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<p><img alt="" src="docs/Images/Pandas-Bokeh-Logo.png" /></p>
<p><strong>Pandas-Bokeh</strong> provides a <a href="https://bokeh.pydata.org/en/latest/">Bokeh</a> plotting backend for <a href="https://pandas.pydata.org/">Pandas</a>, <a href="http://geopandas.org/">GeoPandas</a> and <a href="https://spark.apache.org/docs/latest/api/python/index.html">Pyspark</a> <strong>DataFrames</strong>, similar to the already existing <a href="https://pandas.pydata.org/pandas-docs/stable/visualization.html">Visualization feature of Pandas</a>. Importing the library adds a complementary plotting method <strong><em>plot_bokeh()</em></strong> on <strong>DataFrames</strong> and <strong>Series</strong>.</p>
<p>With <strong>Pandas-Bokeh</strong>, creating stunning, interactive, HTML-based visualization is as easy as calling:
<code>python
df.plot_bokeh()</code></p>
<hr />
<p><strong>Pandas-Bokeh</strong> also provides native support as a Pandas Plotting backend for <a href="https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html">Pandas >= 0.25</a>. When <strong>Pandas-Bokeh</strong> is installed, switchting the default Pandas plotting backend to Bokeh can be done via:</p>
<pre><code>pd.set_option('plotting.backend', 'pandas_bokeh')
</code></pre>
<p>More details about the new Pandas backend can be found <a href="#pandas_backend">below.</a></p>
<hr />
<p>For more information have a look at the <a href="#Examples">Examples</a> below or at notebooks on the <a href="https://github.com/PatrikHlobil/Pandas-Bokeh/tree/master/docs">Github Repository</a> of this project. </p>
<div align="center">
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-1.3.4.min.js"></script>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.4.min.js"></script>
<script type="text/javascript">
Bokeh.set_log_level("info");
</script>
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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values_original":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"AACA9VmNa0IAAADB/o1rQgAAgIyjjmtCAAAAWEiPa0IAAIAj7Y9rQgAAAO+RkGtCAACAujaRa0IAAACG25FrQgAAgFGAkmtCAAAAHSWTa0IAAIDoyZNrQgAAALRulGtCAACAfxOVa0IAAABLuJVrQgAAgBZdlmtCAAAA4gGXa0IAAICtppdrQgAAAHlLmGtCAACARPCYa0IAAAAQlZlrQgAAgNs5mmtCAAAAp96aa0IAAIByg5trQgAAAD4onGtCAACACc2ca0IAAADVcZ1rQgAAgKAWnmtCAAAAbLuea0IAAIA3YJ9rQgAAAAMFoGtCAACAzqmga0IAAACaTqFrQgAAgGXzoWtCAAAAMZiia0IAAID8PKNrQgAAAMjho2tCAACAk4aka0IAAABfK6VrQgAAgCrQpWtCAAAA9nSma0IAAIDBGadrQgAAAI2+p2tCAACAWGOoa0IAAAAkCKlrQgAAgO+sqWtCAAAAu1Gqa0IAAICG9qprQgAAAFKbq2tCAACAHUCsa0IAAADp5KxrQgAAgLSJrWtCAAAAgC6ua0IAAIBL065rQgAAABd4r2tCAACA4hywa0IAAACuwbBrQgAAgHlmsWtCAAAARQuya0IAAIAQsLJrQgAAANxUs2tCAACAp/mza0IAAABznrRrQgAAgD5DtWtCAAAACui1a0IAAIDVjLZrQgAAAKExt2tCAACAbNa3a0IAAAA4e7hrQgAAgAMguWtCAAAAz8S5a0IAAICaabprQgAAAGYOu2tCAACAMbO7a0IAAAD9V7xrQgAAgMj8vGtCAAAAlKG9a0IAAIBfRr5rQgAAACvrvmtCAACA9o+/a0IAAADCNMBrQgAAgI3ZwGtCAAAAWX7Ba0IAAIAkI8JrQgAAAPDHwmtCAACAu2zDa0IAAACHEcRrQgAAgFK2xGtCAAAAHlvFa0IAAIDp/8VrQgAAALWkxmtCAACAgEnHa0IAAABM7sdrQgAAgBeTyGtCAAAA4zfJa0IAAICu3MlrQgAAAHqBymtCAACARSbLa0IAAAARy8trQgAAgNxvzGtCAAAAqBTNa0IAAIBzuc1rQgAAAD9ezmtCAACACgPPa0IAAADWp89rQgAAgKFM0GtCAAAAbfHQa0IAAIA4ltFrQgAAAAQ70mtCAACAz9/Sa0IAAACbhNNrQgAAgGYp1GtCAAAAMs7Ua0IAAID9ctVrQgAAAMkX1mtCAACAlLzWa0IAAABgYddrQgAAgCsG2GtCAAAA96rYa0IAAIDCT9lrQgAAAI702WtCAACAWZnaa0IAAAAlPttrQgAAgPDi22tCAAAAvIfca0IAAICHLN1rQgAAAFPR3WtCAACAHnbea0IAAADqGt9rQgAAgLW/32tCAAAAgWTga0IAAIBMCeFrQgAAABiu4WtCAACA41Lia0IAAACv9+JrQgAAgHqc42tCAAAARkHka0IAAIAR5uRrQgAAAN2K5WtCAACAqC/ma0IAAAB01OZrQgAAgD9552tCAAAACx7oa0IAAIDWwuhrQgAAAKJn6WtCAACAbQzqa0IAAAA5seprQgAAgARW62tCAAAA0Prra0IAAICbn+xrQgAAAGdE7WtCAACAMunta0IAAAD+je5rQgAAgMky72tCAAAAldfva0IAAIBgfPBrQgAAACwh8WtCAACA98Xxa0IAAADDavJrQgAAgI4P82tCAAAAWrTza0IAAIAlWfRrQgAAAPH99GtCAACAvKL1a0IAAACIR/ZrQgAAgFPs9mtCAAAAH5H3a0IAAIDqNfhrQgAAALba+GtCAACAgX/5a0IAAABNJPprQgAAgBjJ+mtCAAAA5G37a0IAAICvEvxrQgAAAHu3/GtCAACARlz9a0IAAAASAf5rQgAAgN2l/mtCAAAAqUr/a0IAAIB07/9rQgAAAECUAGxCAACACzkBbEIAAADX3QFsQgAAgKKCAmxCAAAAbicDbEIAAIA5zANsQgAAAAVxBGxCAACA0BUFbEIAAACcugVsQgAAgGdfBmxCAAAAMwQHbEIAAID+qAdsQgAAAMpNCGxCAACAlfIIbEIAAABhlwlsQgAAgCw8CmxCAAAA+OAKbEIAAIDDhQtsQgAAAI8qDGxCAACAWs8MbEIAAAAmdA1sQgAAgPEYDmxCAAAAvb0ObEIAAICIYg9sQgAAAFQHEGxCAACAH6wQbEIAAADrUBFsQgAAgLb1EWxCAAAAgpoSbEIAAIBNPxNsQgAAABnkE2xCAACA5IgUbEIAAACwLRVsQgAAgHvSFWxCAAAAR3cWbEIAAIASHBdsQgAAAN7AF2xCAACAqWUYbEIAAAB1ChlsQgAAgECvGWxCAAAADFQabEIAAIDX+BpsQgAAAKOdG2xCAACAbkIcbEIAAAA65xxsQgAAgAWMHWxCAAAA0TAebEIAAICc1R5sQgAAAGh6H2xCAACAMx8gbEIAAAD/wyBsQgAAgMpoIWxCAAAAlg0ibEIAAIBhsiJsQgAAAC1XI2xCAACA+PsjbEIAAADEoCRsQgAAgI9FJWxCAAAAW+olbEIAAIAmjyZsQgAAAPIzJ2xCAACAvdgnbEIAAACJfShsQgAAgFQiKWxCAAAAIMcpbEIAAIDraypsQgAAALcQK2xCAACAgrUrbEIAAABOWixsQgAAgBn/LGxCAAAA5aMtbEIAAICwSC5sQgAAAHztLmxCAACAR5IvbEIAAAATNzBsQgAAgN7bMGxCAAAAqoAxbEIAAIB1JTJsQgAAAEHKMmxCAACADG8zbEIAAADYEzRsQgAAgKO4NGxCAAAAb101bEIAAIA6AjZsQgAAAAanNmxCAACA0Us3bEIAAACd8DdsQgAAgGiVOGxCAAAANDo5bEIAAID/3jlsQgAAAMuDOmxCAACAlig7bEIAAABizTtsQgAAgC1yPGxCAAAA+RY9bEIAAIDEuz1sQgAAAJBgPmxCAACAWwU/bEIAAAAnqj9sQgAAgPJOQGxCAAAAvvNAbEIAAICJmEFsQgAAAFU9QmxCAACAIOJCbEIAAADshkNsQgAAgLcrRGxCAAAAg9BEbEIAAIBOdUVsQgAAABoaRmxCAACA5b5GbEIAAACxY0dsQgAAgHwISGxCAAAASK1IbEIAAIATUklsQgAAAN/2SWxCAACAqptKbEIAAAB2QEtsQgAAgEHlS2xCAAAADYpMbEIAAIDYLk1sQgAAAKTTTWxCAACAb3hObEIAAAA7HU9sQgAAgAbCT2xCAAAA0mZQbEIAAICdC1FsQgAAAGmwUWxCAACANFVSbEIAAAAA+lJsQgAAgMueU2xCAAAAl0NUbEIAAIBi6FRsQgAAAC6NVWxCAACA+TFWbEIAAADF1lZsQgAAgJB7V2xCAAAAXCBYbEIAAIAnxVhsQgAAAPNpWWxCAACAvg5abEIAAACKs1psQgAAgFVYW2xCAAAAIf1bbEIAAIDsoVxsQgAAALhGXWxCAACAg+tdbEIAAABPkF5sQgAAgBo1X2xCAAAA5tlfbEIAAICxfmBsQgAAAH0jYWxCAACASMhhbEIAAAAUbWJsQgAAgN8RY2xCAAAAq7ZjbEIAAIB2W2RsQgAAAEIAZWxCAACADaVlbEIAAADZSWZsQgAAgKTuZmxCAAAAcJNnbEIAAIA7OGhsQgAAAAfdaGxCAACA0oFpbEIAAACeJmpsQgAAgGnLamxCAAAANXBrbEIAAIAAFWxsQgAAAMy5bGxCAACAl15tbEIAAABjA25sQgAAgC6obmxCAAAA+kxvbEIAAIDF8W9sQgAAAJGWcGxCAACAXDtxbEIAAAAo4HFsQgAAgPOEcmxCAAAAvylzbEIAAICKznNsQgAAAFZzdGxCAACAIRh1bEIAAADtvHVsQgAAgLhhdmxCAAAAhAZ3bEIAAIBPq3dsQgAAABtQeGxCAACA5vR4bEIAAACymXlsQgAAgH0+emxCAAAASeN6bEIAAIAUiHtsQgAAAOAsfGxCAACAq9F8bEIAAAB3dn1sQgAAgEIbfmxCAAAADsB+bEIAAIDZZH9sQgAAAKUJgGxCAACAcK6AbEIAAAA8U4FsQgAAgAf4gWxCAAAA05yCbEIAAICeQYNsQgAAAGrmg2xCAACANYuEbEIAAAABMIVsQgAAgMzUhWxCAAAAmHmGbEIAAIBjHodsQgAAAC/Dh2xCAACA+meIbEIAAADGDIlsQgAAgJGxiWxCAAAAXVaKbEIAAIAo+4psQgAAAPSfi2xCAACAv0SMbEIAAACL6YxsQgAAgFaOjWxCAAAAIjOObEIAAIDt145sQgAAALl8j2xCAACAhCGQbEIAAABQxpBsQgAAgBtrkWxCAAAA5w+SbEIAAICytJJsQgAAAH5Zk2xCAACASf6TbEIAAAAVo5RsQgAAgOBHlWxCAAAArOyVbEIAAIB3kZZsQgAAAEM2l2xCAACADtuXbEIAAADaf5hsQgAAgKUkmWxCAAAAccmZbEIAAIA8bppsQgAAAAgTm2xCAACA07ebbEIAAACfXJxsQgAAgGoBnWxCAAAANqadbEIAAIABS55sQgAAAM3vnmxCAACAmJSfbEIAAABkOaBsQgAAgC/eoGxCAAAA+4KhbEIAAIDGJ6JsQgAAAJLMomxCAACAXXGjbEIAAAApFqRsQgAAgPS6pGxCAAAAwF+lbEIAAICLBKZsQgAAAFeppmxCAACAIk6nbEIAAADu8qdsQgAAgLmXqGxCAAAAhTypbEIAAIBQ4alsQgAAAByGqmxCAACA5yqrbEIAAACzz6tsQgAAgH50rGxCAAAAShmtbEIAAIAVvq1sQgAAAOFirmxCAACArAevbEIAAAB4rK9sQgAAgENRsGxCAAAAD/awbEIAAIDamrFsQgAAAKY/smxCAACAceSybEIAAAA9ibNsQgAAgAgutGxCAAAA1NK0bEIAAICfd7VsQgAAAGsctmxCAACANsG2bEIAAAACZrdsQgAAgM0KuGxCAAAAma+4bEIAAIBkVLlsQgAAADD5uWxCAACA+526bEIAAADHQrtsQgAAgJLnu2xCAAAAXoy8bEIAAIApMb1sQgAAAPXVvWxCAACAwHq+bEIAAACMH79sQgAAgFfEv2xCAAAAI2nAbEIAAIDuDcFsQgAAALqywWxCAACAhVfCbEIAAABR/MJsQgAAgByhw2xCAAAA6EXEbEIAAICz6sRsQgAAAH+PxWxCAACASjTGbEIAAAAW2cZsQgAAgOF9x2xCAAAArSLIbEIAAIB4x8hsQgAAAERsyWxCAACADxHKbEIAAADbtcpsQgAAgKZay2xCAAAAcv/LbEIAAIA9pMxsQgAAAAlJzWxCAACA1O3NbEIAAACgks5sQgAAgGs3z2xCAAAAN9zPbEIAAIACgdBsQgAAAM4l0WxCAACAmcrRbEIAAABlb9JsQgAAgDAU02xCAAAA/LjTbEIAAIDHXdRsQgAAAJMC1WxCAACAXqfVbEIAAAAqTNZsQgAAgPXw1mxCAAAAwZXXbEIAAICMOthsQgAAAFjf2GxCAACAI4TZbEIAAADvKNpsQgAAgLrN2mxCAAAAhnLbbEIAAIBRF9xsQgAAAB283GxCAACA6GDdbEIAAAC0Bd5sQgAAgH+q3mxCAAAAS0/fbEIAAIAW9N9sQgAAAOKY4GxCAACArT3hbEIAAAB54uFsQgAAgESH4mxCAAAAECzjbEIAAIDb0ONsQgAAAKd15GxCAACAchrlbEIAAAA+v+VsQgAAgAlk5mxCAAAA1QjnbEIAAICgredsQgAAAGxS6GxCAACAN/fobEIAAAADnOlsQgAAgM5A6mxCAAAAmuXqbEIAAIBliutsQgAAADEv7GxCAACA/NPsbEIAAADIeO1sQgAAgJMd7mxCAAAAX8LubEIAAIAqZ+9sQgAAAPYL8GxCAACAwbDwbEIAAACNVfFsQgAAgFj68WxCAAAAJJ/ybEIAAIDvQ/NsQgAAALvo82xCAACAho30bEIAAABSMvVsQgAAgB3X9WxCAAAA6Xv2bEIAAIC0IPdsQgAAAIDF92xCAACAS2r4bEIAAAAXD/lsQgAAgOKz+WxCAAAArlj6bEIAAIB5/fpsQgAAAEWi+2xCAACAEEf8bEIAAADc6/xsQgAAgKeQ/WxCAAAAczX+bEIAAIA+2v5sQgAAAAp//2xCAACA1SMAbUIAAAChyABtQgAAgGxtAW1CAAAAOBICbUIAAIADtwJtQgAAAM9bA21CAACAmgAEbUIAAABmpQRtQgAAgDFKBW1CAAAA/e4FbUIAAIDIkwZtQgAAAJQ4B21CAACAX90HbUIAAAArgghtQgAAgPYmCW1CAAAAwssJbUIAAICNcAptQgAAAFkVC21CAACAJLoLbUIAAADwXgxtQgAAgLsDDW1CAAAAh6gNbUIAAIBSTQ5tQgAAAB7yDm1CAACA6ZYPbUIAAAC1OxBtQgAAgIDgEG1CAAAATIURbUIAAIAXKhJtQgAAAOPOEm1CAACArnMTbUIAAAB6GBRtQgAAgEW9FG1CAAAAEWIVbUIAAIDcBhZtQgAAAKirFm1CAACAc1AXbUIAAAA/9RdtQgAAgAqaGG1CAAAA1j4ZbUIAAICh4xltQgAAAG2IGm1CAACAOC0bbUIAAAAE0httQgAAgM92HG1CAAAAmxsdbUIAAIBmwB1tQgAAADJlHm1CAACA/QkfbUIAAADJrh9tQgAAgJRTIG1CAAAAYPggbUIAAIArnSFtQgAAAPdBIm1CAACAwuYibUIAAACOiyNtQgAAgFkwJG1CAAAAJdUkbUIAAIDweSVtQgAAALweJm1CAACAh8MmbUIAAABTaCdtQgAAgB4NKG1CAAAA6rEobUIAAIC1ViltQgAAAIH7KW1CAACATKAqbUIAAAAYRSttQgAAgOPpK21CAAAAr44sbUIAAIB6My1tQgAAAEbYLW1CAACAEX0ubUIAAADdIS9tQgAAgKjGL21CAAAAdGswbUIAAIA/EDFtQgAAAAu1MW1CAACA1lkybUIAAACi/jJtQgAAgG2jM21CAAAAOUg0bUIAAIAE7TRtQgAAANCRNW1CAACAmzY2bUIAAABn2zZtQgAAgDKAN21CAAAA/iQ4bUIAAIDJyThtQgAAAJVuOW1CAACAYBM6bUIAAAAsuDptQgAAgPdcO21CAAAAwwE8bUIAAICOpjxtQgAAAFpLPW1CAACAJfA9bUIAAADxlD5tQgAAgLw5P21CAAAAiN4/bUIAAIBTg0BtQgAAAB8oQW1CAACA6sxBbUIAAAC2cUJtQgAAgIEWQ21CAAAATbtDbUIAAIAYYERtQgAAAOQERW1CAACAr6lFbUIAAAB7TkZtQgAAgEbzRm1CAAAAEphHbUIAAIDdPEhtQgAAAKnhSG1CAACAdIZJbUIAAABAK0ptQgAAgAvQSm1CAAAA13RLbUIAAICiGUxtQgAAAG6+TG1CAACAOWNNbUIAAAAFCE5tQgAAgNCsTm1CAAAAnFFPbUIAAIBn9k9tQgAAADObUG1CAACA/j9RbUIAAADK5FFtQgAAgJWJUm1CAAAAYS5TbUIAAIAs01NtQgAAAPh3VG1CAACAwxxVbUIAAACPwVVtQgAAgFpmVm1CAAAAJgtXbUIAAIDxr1dtQgAAAL1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<p><br></p>
