0 0;
width: 32px;
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.colab-df-convert:hover {
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box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);
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background-color: #3B4455;
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[theme=dark] .colab-df-convert:hover {
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fill: #FFFFFF;
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const buttonEl =
document.querySelector('#df-394365d0-af90-4e40-9de6-0b0f2c98f131 button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-394365d0-af90-4e40-9de6-0b0f2c98f131');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
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if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
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</div>
</div>
</div></div>
</div>
<p>Mengambil data <code class="docutils literal notranslate"><span class="pre">Sepal</span> <span class="pre">Length</span></code></p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_postgreSQL</span><span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'sepalwidth'</span><span class="p">,</span><span class="s1">'petallength'</span><span class="p">,</span><span class="s1">'petalwidth'</span><span class="p">,</span><span class="s1">'class'</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="hasil-dari-sql-server-local-ke-power-bi">
<h3>Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">SQL</span> <span class="pre">SERVER</span> <span class="pre">Local</span></code></strong> ke Power Bi<a class="headerlink" href="#hasil-dari-sql-server-local-ke-power-bi" title="Permalink to this heading">#</a></h3>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.read_csv('/content/drive/MyDrive/prosaindata/tugas/Data/data_sqlServer.csv')</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'https://raw.githubusercontent.com/LALA09-erha/File-semester5/master/PSD/data_sqlServer.csv'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_html">
<div id="df-ecbd34be-ba8c-44dd-98f7-e82e10c7d9ac">
<div class="colab-df-container">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>sepallength</th>
<th>sepalwidth</th>
<th>petallength</th>
<th>petalwidth</th>
<th>class</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>6</td>
<td>4.0</td>
<td>1.2</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>1</th>
<td>6</td>
<td>4.4</td>
<td>1.5</td>
<td>0.4</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>2</th>
<td>6</td>
<td>3.8</td>
<td>1.7</td>
<td>0.3</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>3</th>
<td>6</td>
<td>4.2</td>
<td>1.4</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
<tr>
<th>4</th>
<td>6</td>
<td>3.5</td>
<td>1.3</td>
<td>0.2</td>
<td>Iris-setosa</td>
</tr>
</tbody>
</table>
</div>
<button class="colab-df-convert" onclick="convertToInteractive('df-ecbd34be-ba8c-44dd-98f7-e82e10c7d9ac')"
title="Convert this dataframe to an interactive table."
style="display:none;">
<svg xmlns="http://www.w3.org/2000/svg" height="24px"viewBox="0 0 24 24"
width="24px">
<path d="M0 0h24v24H0V0z" fill="none"/>
<path d="M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z"/><path d="M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z"/>
</svg>
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<style>
.colab-df-container {
display:flex;
flex-wrap:wrap;
gap: 12px;
}
.colab-df-convert {
background-color: #E8F0FE;
border: none;
border-radius: 50%;
cursor: pointer;
display: none;
fill: #1967D2;
height: 32px;
padding: 0 0 0 0;
width: 32px;
}
.colab-df-convert:hover {
background-color: #E2EBFA;
box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);
fill: #174EA6;
}
[theme=dark] .colab-df-convert {
background-color: #3B4455;
fill: #D2E3FC;
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<script>
const buttonEl =
document.querySelector('#df-ecbd34be-ba8c-44dd-98f7-e82e10c7d9ac button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-ecbd34be-ba8c-44dd-98f7-e82e10c7d9ac');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
</div>
</div></div>
</div>
<p>Mengambil data <code class="docutils literal notranslate"><span class="pre">petal</span> <span class="pre">width</span></code> dan <code class="docutils literal notranslate"><span class="pre">class</span></code></p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_SqlServer</span><span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'sepallength'</span><span class="p">,</span><span class="s1">'sepalwidth'</span><span class="p">,</span><span class="s1">'petallength'</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="hasil-mysql-cloud">
<h3>Hasil <code class="docutils literal notranslate"><span class="pre">MySQL</span> <span class="pre">Cloud</span></code><a class="headerlink" href="#hasil-mysql-cloud" title="Permalink to this heading">#</a></h3>
<p>untuk menghungkan menggunakan connector untuk menginstallnya dibawah ini</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mysql-connector-python
