sinhrks/plot.html

## plot.html
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<html>
<head>

<meta charset="utf-8" />
<title>Untitled1</title>

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<p>This is a example to use <code>pandas</code> plotting methods to outputs the same results as below cheatsheet.</p>
<ul>
<li><a href="http://www.datasciencecentral.com/group/tutorials/forum/topics/cheat-sheet-data-visualisation-in-python?xg_source=activity">http://www.datasciencecentral.com/group/tutorials/forum/topics/cheat-sheet-data-visualisation-in-python?xg_source=activity</a></li>
</ul>
<p>Thanks for sharing the great sheet:)</p>
<p>NOTE: Skipped violin and heatmap which is not supported by <code>pandas</code></p>

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<div class=" highlight hl-ipython2"><pre><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">mpl_style</span> <span class="o">=</span> <span class="s">&#39;default&#39;</span>
</pre></div>

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<div class=" highlight hl-ipython2"><pre><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s">&#39;EMPID&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s">&#39;E{0:03}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">)],</span>
                   <span class="s">&#39;Gender&#39;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="s">&#39;MFFMFMMFMM&#39;</span><span class="p">),</span>
                   <span class="s">&#39;Age&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">34</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">37</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">44</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">36</span><span class="p">],</span>
                   <span class="s">&#39;Sales&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">123</span><span class="p">,</span> <span class="mi">114</span><span class="p">,</span> <span class="mi">135</span><span class="p">,</span> <span class="mi">139</span><span class="p">,</span> <span class="mi">117</span><span class="p">,</span> <span class="mi">121</span><span class="p">,</span> <span class="mi">133</span><span class="p">,</span> <span class="mi">140</span><span class="p">,</span> <span class="mi">133</span><span class="p">,</span> <span class="mi">133</span><span class="p">],</span>
                   <span class="s">&#39;BMI&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s">&#39;Normal&#39;</span><span class="p">,</span> <span class="s">&#39;Overweight&#39;</span><span class="p">,</span> <span class="s">&#39;Obesity&#39;</span><span class="p">,</span> <span class="s">&#39;Underweight&#39;</span><span class="p">,</span> <span class="s">&#39;Underweight&#39;</span><span class="p">,</span>
                           <span class="s">&#39;Normal&#39;</span><span class="p">,</span> <span class="s">&#39;Obesity&#39;</span><span class="p">,</span> <span class="s">&#39;Normal&#39;</span><span class="p">,</span> <span class="s">&#39;Normal&#39;</span><span class="p">,</span> <span class="s">&#39;Underweight&#39;</span><span class="p">],</span>
                   <span class="s">&#39;Income&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">350</span><span class="p">,</span> <span class="mi">450</span><span class="p">,</span> <span class="mi">169</span><span class="p">,</span> <span class="mi">189</span><span class="p">,</span> <span class="mi">183</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">166</span><span class="p">,</span> <span class="mi">120</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">40</span><span class="p">]},</span>
                  <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;EMPID&#39;</span><span class="p">,</span> <span class="s">&#39;Gender&#39;</span><span class="p">,</span> <span class="s">&#39;Age&#39;</span><span class="p">,</span> <span class="s">&#39;Sales&#39;</span><span class="p">,</span> <span class="s">&#39;BMI&#39;</span><span class="p">,</span> <span class="s">&#39;Income&#39;</span><span class="p">])</span>
<span class="n">df</span>
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<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>EMPID</th>
      <th>Gender</th>
      <th>Age</th>
      <th>Sales</th>
      <th>BMI</th>
      <th>Income</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>E001</td>
      <td>M</td>
      <td>34</td>
      <td>123</td>
      <td>Normal</td>
      <td>350</td>
    </tr>
    <tr>
      <th>1</th>
      <td>E002</td>
      <td>F</td>
      <td>40</td>
      <td>114</td>
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      <td>450</td>
    </tr>
    <tr>
      <th>2</th>
      <td>E003</td>
      <td>F</td>
      <td>37</td>
      <td>135</td>
      <td>Obesity</td>
      <td>169</td>
    </tr>
    <tr>
      <th>3</th>
      <td>E004</td>
      <td>M</td>
      <td>30</td>
      <td>139</td>
      <td>Underweight</td>
      <td>189</td>
    </tr>
    <tr>
      <th>4</th>
      <td>E005</td>
      <td>F</td>
      <td>44</td>
      <td>117</td>
      <td>Underweight</td>
      <td>183</td>
    </tr>
    <tr>
      <th>5</th>
      <td>E006</td>
      <td>M</td>
      <td>36</td>
      <td>121</td>
      <td>Normal</td>
      <td>80</td>
    </tr>
    <tr>
      <th>6</th>
      <td>E007</td>
      <td>M</td>
      <td>32</td>
      <td>133</td>
      <td>Obesity</td>
      <td>166</td>
    </tr>
    <tr>
      <th>7</th>
      <td>E008</td>
      <td>F</td>
      <td>26</td>
      <td>140</td>
      <td>Normal</td>
      <td>120</td>
    </tr>
    <tr>
      <th>8</th>
      <td>E009</td>
      <td>M</td>
      <td>32</td>
      <td>133</td>
      <td>Normal</td>
      <td>75</td>
    </tr>
    <tr>
      <th>9</th>
      <td>E010</td>
      <td>M</td>
      <td>36</td>
      <td>133</td>
      <td>Underweight</td>
      <td>40</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython2"><pre><span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s">&#39;Age&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">&#39;Age distribution&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s">&#39;Age&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;#Employee&#39;</span><span class="p">)</span>
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<pre>&lt;matplotlib.text.Text at 0x11410b4d0&gt;</pre>
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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">df</span><span class="p">[</span><span class="s">&#39;Age&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;box&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[4]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes.AxesSubplot at 0x114108210&gt;</pre>
</div>