<h2>Installation</h2>
<p>You can install <strong>Pandas-Bokeh</strong> from <a href="https://pypi.org/project/pandas-bokeh/">PyPI</a> via <em>pip</em></p>
<pre><code>pip install pandas-bokeh
</code></pre>
<p>or <em>conda</em>:</p>
<pre><code>conda install -c patrikhlobil pandas-bokeh
</code></pre>
<p>With the current release 0.4, <strong>Pandas-Bokeh</strong> officially supports <strong>Python 3.5</strong> and newer. It will probably still work for older Python versions, but is not tested against these.</p>
<p><br></p>
<p>The current release is <strong>0.4</strong>. For more details, see <a href="#releasenotes">Release Notes</a>.</p>
<p><br></p>
<h2>How To Use</h2>
<h3>Classical Use</h3>
<p><p id="Basics"> </p></p>
<p>The <strong>Pandas-Bokeh</strong> library should be imported after <strong>Pandas</strong>, <strong>GeoPandas</strong> and/or <strong>Pyspark</strong>. After the import, one should define the plotting output, which can be:</p>
<ul>
<li>
<p><strong>pandas_bokeh.output_notebook()</strong>: Embeds the Plots in the cell outputs of the notebook. Ideal when working in Jupyter Notebooks.</p>
</li>
<li>
<p><strong>pandas_bokeh.output_file(filename)</strong>: Exports the plot to the provided filename as an HTML.</p>
</li>
</ul>
<p>For more details about the plotting outputs, see the reference <a href="#Layouts">here</a> or the <a href="https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#getting-started">Bokeh documentation</a>.</p>
<h4>Notebook output (see also <a href="https://bokeh.pydata.org/en/latest/docs/reference/io.html#bokeh.io.output_notebook">bokeh.io.output_notebook</a>)</h4>
<p><code>python
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_notebook()</code></p>
<h4>File output to "Interactive Plot.html" (see also <a href="https://bokeh.pydata.org/en/latest/docs/reference/io.html#bokeh.io.output_file">bokeh.io.output_file</a>)</h4>
<p><code>python
import pandas as pd
import pandas_bokeh
pandas_bokeh.output_file("Interactive Plot.html")</code></p>
<h3><strong>Pandas-Bokeh</strong> as native Pandas plotting backend</h3>
<p><p id="pandas_backend"> </p></p>
<p>For pandas >= 0.25, a plotting backend switch is natively supported. It can be achievied by calling:</p>
<pre><code>import pandas as pd
pd.set_option('plotting.backend', 'pandas_bokeh')
</code></pre>
<p>Now, the plotting API is accessible for a Pandas DataFrame via:</p>
<pre><code>df.plot(...)
</code></pre>
<p>All additional functionalities of <strong>Pandas-Bokeh</strong> are then accessible at <em>pd.plotting</em>. So, setting the output to notebook is:</p>
<pre><code>pd.plotting.output_notebook()
</code></pre>
<p>or calling the <a href="#dashboard_layouts">grid layout functionality</a>:</p>
<pre><code>pd.plotting.plot_grid(...)
</code></pre>
<p><strong>Note:</strong> Backwards compatibility is kept since there will still be the <em>df.plot_bokeh(...)</em> methods for a DataFrame.</p>
<p id="output_file"> </p>
<p><br></p>
<hr />
<h3>Plot types</h3>
<p>Supported plottypes are at the moment:
* Pandas & Pyspark DataFrames
* <a href="#lineplot">lineplot</a>
* <a href="#pointplot">pointplot</a>
* <a href="#stepplot">stepplot</a>
* <a href="#scatterplot">scatterplot</a>
* <a href="#barplot">barplot</a>
* <a href="#histogram">histogram</a>
* <a href="#areaplot">areaplot</a>
* <a href="#pieplot">pieplot</a>
* <a href="#mapplot">mapplot</a></p>
<ul>
<li><a href="#geoplots">Geoplots (Point, Line, Polygon) with <strong>GeoPandas</strong></a></li>
</ul>
<p>Also, check out the complementary chapter <a href="#Layouts">Outputs, Formatting & Layouts</a> about:</p>
<ul>
<li><a href="#output_options">Output options (how to get HTML representation of Bokeh plots)</a></li>
<li><a href="#number_formats">Number formats in Pandas-Bokeh (modify Hovertool number format, suppress scientific notation on axes)</a></li>
<li><a href="#dashboard_layouts">Dashboard layouts (How to layout multiple plots in rows, columns and grids)</a></li>
</ul>
<hr />
<p><br>
<br></p>
<p id="Examples"></p>
<h2>Lineplot</h2>
<h3>Basic Lineplot</h3>
<p>This simple <strong>lineplot</strong> in <strong>Pandas-Bokeh</strong> already contains various interactive elements:</p>
<ul>
<li>a pannable and zoomable (zoom in plotarea and zoom on axis) plot</li>
<li>by clicking on the legend elements, one can hide and show the individual lines</li>
<li>a Hovertool for the plotted lines</li>
</ul>
<p>Consider the following simple example:</p>
<p>```python
import numpy as np</p>
<p>np.random.seed(42)
df = pd.DataFrame({"Google": np.random.randn(1000)+0.2,
"Apple": np.random.randn(1000)+0.17},
index=pd.date_range('1/1/2000', periods=1000))
df = df.cumsum()
df = df + 50
df.plot_bokeh(kind="line") #equivalent to df.plot_bokeh.line()
```</p>
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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"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</div>