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Collecting mysql-connector-python
Downloading mysql_connector_python-8.0.33-cp310-cp310-manylinux1_x86_64.whl (27.4 MB)
?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">0.0/27.4 MB</span> <span class=" -Color -Color-Red">?</span> eta <span class=" -Color -Color-Cyan">-:--:--</span>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">0.3/27.4 MB</span> <span class=" -Color -Color-Red">10.5 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:03</span>
━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">3.0/27.4 MB</span> <span class=" -Color -Color-Red">43.5 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">10.3/27.4 MB</span> <span class=" -Color -Color-Red">96.9 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">16.8/27.4 MB</span> <span class=" -Color -Color-Red">193.2 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ <span class=" -Color -Color-Green">23.1/27.4 MB</span> <span class=" -Color -Color-Red">172.7 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ <span class=" -Color -Color-Green">27.4/27.4 MB</span> <span class=" -Color -Color-Red">202.3 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ <span class=" -Color -Color-Green">27.4/27.4 MB</span> <span class=" -Color -Color-Red">202.3 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ <span class=" -Color -Color-Green">27.4/27.4 MB</span> <span class=" -Color -Color-Red">202.3 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:01</span>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ <span class=" -Color -Color-Green">27.4/27.4 MB</span> <span class=" -Color -Color-Red">61.4 MB/s</span> eta <span class=" -Color -Color-Cyan">0:00:00</span>
?25hRequirement already satisfied: protobuf<=3.20.3,>=3.11.0 in /usr/local/lib/python3.10/dist-packages (from mysql-connector-python) (3.20.3)
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Installing collected packages: mysql-connector-python
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Successfully installed mysql-connector-python-8.0.33
</pre></div>
</div>
</div>
</div>
<p>Mysql Cloud Menghubungkan Menggunakan Cara dibawah:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># import mysql.connector</span>
<span class="c1"># mydb = mysql.connector.connect(</span>
<span class="c1"># host="sql12.freemysqlhosting.net",</span>
<span class="c1"># user="sql12604576",</span>
<span class="c1"># password="jQKpjE31LM",</span>
<span class="c1"># database = "sql12604576"</span>
<span class="c1"># )</span>
<span class="c1"># cursor = mydb.cursor()</span>
<span class="c1"># cursor.execute("""</span>
<span class="c1"># select * from iris_arff</span>
<span class="c1"># """)</span>
<span class="c1"># result = cursor.fetchall()</span>
<span class="c1"># column =['sepallength','sepalwidth','petallength','petalwidth','class']</span>
<span class="c1"># data_sql = pd.DataFrame(result,columns =column )</span>
<span class="c1"># print(data_sql.head())</span>
<span class="c1"># data_Mysql= data_sql.drop(['sepallength','sepalwidth','petalwidth','class'],axis=1)</span>
<span class="c1"># data_Mysql['petallength'] = pd.to_numeric(data_Mysql['petallength'])</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="hasil-dari-mysql-local-ke-power-bi">
<h3>Hasil dari <strong><code class="docutils literal notranslate"><span class="pre">MySQL</span> <span class="pre">Local</span></code></strong> ke Power Bi<a class="headerlink" href="#hasil-dari-mysql-local-ke-power-bi" title="Permalink to this heading">#</a></h3>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df = pd.read_csv('/content/drive/MyDrive/prosaindata/tugas/Data/data_mysql.csv')</span>
<span class="n">data_Mysql</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'https://raw.githubusercontent.com/LALA09-erha/File-semester5/master/PSD/data_mysql.csv'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># df.head()</span>
</pre></div>
</div>
</div>
</div>
<p>Mengambil data <code class="docutils literal notranslate"><span class="pre">petal</span> <span class="pre">length</span></code></p>
<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># data_Mysql= df.drop(['sepallength','sepalwidth','petalwidth','class'],axis=1)</span>
<span class="n">data_Mysql</span><span class="o">=</span> <span class="n">data_Mysql</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'sepallength'</span><span class="p">,</span><span class="s1">'sepalwidth'</span><span class="p">,</span><span class="s1">'petalwidth'</span><span class="p">,</span><span class="s1">'class'</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">data_Mysql</span><span class="p">[</span><span class="s1">'petallength'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">data_Mysql</span><span class="p">[</span><span class="s1">'petallength'</span><span class="p">])</span>
<span class="n">data_Mysql</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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