</div>

<div class="output_area"><div class="prompt"></div>


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"
>
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</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s">&#39;Gender&#39;</span><span class="p">)[</span><span class="s">&#39;Sales&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">&#39;Gender wise Sum of Sales&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;Sum of Sales&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[5]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.text.Text at 0x114254350&gt;</pre>
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</div>

<div class="output_area"><div class="prompt"></div>


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"
>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">ax</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s">&#39;BMI&#39;</span><span class="p">)[</span><span class="s">&#39;Sales&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s">&#39;BMI wise Sum of Sales&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;Sum of Sales&#39;</span><span class="p">)</span>
</pre></div>

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<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[6]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.text.Text at 0x11427cb90&gt;</pre>
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<div class="output_area"><div class="prompt"></div>


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>
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</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">d</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s">&#39;BMI&#39;</span><span class="p">,</span> <span class="s">&#39;Gender&#39;</span><span class="p">])[</span><span class="s">&#39;Sales&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">unstack</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;bar&#39;</span><span class="p">,</span> <span class="n">stacked</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">&#39;BMI wise Sum of Sales&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;Sum of Sales&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

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<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.text.Text at 0x114250e90&gt;</pre>
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<div class="output_area"><div class="prompt"></div>


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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;scatter&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s">&#39;Age&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s">&#39;Sales&#39;</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[8]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes.AxesSubplot at 0x114217f90&gt;</pre>
</div>

</div>

<div class="output_area"><div class="prompt"></div>


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>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;scatter&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s">&#39;Age&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s">&#39;Sales&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s">&#39;Income&#39;</span><span class="p">])</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[9]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes.AxesSubplot at 0x114459490&gt;</pre>
</div>

</div>

<div class="output_area"><div class="prompt"></div>


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>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span class="n">d</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s">&#39;Gender&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">stack</span><span class="p">()</span><span class="o">.</span><span class="n">unstack</span><span class="p">()[</span><span class="s">&#39;Sales&#39;</span><span class="p">]</span>
<span class="n">d</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;pie&#39;</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s">&#39;</span><span class="si">%1.1f%%</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s">&#39;Pastafarianism expenses&#39;</span><span class="p">)</span>
</pre></div>

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</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area"><div class="prompt output_prompt">Out[12]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes.AxesSubplot at 0x114515450&gt;</pre>
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<div class="output_area"><div class="prompt"></div>


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