<p>Note, that similar to the regular <strong>pandas.DataFrame.plot</strong> method, there are also additional accessors to directly access the different plotting types like:</p>
<ul>
<li><code>df.plot_bokeh(kind="line", ...)</code> → <code>df.plot_bokeh.line(...)</code></li>
<li><code>df.plot_bokeh(kind="bar", ...)</code> → <code>df.plot_bokeh.bar(...)</code></li>
<li><code>df.plot_bokeh(kind="hist", ...)</code> → <code>df.plot_bokeh.hist(...)</code></li>
<li>...</li>
</ul>
<h4>Advanced Lineplot</h4>
<p>There are various optional parameters to tune the plots, for example:</p>
<ul>
<li><strong>kind</strong>: Which kind of plot should be produced. Currently supported are: <em>"line", "point", "scatter", "bar"</em> and <em>"histogram"</em>. In the near future many more will be implemented as horizontal barplot, boxplots, pie-charts, etc.</li>
<li><strong>x</strong>: Name of the column to use for the horizontal x-axis. If the <strong>x</strong> parameter is not specified, the index is used for the x-values of the plot. Alternative, also an array of values can be passed that has the same number of elements as the DataFrame.</li>
<li><strong>y</strong>: Name of column <em>or</em> list of names of columns to use for the vertical y-axis. </li>
<li><strong>figsize</strong>: Choose width & height of the plot</li>
<li><strong>title</strong>: Sets title of the plot</li>
<li><strong>xlim</strong>/<strong>ylim</strong>: Set visibler range of plot for x- and y-axis (also works for <em>datetime x-axis</em>)</li>
<li><strong>xlabel</strong>/<strong>ylabel</strong>: Set x- and y-labels</li>
<li><strong>logx</strong>/<strong>logy</strong>: Set log-scale on x-/y-axis</li>
<li><strong>xticks</strong>/<strong>yticks</strong>: Explicitly set the ticks on the axes</li>
<li><strong>color</strong>: Defines a single color for a plot.</li>
<li><strong>colormap</strong>: Can be used to specify multiple colors to plot. Can be either a list of colors or the name of a <a href="https://bokeh.pydata.org/en/latest/docs/reference/palettes.html">Bokeh color palette</a></li>
<li><strong>hovertool</strong>: If True a Hovertool is active, else if False no Hovertool is drawn.</li>
<li><strong>hovertool_string</strong>: If specified, this string will be used for the hovertool (@{column} will be replaced by the value of the column for the element the mouse hovers over, see also <a href="https://bokeh.pydata.org/en/latest/docs/user_guide/tools.html#custom-tooltip">Bokeh documentation</a> and <a href="#Dropdown">here</a>)</li>
<li><strong>toolbar_location</strong>: Specify the position of the toolbar location (None, "above", "below", "left" or "right"). Default: <em>"right"</em></li>
<li><strong>zooming</strong>: Enables/Disables zooming. Default: <em>True</em></li>
<li><strong>panning</strong>: Enables/Disables panning. Default: <em>True</em></li>
<li>
<p><strong>rangetool</strong> Enables a range tool scroller. Default <em>False</em></p>
</li>
<li>
<p><strong>kwargs</strong>**: Optional keyword arguments of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.line">bokeh.plotting.figure.line</a></p>
</li>
</ul>
<p id="hovertool_string_example"></p>
<p>Try them out to get a feeling for the effects. Let us consider now:</p>
<p>```python
df.plot_bokeh.line(
figsize=(800, 450),
y="Apple",
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(0, 400),
toolbar_location=None,
colormap=["red", "blue"],
hovertool_string=r"""<img
src='https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/170px-Apple_logo_black.svg.png'
height="42" alt="@imgs" width="42"
style="float: left; margin: 0px 15px 15px 0px;"
border="2"></img> Apple </p>
<pre><code> <h4> Stock Price: </h4> @{Apple}""",
panning=False,
zooming=False)
</code></pre>
<p>```</p>
<div align="center">
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-1.3.4.min.js"></script>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-gl-1.3.4.min.js"></script>
<script type="text/javascript">
Bokeh.set_log_level("info");
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</div>
<h4>Lineplot with data points</h4>
<p>For <strong>lineplots</strong>, as for many other plot-kinds, there are some special keyword arguments that only work for this plotting type. For lineplots, these are:</p>
<ul>
<li><strong>plot_data_points</strong>: Plot also the data points on the lines</li>
<li><strong>plot_data_points_size</strong>: Determines the size of the data points</li>
<li>
<p><strong>marker</strong>: Defines the point type <em>(Default: "circle")</em>. Possible values are: 'circle', 'square', 'triangle', 'asterisk', 'circle_x', 'square_x', 'inverted_triangle', 'x', 'circle_cross', 'square_cross', 'diamond', 'cross'</p>
</li>
<li>
<p><strong>kwargs</strong>**: Optional keyword arguments of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.line">bokeh.plotting.figure.line</a></p>
</li>
</ul>
<p>Let us use this information to have another version of the same plot:</p>
<p><code>python
df.plot_bokeh.line(
figsize=(800, 450),
title="Apple vs Google",
xlabel="Date",
ylabel="Stock price [$]",
yticks=[0, 100, 200, 300, 400],
ylim=(100, 200),
xlim=("2001-01-01", "2001-02-01"),
colormap=["red", "blue"],
plot_data_points=True,
plot_data_points_size=10,
marker="asterisk")</code></p>
<div align="center">
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-1.3.4.min.js"></script>
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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"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pqo27GY0DWAypCMe5jQPNOqDT052NAiOJDCorSY0CYEGqai7ZjQDqmmdkPfWNAIc1V8EzEY0AGaYsYcKZjQMDF5znMpGNAIAwuNmGhY0CIS5cgqLBjQFaVW0kz0GNAJBLl7CzxY0BW5rf+9ONjQFIL7JIM5GNA2NDeG4jyY0ClIp54A/BjQOx4cnzjKGRASpFjMQo+ZEAK0KXz/lpkQB4cROqldWRA5Dknp6OgZEA9lLc436tkQF8C2DbQh2RA3DgSGQuaZECUp3/So4pkQEuqzggqf2RA3jcfK16XZEAKIsjSbsRkQJb5WbiNymRACCX2UeDZZEBW7kaByRVlQIoCIWXuImVAgkvqBaB7ZUCrh3BRKZNlQBA/I8pBYGVAznTdzc19ZUBqNMbkcI9lQGqVLjAnvmVAEZ+jhyLZZUCkRDzwJNplQKihlCBouGVAMufgcii3ZUBhBAygYaFlQIAf+Z0+lGVAiO0w5ISsZUACLJpjZedlQFBeoS13+WVAiz/qJKPYZUDSQCH4TexlQEjbFWoJGGZAv+58tvYJZkAuevV7HQtmQGQELXMGEWZArOLyM1n9ZUDIrGWOiBdmQP7dlWMaGWZAXq661vcrZkDm1JIkAhVmQA033R9zDGZA0qQ2gwEpZkDnS0PngyJmQKMjzNQ3X2ZAd73Ppt5XZkAwaKzof2RmQKbgPVXIh2ZAB1SMuNVfZkBan6rQ7SxmQCKpUEeMAWZAysjuAWQvZkCSvlvTKyNmQN9b8jt5emZAjYPOjtttZkAWA8gKNHlmQAOxl6X9r2ZAdyclM6D1ZkBleVWtCD1nQEPD1EAkaWdA7PpxA1qPZ0BUn1yiwKdnQEB9sJsZxmdAlVJN7+a5Z0BNn7fjKKVnQHKizuJ9qmdA1SugX3yqZ0BgBlsrbKFnQBoWhp0lvWdAoLCXHSjhZ0CJZhz8bOlnQGmQHY0lHmhAxK1YeQ3/Z0CmMt2nS/5nQN47VxbM7GdAa2IuD4K2Z0AXNg5hTblnQE9k3anYumdAN1rce7fwZ0CTx6+7VwpoQDJiFDwC72dAn3HamccvaEDqLvPySlxoQFYoJyJcdGhAm35pao1yaECZgVZUSlloQHI5/tnRUmhA6qkthRl7aEB4g/Ww2LxoQOADDZWr82hAayZZkXfpaEBpsD9NFMtoQLmY9hQH1WhAUaGmquChaEANHU+yqLFoQKQIFy1gsmhAXEqMC+eoaEDdPKngT3toQCfl/e0vkWhAm1Izr5SFaECNz5tdlWVoQDHnAG8cD2hAZMbnq6sTaEAA0LmY0lFoQGpvpkVsjGhAeFQpYTyDaEBeTsayZ3VoQCy6e4nYiWhAsYBNZ1ZvaEByCKFSb35oQGrLQbFjnGhAV7SPXhfJaEA4XRSMU8toQGtlG7E/ymhAMl0C5pezaECknQ+8kp5oQHBpPQ/EnGhANCJjKPataEANpg3iotBoQAzwjOFfvGhAs1M1oJTxaEAXaaU4WO5oQI2lBdIZ82hAqAtlTaHgaEBLpeyQfphoQCPhVU45umhA/vPTKz7XaEAS1QQtrtNoQAlTk5pD22hAsLVg0TbxaEBuhQwVp8RoQKMtArkpuWhA4CKc+gTYaEC8peB6UrVoQFx0UvInxGhAANjqdy+eaEDe71MajbJoQPPej8TZtmhAzud7P5uIaEBLaspuUbNoQBCsb0xAoWhA6ebBWcOMaEBKx7ANn5hoQN0ppVTRwmhAsNG5W8CnaEBYGZZHKq9oQIvAzshTwmhAm0Z1WPLdaEAfAw9lCOloQKlau1e64mhAR98VXa/NaEBt972x4dVoQNzZG18DuWhArovwTvhgaEBKyQs/YXRoQEtZpeu+lmhAnrkAa5JQaED6cxNYszVoQIBEgxj0TmhATJEXvTuWaEA/aX+4VpxoQGp0g497imhAdFu36RGKaEDAbmOKgbtoQLeBKHFGrGhAwhLiGSSYaEDpGC4bIo5oQCjJBRQRdWhAzT8pom5+aECZ/q2j3LdoQGYK3fGjx2hAaD8NTgHFaECPNk5tG8FoQHsoamiGlGhAuKawezm2aEBTvhh7LLloQPT5H6HWuGhAUBTjNHUkaUAMf/w6dTNpQJfWuDLXIGlAgV3gHaMYaUAor+7S00JpQA1VCmDkS2lAh3FXpE4jaUD37PqaKUZpQDhqHc84NmlAOQUigZt3aUC+Gu/lm59pQIgQxBO8lmlAJ4ShzSrFaUCWYMpQx8xpQBgcpuCB7WlA0tjicnUCakANpSy80OxpQH9KMmqp3WlAQspsfg8EakCJ94Gnyf5pQDdrKptP92lANDbH4S/eaUBT6z6lLvFpQENgWH2fOGpAeyZm5uYbakB8+x7yHSJqQKuNH3+/VGpACz7ctqNXakAYGR4vjmtqQGfHPrL/TmpAfrYrByViakA4hgqVmGFqQKhUg1KUhmpANcViBwSyakCg+8tfUgprQP5ZgV5PImtAUQC+7iwya0C7TgL41T1rQHyjc3j5N2tASGpc+D5Ia0Dc/IquO0RrQHf+p1oQT2tA85/NwKp+a0AirePL5WNrQB5mVVLQjWtAwNmrxGa9a0BbIN0wEL9rQGtndZiagGtAIeYz9Zdya0CeAsCTiaFrQJGWyeE+pmtA9s/zPLWLa0CviQ7D/oBrQFDa3cJVoWtAKkYaPkW4a0CnUwONELZrQBraTSrEr2tAoiHChauoa0B8LjzHl5BrQITZbpO5yWtAv6BBxNnEa0DFyoVePfFrQGMRXaFcJ2xAxnTmbr9MbEDKkVY9YERsQAxpizy8VmxATS6FqmZbbEAYEzIu7ENsQBWCwPi9U2xAeZXv6nMzbEAjEvsG5l5sQLL6uYV4VWxA4+zwD1lhbEC964Fl2m9sQGWbQxYCbWxA6Ogu7DiFbEDstN4md3tsQKt0lHXJnGxAOAdx1Sd3bECNMnjGo4BsQOBDIx8gxGxAEcMjDI6pbEB2X8KZTplsQN5XeSwxr2xAhm6b8mC6bEC0pA/zCctsQGliqwIi4GxA/h3oSeL5bEC9Wv2W1SJtQIl+2HZjNW1A5Xi2uBszbUA63dUVEk5tQNP7345OkG1ALKSWqYCRbUCvHFn1wXdtQM66oM2foG1AhX3eRzaibUAb42xpIGJtQGOWVCGvgm1ADjeYXP52bUAHa2FaiXltQPZLb5OZiW1AyScPyiKVbUBsSDJCR7FtQDlk7GTIqG1AM0CdZaS+bUAsyp7UxrttQLx/Vs7b2G1AbeVTif3xbUBz2QUQf9ltQBgEbhGwAW5AnHp1GPr1bUCAoVNYRhVuQCqVfc13Jm5AcJvn17pmbkDm45zZBGVuQHYFCeNHX25Ap4kCcRlkbkBIEcNu1l9uQNaMXDLgfm5A3CktBZhQbkDH4NLkUDRuQNSSoReWF25AajLwpm87bkCGpwijnXduQAF7/KC2eW5AqwYDSMB5bkCiw+LJboFuQCvCIYkNrG5AR+Z9TNGTbkDFF1Ko4qBuQK4D0h2IxW5AXxFNuAHbbkB5gptpg+ZuQGA4Q5/8C29AFsUXYuy6bkAn1IMtDtZuQOBZOqGQxm5Av4SguWyRbkDeYQUjOaduQC1XoMme2G5AtPoJc6nZbkCfMqQJmP1uQHsADnagNm9AhfBe4SRmb0DEghocEKBvQLqAGEJAvW9AMA6+2RrFb0Bcq8YvSJdvQE5MLRXZlG9Adqu72E1/b0BDWm/ZNspvQDvd/aEGym9ApuIJkWjTb0ABt4n5fupvQH7nVc5U8W9AFHTtpoEWcEDOoKuZQw9wQMAiTAgZFXBA1GphMfALcEA/asBmE/NvQPKv0W3w5G9A5rfdqTHpb0BJ9a915eBvQJsLfMkt0G9AZM3X352ob0DaRaq2ZatvQEbNKa+vgG9AKj3js3Keb0A2n2uwhqZvQHLbNxFTfW9A6bOfqt54b0CFTiekPWZvQLbXdPXkdW9Ac1ShkTqmb0ATb8bLqW9vQFN9ybrIeG9AV4jQjxl5b0C/rdbRBZRvQLz0afdIoG9AwBFEeamNb0DZsIFs5IhvQF0DIQXcdG9A/959T7ucb0B5KxmJ2qJvQFKOyboh5W9AHimub6Hob0CzLtrOZtdvQLrAhhlnrG9AglZjayR4b0Cy7F3R40pvQG+2ZcLgWG9Aau7v35hub0CzuYhmcEFvQOe3wzGFY29AsGMnmYBZb0A5pkDEo2NvQNy5zrernG9ANDEDRM++b0AcZEQaqLtvQOSajBiUpG9AgK1qECllb0AmWW9mllNvQJ6EEnZFUm9AN4JkIB84b0Bh62uCXDlvQNNzV7hCQW9AcXgCV4E/b0A1JKMoJTBvQLi79Wr7Om9AhFuEb5BOb0AtP8CGHDFvQIDFQIazY29A1VHLh/xlb0D0niiWB2xvQFr3SushiG9APBfPEAmVb0BWCgzN+bhvQHCC54+ax29A9a56HXK5b0D2/SgxdMpvQI0HTG1Lq29A8lBXYzW0b0Ag+dbOlbhvQPIL/41dt29AIBgjN7G4b0Dj1Hy/6YFvQDA3yjvLdW9AeQGAdTR+b0ClAX6kwohvQNXlWpFQbW9A0sns4kGbb0CxHXPS+oRvQDmMZUFxqW9APz+/eI28b0CHF7E3UK1vQHYhUjSQ629AdbVKks7Kb0AUNCXsqO1vQF7umCqPCXBAJ9NCoIwBcEC7IeB7jBpwQPN7BZ1EGXBAMgYnbJsgcEChCk4FfidwQNJMTTYQKHBAtFpRmsE3cEAXaHcNLEdwQB5cuWLqLXBAiMGESIFFcEAMXz0JoC1wQEQYVKrdQHBA8cs2+JxVcEAiJOuw4EZwQFfx1VAGQ3BAjpNgqw00cECsU0OsVTNwQMxO00ggMXBALlURGFJAcEA+nkpwAVhwQMFRwnoOcXBAtEbHKMhqcEBgtqmBJ2pwQKx0snbwUXBA+DKb8MNHcEBs0spK7FlwQHJJFgp+dnBAToVnd3ZlcECoeIUNtF5wQL6U/zYAYXBAsoUvEzRocEAt/khC+F1wQP5acIh5Z3BAeKzKAZ1icEAwRWghGmVwQD0C4QeQcHBAOXQJqGJzcEBidS+KH29wQJZH5eUWcHBARY2JSGVxcEBYisUJMm5wQKT3WVTGbHBAVa9TtRKJcEAF/uqNxJRwQGSaRJbCknBAVoLZoaGgcEAlE6qgAp5wQFSh7BCAs3BA2pHGcCK8cEBZIj5AI71wQADLYmsFx3BA6hxGbpuwcEBnT96CTKFwQMFOroPqkHBAEKdMVOyVcEARFt4HXbRwQIHSkcHI2nBAdrWPiLbncEDiPvpPcvJwQLOd+39Z2HBAwj4uB2PScECgF/eVgMhwQCB4/ulJwXBAtF9XhFHBcEB7ZWknfLxwQOrBDUOJn3BA6uy3BTmucEABKQxjx59wQJK+fKlTpnBAHoC3bDvKcEBG8ATyPb5wQKR6KzV6mHBA30ftDZ+WcECNdbLn9KpwQP3bwZ0CzXBAYFF7sw68cEB9UkmAucZwQOUx1Oj/wXBAMurYQwbDcECmnLzQDfBwQLisCvi52nBA6u5Hm4HhcEDBSAmZtetwQNAOcEzN/3BAZQ8k9RYEcUB6n3YNvQtxQPhaGC4xCHFABBIR2DcPcUAen81ycgxxQPPUFrocGXFA70B7H+sVcUD099ILrB5xQNHAdYHsIHFAcdGlIKk1cUAMtlfwjjdxQA0g+OroHXFAJSB9EdI0cUAYc0GCDClxQKi8ewBPIXFAOCtEfn8acUAQ+fuCNTNxQP/+LRzCE3FAYvbnpa5IcUADlqaTTFxxQDTRiUiXYnFABkDtwW5kcUAyEf+0uGtxQPBHY4vHdnFAdNXiqtKDcUCEilC+bo9xQCcVY/yVk3FA2ML5ASaTcUAoZ1y2cpNxQO7OIrAMk3FArUcr7uincUASNWx1ILRxQJXDY/LMh3FAkFIf1gOVcUBREr7L2ZpxQBgGHHxFnXFAqmznbsaZcUCCh3Eee65xQDn73oFcwHFAkE7RHre2cUATI/+58r9xQIIL/CMfs3FA2mRoi8SfcUC28q/UdZhxQIR6tIP6qHFA2y2s1fK6cUDuaKbA4MVxQLodnBAz1HFA3oOxpi3fcUB+gWh2otdxQBdD8e9D4XFAXqwUvMrwcUDHRMmhlP9xQBLOu71SFXJA8YgRRmAjckAg6CQguCtyQEKj68ePP3JAZtZIeNtBckBoOJyhdzZyQGLqbAeUNnJAJE1POmEtckA=","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"++vq7S1ZSUCPp37iFGFJQKLSWPGVzUlAjXliLyKqSkCAuxwMw6VKQD/kj3JkoUpAeYFrsSGFS0By3iCY9gBMQM+EI3V43ktAHsdCqoQ9TECB3n7+zBtMQDtKwo/J+UtAzoIByFsyTEC5qqMDD1dLQDLNtYHek0pA2uGkEX9lSkDoYAE4dP1JQI4nSxNHP0pAGJXeiqbkSUATjlfGeUlJQBcg7sCtHkpAGFOmHWEbSkBMReN6nz1KQKt1dO7aoElAXOPGMsZ0SUB4FYCCkpxJQGk3K1rYIklAv8wr04hsSUBpcUCyQDlJQF0jVBOELUlAABUH9Bj6SEBNdjkBygBKQN6k5VOpGEpAFQAj29+qSUBoFpGbwi1KQDGcW5oXq0lANCXIPm3fSUBHPedfMP5IQAlUY/nHbUhA3eG5UpSgSEDtnfz+sxhJQLXb6/08SElANhxqBwlTSUBwFCUQGEZJQEkoWXRxokhASh8QM+dfSECGc5WWij5IQDPLJfhz30hAT2XhQAklSUA/lrqN91xIQIkKtbwMoEhABFAC9luISEC4SE0uUEtIQAYKgzA1s0hAhkv0lMZQSUCQZ2lelOFJQNTELn3Cj0lAGnhf0ceBSUBNMAtCyMVJQIYrTYVAXEpAU3QOioQ4SkB/kEd3WjpKQKdGd65XxklAkQic+9NGSUCBzdJtbshJQIuFYE2hj0pAkolLRgOgSkAvl/2jEDpLQBKCcVT0gUtAgBdPpfpIS0Dt+vl01pBLQIgXbHBOb0xA24dsF1KETEAu8E/vMWZNQNKTLLp3MExAzpKybUWzTEA/0gdjA9hMQP2pLR1Xy0xA3Gb2h6/wTECOnxB54AtMQLsqT91bCUxAaJVIVKtQTEAcvrCPcCdNQMVal3uz/kxAlTpJXdCwTEBUdrxjMIpMQAgs3eL1GE1ALAenAKRcTUBarZFrbjJNQGTwdsS6jU1AE1qSZ8GzTUCe1kmQV0lOQBZPsEkUCU5AigX8Db34TUDahf4NJuBNQNNnZDJrPk1AzdfqEOx9TUBprNzs77hNQETMPhUx001Aj3FXu8POTUD5oZx2MjNNQIb8fVv0Fk1At2lb468ETUCLMnB3mLdMQLBWbQ6NvExAguhGmN4JTUAtcgS85hROQO5ndObYRE5Af55S6Wl/TkBuc1ERfI9OQHDxWV97s01AIZJxKrDJTUCBIqpj/+pNQKZcwYHkP09AuZSg0t5AT0Cz8KyGEYFPQIC02LA5lk9AjL2CDDwaT0CgEreqHcZPQBxh4k37H1BA8NYkX2hfUEChNIHEATJQQH4gGPOVmFBAu8lf0qpLUECW6TqwBn5QQL7C0ukDF1FAnhMnxGvkUEASxB5Y+sxQQEYUCtUn4FBAWrDjr7vMUEAXm9tqSnZQQG0Ino16h1BAW52zkUpQUECttyC1ZntQQLI4L6lbTVBANiUPllq9UECiLXGQBphQQJogVrU2kFBA48KnLBTRUEBDVlR+Go9QQKo0Pv91qlBAojcgBusKUUCssJDR1rBQQNSF5ah0yVBAZNgjYePmUEBJCL2QuSVRQAdWOSpc41BA3OORmqabUECBYPfk2slQQBfqxH2p6VBAZ767XX4GUUA67qRfdylRQFvRCKa+ClFAxwlysWgmUUDS4Uox90VRQA4dFQ8MJVFAyq5gtUGpUUD8QH7JYdRRQKcHRUXwlFFAQB5/C8LLUUBIghGpLZpRQKhf+A1a2VFAlXTXSE0wUkDq0oIGlAhSQDXWqccIU1JAtwkolUB6UkDadywEqrtSQKI2Xt/bQVNAor/PO/Q+U0DZnQDSgxtTQLNG39Bi71JA0KhUYfnHUkAOXB/y1s9SQETcAy558lJAkTckSPsQU0CGnvOmuFJTQAqFo3laYFNAnpI7+i3KU0CLhNGjCsZTQBrXCLHugFRAwNjkbMa1VECckFOOt4tUQFcwcNr6U1RAZyk4e6h/VED1PyoRKH5UQMbpFw2nuFRAgOhaYL3jVEAsMY7y4OtUQACaE+F7wlRAOUkabFVuVECkAnSFjl5UQHFMJI8qolRAjm0wEqu8VEAIv9ivvXlUQG7AGOKfkVRAPK4fuRW3VEAuxyRnUYtUQAg7xdX0oVRAKYyhU3uyVEAe+omzIXZUQHmuS33UmVRA9LHgLoXKVEDg09axohxVQFIdzfzgbFVANuV1DYIhVUB4vvGGSfJUQO7HOaoMIFVAtqh+VbtNVUCrmd2sfntVQOCOxKDeflZANJ3r5TSwVkBeLDrOrgVXQBGbcPiJT1dAcLgrKgeGV0CvuuqXpn5XQJizWlgGvFdAwgJELV2XV0B4DdbwAZVXQBe0XYu+gldAPkQyxciUV0BCYq7vuDVYQA5mnXYEy1dA/r9G87wDWEDPbHIDU6lXQDzZiK7rl1dA2OEt2WnqV0DsoevPU/tXQLDFfNcmw1dA1BMUGyyiV0CmZ29vd9pXQE7gjOiFuFdAcnl6Ki3TV0CYxm2d5OJXQIbRSJj9xVdACKVaxgBcWECYvD60X5FYQMqtZJGQHFhAF4NFPEw1WEB8iHRTvhdYQF4TzWQZW1hAEqKyiC01WEBAfvt9ojpYQIpS8gDBZ1hAuHI4VvarWECgy/968WtYQEh+7c9VY1hAS5a1G71RWEDmhxjDuTRYQJR6rcODslhA7UrjyDvZWEDWHBNDVpVYQJgf6U/h3FhAeLfvhH9xWUD1mOg6YMBZQL7SLqzva1lAGfsSyL5ZWUBW9F+noLdZQKrzTf8il1lAYg61VVfAWUBiUTe9t/5ZQDDY1LUx0FlAn0AxPy/ZWUBKQWwfixZZQNKQClvI4VhA9M07FGveWEBU2AozXJtYQJWlGm2iEFlAjrIzyufBWEDYtabmirJYQLJvLcG1x1hASIVMYMAwWUDEGbwCqOFYQA7A6RXmOFlAjCdIi1pGWUDctGtOVhRZQLLWqzW2PllApD9ZZ0BYWUCRkhlAoz5ZQMBtELDnT1lAIOyAggtEWUBuEY8tHFhZQP4NslNJj1lA0OorbZcBWkA+O3nbK79ZQEJjsEZ8VFpAZLjSEVrkWUCEuMoFcOdZQPDmzc/jGVpACEvrYaw4WkCC4KHfnh1aQKD2ccwZHVpA+PJzRVkKWkBjoEvrbfFZQI/BkZmaNFpABqfZvUBYWkAqxSnptDhaQCOwUcEUf1pAZIy6WYyfWkC2fV0VX+BaQAx277h3FVtAkiWlRDbtWkBQE94PKdZaQAiBOYXJEltACm14oKZGW0AumaH5HFJbQB31JBFsZltAD2ztIP7EW0C2JXyf7qtbQBPpBBG/21tAZEleJJvbW0AK4gZ0edpbQJnn+JyYLVxAltPdCDlvXEAEB2NgFrBcQCZqKyRwEF1Av3B3EZUeXUBZhVf8BlddQG8rC2D4T11A7ZESUYRxXUDWylba/HVdQNn2s9X+iF1Aw9L+r+K7XUAOxoLCUZRdQIh7oTsIJ15AaRi0cXLzXUDhHln6ibJdQHegN0R1CV5Acm8Fq+xIXkDQ/RAMq31eQNcC86iusl5AqZgPz7K+XkAV90L+EpJeQP1YbMa5o15AueIl9S+FXkAv3YseZdBeQAOe+4fI015AWaCWYsCrXkAjmneZ+6NeQJBNUd41y15Aqor4mu6zXkB/E3olHIxeQDbUhYSBqF5AoIKX2fvEXkDdhZrkVrFeQNrfjTP+n15AAaxV6KS7XkCkUzBLxGteQFDQDDV9Hl5AOwthBE/9XUBS1u+ycvxdQME3lWglHV5Ad3TdcV6IXkBqcdojD8xeQCta1oGfzl5AqmThvjTaXkDtl8Ea2KZeQCv81ZV1sl5ATFGNAMmsXkDQuxk5Pc5eQKRV2KsYpl5At5/OcSLUXkBJlo+jB0NfQFg0PIPeSF9AkjYe9WBvX0Dmfp8TWahfQKP8ykd4m19AX/OVnJy2X0B5Ir65N8RfQNUmy9lE119ANaqQqJiyX0AjrnwI98BfQC0Sgwmj7V9AbBjgr6crYEAxwAFvwFBgQEDRTbQNnGBABk86/uWJYEBYgJhxNqxgQOK6DMh6uGBAovwMDPQEYUCcwhXefPFgQBrsJEQE3WBA3R0PcTzQYEB4A0vjq5JgQGpvf00/iGBAdn9R41p2YEDYM1lQkYFgQNz/+WDnkmBAxurNXlfVYEBOHHWkJ/pgQEvCMwwY7mBArzJ6or7XYEDA1SbW4u1gQEBU1+IJymBAzKSnmAsLYUAETkP4LzdhQJp4AeKSLmFAAtzmSCf+YEBagnWb4C9hQGO6ZLKcMmFA/my/SZ9gYUDih/4iADRhQEK63XQ4J2FAP/oZ0MktYUAqmfITsTVhQJTdpIqwLWFA7HsGVAVIYUCmShvIQSxhQNi3Fc8ZLmFA+p+1q1k4YUDFCIpaNk9hQNAGgU1ibGFAdOh9ostOYUBiWkqSGiRhQPJdMbNjU2FA6HKWamxkYUAXWfM231JhQFR/0aboimFAMk6CqAKVYUDOblvcJcFhQDuGRl+1yWFAAsJOaw0SYkB2HESSn1BiQNkPAXUOT2JANvld94t0YkCYAjtJmY9iQP7kCYTLwWJArgBGQ1GpYkA0ad3Lq8ViQFpZS8/w7WJA4LOpnQ+8YkCoRhvDmJxiQEifqsW9YWJAah38MIVfYkBayI+y4XxiQBzkDmhbs2JAJKF5yiC8YkAXRznPpPZiQEEnF2vh0GJAGtqZtcWgYkBffAgQZaViQL65DroVuGJAEOaZSnC9YkAGi6o0roFiQGgNzIg6hWJARgpguOJhYkDOeOYTt31iQGyXi6bYj2JAgu8rjit4YkBEx4lbIG5iQNytLq6hUmJAd/b8nAZXYkBgDwSK/XtiQFP7EN/YYmJAtNufa2B5YkBxFjPzzm5iQFPuwCLWW2JA1l44vs9eYkD//RxwFURiQPvmw1fEOGJAuqQlutUYYkBK+8knG15iQGSwN2+iZWJAoHberqRVYkBesKoB5GJiQLabEjeyZWJAJJWibgZlYkDcWvUVFH9iQCLlKv23nWJAP1wGhiSTYkBawgzSHYdiQIipO/+2hGJAoP8OD3RBYkBU+30DXhdiQBzv/tiBSWJAXOS50ouEYkBRnbIe+oJiQBiXr6zTm2JA9VH41S+sYkB4S2ptHBVjQPJylGJWP2NAktlY4qRBY0CsczN/dyljQO7QfON1/GJAh/YOEF8JY0BJ8/pvkfdiQOkuK7x00GJApYvzaCrCYkCN38rE9KViQF2RpTtY4mJANFagBvUEY0CkhS8dGgtjQBt6ZTfcQGNAfsPlarxJY0BfgWktkzRjQLhbhAK3a2NAsGUxKVyDY0Ca9L5wkWhjQPOpAZbgaGNAK87V60FTY0B4hwNtaC1jQOGLghJyUWNAhBrWafKUY0BWp3O/l25jQGyV6f0Bh2NAYoPyU5Z4Y0CE318yZm9jQFvnq7TXYmNAftMZS5hNY0Bk37oujFVjQNJybnBbQWNAwo/na2lQY0AOU1pFNFVjQPbhYDX1U2NAJljU2FA9Y0CVfBRWQjFjQGg2s+bUT2NACviU0EJmY0BCkAsVYU1jQNBeYzb1VmNAxfXL+WZ1Y0A4lpKbYUZjQKoD4zYrXmNA3KKLZl1PY0A5oRUlBmhjQICQBO3/VWNAOIdPu6QiY0Cx+9RN9vRiQP2MrJ3m/GJAHlGjqZwLY0DWdZ7mEvViQLaZeKboD2NAIt2/4CPhYkDOf8TzbOViQDdJH7USxWJABgQgRJ22YkAeUL70h75iQJGYiNllqWJAKkCB+H2jYkBm3+HrF8piQAgoWGwIvmJAeOkg0CzfYkCU9YmnbMFiQMCJ1DXH2GJAcgW/8E4NY0CGf/CgncRiQENT3NyDsWJAOO85o2HKYkCMwJawSMpiQI6sTISP3GJAJAz1EaLPYkAunk3QzdhiQOGs4Oc42mJALonDxv0FY0BcHFhkiBRjQLyX1268JWNAC4+jvPQeY0B8vquqwBVjQDZbzYxPDmNA4CxNTVUhY0Bia0H/QhpjQCCEkzvvKWNAT3PpUL9yY0D42Dn4BZVjQPoyuZX9kGNAln9NVNS9Y0A0OJ7GK7djQCimEtxZfGNAnu4/BH5iY0Bah6HjBi1jQNIr07AtKGNArppd+SovY0CcwaC/NmtjQIJ3bFYTfGNAQSW23XZ7Y0BZVa/BZ5xjQLjUP4kMXGNAVOwwF/1pY0CK/hJrDoljQN4mXz0kYGNA/9TZRSSLY0B2O6uiX5xjQMSNMP97lWNA4IFYJSKwY0BBWdAPMv9jQMT2ZE9qC2RAfugXIsIZZEAgPN1ydRFkQDbxceyp/GNAxm9Db6IdZED0eMHLowhkQLbqbndUEWRAnD+Q5XEIZEDLhnYZLB5kQBixA9w/L2RAszNiydlWZEDyad8h7kxkQE0tg7exSmRAN9qgFcYxZEAYhYfV9ClkQAy4aRRuPGRAZhwBuw1bZECp54zA8UNkQHC2vvYrZmRABLbKv/OXZEBHqAYClatkQEqVhB4K7mRA9fZZo63bZEA8rKbTP7pkQMbZUvO6h2RA/CYL8gC+ZEC1QrDoV9lkQBaylfX23WRA4Na/3FLtZEBk+IpBtc9kQGYxmUFfJGVA5Gh1POguZUAS9V7MzjhlQAShsq1uVmVArvORgTlsZUAapsz2yXllQMA9HczkZmVAFoRSd2F8ZUCuvihpAb9lQN5Y5n118GVALPrRRtcpZkBk8T3M4R9mQJv46VqdBmZAC9wFT/0IZkC4vQg1LBFmQLWQXTmWOmZAuq4u9NMKZkCOChhuLEJmQPPT6W2EQ2ZAxOPS0UE8ZkD6TYIPRSJmQP4M7t+282VADZgqsHQUZkCF53y1Mx1mQLadzr9S+mVA4osr3UfXZUDwMwSE79JlQFoIP4q+DmZA9eAfXtYMZkD+K0YFI+NlQGujHUus4WVAjWy8iljfZUByROsLco9lQLwpu6kblGVAtLdRPx6TZUCcIkr4y69lQPh5qQRd8WVAkbcqPdAbZkColxznmxlmQPI2faSZ/GVA6xaMAVlVZkCFKgGGpF1mQAo4Pwh9ZGZAnxmszB1qZkC1quzo2nZmQH4PmbWieGZAoKWSq61sZkAkh6YzlGFmQEXgdknuZmZAScuE8/BbZkCbu/W3h0tmQMSJuf5VVWZApK4kn5NTZkASgXK7GopmQBEwNmOsO2ZANaJjaQBlZkBwwte9RpNmQJgnxe1TV2ZAwWMyCMNSZkD1TqSWRk1mQL2DE6eiJmZAl3qnLSUUZkDi4LK9AfdlQFhPkL16NWZATPrGN9JZZkCv4nwy6YhmQCp55ohnpmZAw8MevqyIZkDRcctFSn5mQDPXCKFZlGZAduanW6RzZkDvwiOk25BmQL15pkuRj2ZA/hbYKfmJZkDEA12/H6dmQNCedI29u2ZAPWDw6pa2ZkDsnSeMFuJmQAlmb+DkxWZA/9RsBQHgZkB/gVAbYvlmQJm5x7Pg9WZAnTWzyLYGZ0AC18IPFOVmQA7BRRcMCWdA/ofJxYcJZ0CILo8GNP9mQG1awegrJ2dABkBgUwgXZ0Bm9FmcXPBmQLBRxBrzxGZApvol8L3eZkAhJ5IPK7xmQDdoGby4+mZAf+AT+H++ZkCX41e9L/tmQKFacstWCGdAiy7A66QLZ0ABe/9XmwBnQNWaf3fHE2dAu/00kPkYZ0ArAFs2rkJnQD9Qf128TGdA93aJCfJXZ0AO4du5tVJnQGxWoaBJV2dALR4giolnZ0D14HU9NjdnQPEqG054EmdAI5VJhqcwZ0BglHunhTxnQC1m3NwIPWdAdUEFRgZEZ0DHzhYQjFVnQBRhZcCsSmdA3wl8Rys4Z0BaJycL1kRnQNkoIJ3tK2dAtNiba2Q/Z0AS4mVBTw9nQFHimX+kNmdAEa3BaipMZ0BICW02wlpnQIYzMNGagGdAOIOmyEy8Z0AJ00LnKONnQKTFldymrmdAAhGX9xqMZ0AN03Xagn5nQGolwv2+hWdAbvjODbacZ0A9hh4p44tnQLgZ0I1DmGdA3rJi232GZ0CPgAC0UnlnQF32RLy1UmdAuYg4ApE7Z0AA+4tothZnQOEOZ3Ti/WZAa7nNSQAmZ0BH02w2BQ5nQGQbIxaoaGdA3dsov9d+Z0C1jgFTKItnQHSwqw4XdmdA7bpVZeaSZ0BPYLcr4YZnQOE8PhMvkWdAyn4NsIHpZ0Bo7D0q1exnQMskf2kCGGhAteMMZOgHaECkfEQqMA1oQFYcw/ZATGhA92bxP5c+aEBMFnY6/X5oQHuLP4gJnGhANABTNHCQaED9BeFJE6toQJ/DydqY0GhAnqsxH+XqaEBpqK49DL9oQNtS5u4trmhAPR4ZqaisaEDMjUhNrbBoQNUEaj/wymhAbdrSXwbXaED4jiGisbJoQO/3Np1CxWhAEEu77jLfaEC6AMoig/doQBA0pkp/IGlAyECQLpVBaUCHj2IvrVZpQG91n8nUWmlAha96mBQsaUACNJZtOkBpQCCfojRGTWlAzQYjYV1caUCXIbWn6DlpQDjCOAq3HWlAvgA83tBFaUCp4HY780ppQGQ0anwoZ2lAMAbB3nZuaUBALE0I0XVpQLczdto9mmlALbug0CCQaUBDT+iimplpQBPewhM2kWlApWKHFbWJaUDM5P2+NoZpQFPbrN24k2lAIiByW82KaUBp6VDpYrlpQFoWObAoo2lAn3VhPJSjaUCvkelb6JtpQMpi4Gac0GlA3eiE+kzdaUChQcI/uARqQFtaQPCU22lAP4qmA4fqaUCYhw1FZQ1qQIJLfL1tFmpA2F8vjuw+akBMzRNUxTRqQD+9RxpFaGpAn1x6Ezy4akDo6EMaBrNqQNlYKfIqq2pAwXa5eBPgakDoTwK6BRlrQAUWdduwDmtACcg7FqUHa0AhPHobBwVrQBWjzMRn4GpArrdukmjJakAb5QMNra9qQD9JN6eBnWpA6o8i6suiakDcBW4AsbBqQHIOChq16GpAKhtE/CjPakA8R1r0DvVqQJm4nluc9GpAhjUajW35akDPzYwSbBVrQLiQNyLl92pAJij7O4gKa0B0YvY1QhZrQBkChMVqLGtAG/yDChI8a0CEu4NCCpFrQCorO0sIg2tATBFOxHB4a0DjvU1m5mprQEOVwFSGX2tAIaZ2QYdRa0DcfEIU+n1rQIQB/D/VsWtA9yinGPila0BV6+XWu5FrQOYBYBM4p2tAM/8cqvKba0AMob0KmrZrQKBMj8l+w2tAdlIoNmSZa0AkfC/GT9FrQIUUCgEuEWxARVtpcPgDbECbsLXJ9v1rQBoeSf+CDWxA7GFpRJ0ebEB1C0B2FjpsQJJ6HnXQgGxAa3pJTo2BbEDfJwYOaG5sQCasWBKrSGxAr+tZsa03bECdLnS3BD1sQNlndyLYfGxAb6FLQ65ybEC6uAvvPYBsQOjtIswdhmxALdzdg4uybEAcTaiLzgltQGBujBE4/2xAJ/UK+/T1bEBVtIMlxR1tQG8GBJr9OW1AEpdgOnx7bUB6Y/AqkpRtQFZVNj95j21ARV2SSsaobUClNecwp9JtQDyCJvtO821Ai59M+PAJbkAmbqiReTJuQNPepNZKXm5AVlGJ4uuQbkD/sCyEFKxuQGbmjuIhrW5AN/DbKTq4bkAchvNIPOVuQBQyE1l+0W5ABylx67DjbkA8qZAWgd1uQIldOvfS5G5AAzGCcSIUb0CLhbNTpiBvQGDhmSKJKG9ABq/5l2sDb0D0qIhNsyFvQB0SYILBPG9A+qi+SmGIb0D6XWdf7oRvQI4LWg1Ykm9AJyg+Z7mgb0A4gPFMmtlvQM2EIAr03G9APe0TL0jsb0Au8208EQNwQNk3eslACXBAxoUaZU8FcECkeGmWnQtwQFwFGWL+H3BAiLv9+cQScEDcCwDSGBhwQHYIUVMYEHBAfgK7b2omcEDU2bKpPhFwQDQc3YSAC3BAcgBix7wUcEBE3oFd/DBwQEbBj0AiM3BACtbcWnMtcEBtxzfGv05wQJFTy+jHOnBA1ufWzMwacED6pqFMCiVwQJIz0hU1IHBA6nPMUBETcEAZmJHxmSFwQISRWMCzKHBAiE47fOAicEBEbveOlxFwQF5j+1fAInBABEmIxFowcEB0RE2+9zFwQGSRvMS2UnBAfo+75spEcEAl3dmMlS9wQLDQ2rG2J3BAdu2mLC8qcEBYK8IXRzFwQMwsnjCeMHBAjEaoaHM5cEDocKdNoChwQPp9xCntQnBAy6g2389EcEAL5gGE41lwQHy8x4SSYnBAaplxlBRtcEB3e/CLZXlwQEgll4/Cg3BA5nhzWj6RcEAhMRjDtalwQMIxrekNsHBATV4zMZm+cECKjbTVXMBwQO8sQcGa2nBAXFa4c/vScEDm39VN//JwQAhIXwSO9XBAyIIjw9zhcEA4vo2tHOdwQJC1VEpq33BAwlAuxBDwcEDvCIrR0uhwQFjhf3Pi5HBAm1ajF9rJcECgekml0MVwQKxYzKI7onBAQ92CKxeMcEAWCUUHdZtwQNYlqN06q3BARvRxvTy1cEBwO6s096hwQDLK4vpmq3BALMSMbIuucECaaFb2NZ9wQAhL6hR3unBAfQoo9bPLcEC2eqAWXstwQDkhrGn/znBAXGcVJojVcEDMKhpnELhwQDT6BypPt3BAzpTq9JivcEC4r2mFxaJwQHWVl1V5oXBAHV2922/BcECWQGLz485wQFDfAJrzyHBAWhXEGVDVcEA=","dtype":"float64","shape":[1000]},"__x__values":{"__ndarray__":"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","dtype":"float64","shape":[1000]},"Google":{"__ndarray__":"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ACxY0dsQgAAgHwISGxCAAAASK1IbEIAAIATUklsQgAAAN/2SWxCAACAqptKbEIAAAB2QEtsQgAAgEHlS2xCAAAADYpMbEIAAIDYLk1sQgAAAKTTTWxCAACAb3hObEIAAAA7HU9sQgAAgAbCT2xCAAAA0mZQbEIAAICdC1FsQgAAAGmwUWxCAACANFVSbEIAAAAA+lJsQgAAgMueU2xCAAAAl0NUbEIAAIBi6FRsQgAAAC6NVWxCAACA+TFWbEIAAADF1lZsQgAAgJB7V2xCAAAAXCBYbEIAAIAnxVhsQgAAAPNpWWxCAACAvg5abEIAAACKs1psQgAAgFVYW2xCAAAAIf1bbEIAAIDsoVxsQgAAALhGXWxCAACAg+tdbEIAAABPkF5sQgAAgBo1X2xCAAAA5tlfbEIAAICxfmBsQgAAAH0jYWxCAACASMhhbEIAAAAUbWJsQgAAgN8RY2xCAAAAq7ZjbEIAAIB2W2RsQgAAAEIAZWxCAACADaVlbEIAAADZSWZsQgAAgKTuZmxCAAAAcJNnbEIAAIA7OGhsQgAAAAfdaGxCAACA0oFpbEIAAACeJmpsQgAAgGnLamxCAAAANXBrbEIAAIAAFWxsQgAAAMy5bGxCAACAl15tbEIAAABjA25sQgAAgC6obmxCAAAA+kxvbEIAAIDF8W9sQgAAAJGWcGxCAACAXDtxbEIAAAAo4HFsQgAAgPOEcmxCAAAAvylzbEIAAICKznNsQgAAAFZzdGxCAACAIRh1bEIAAADtvHVsQgAAgLhhdmxCAAAAhAZ3bEIAAIBPq3dsQgAAABtQeGxCAACA5vR4bEIAAACymXlsQgAAgH0+emxCAAAASeN6bEIAAIAUiHtsQgAAAOAsfGxCAACAq9F8bEIAAAB3dn1sQgAAgEIbfmxCAAAADsB+bEIAAIDZZH9sQgAAAKUJgGxCAACAcK6AbEIAAAA8U4FsQgAAgAf4gWxCAAAA05yCbEIAAICeQYNsQgAAAGrmg2xCAACANYuEbEIAAAABMIVsQgAAgMzUhWxCAAAAmHmGbEIAAIBjHodsQgAAAC/Dh2xCAACA+meIbEIAAADGDIlsQgAAgJGxiWxCAAAAXVaKbEIAAIAo+4psQgAAAPSfi2xCAACAv0SMbEIAAACL6YxsQgAAgFaOjWxCAAAAIjOObEIAAIDt145sQgAAALl8j2xCAACAhCGQbEIAAABQxpBsQgAAgBtrkWxCAAAA5w+SbEIAAICytJJsQgAAAH5Zk2xCAACASf6TbEIAAAAVo5RsQgAAgOBHlWxCAAAArOyVbEIAAIB3kZZsQgAAAEM2l2xCAACADtuXbEIAAADaf5hsQgAAgKUkmWxCAAAAccmZbEIAAIA8bppsQgAAAAgTm2xCAACA07ebbEIAAACfXJxsQgAAgGoBnWxCAAAANqadbEIAAIABS55sQgAAAM3vnmxCAACAmJSfbEIAAABkOaBsQgAAgC/eoGxCAAAA+4KhbEIAAIDGJ6JsQgAAAJLMomxCAACAXXGjbEIAAAApFqRsQgAAgPS6pGxCAAAAwF+lbEIAAICLBKZsQgAAAFeppmxCAACAIk6nbEIAAADu8qdsQgAAgLmXqGxCAAAAhTypbEIAAIBQ4alsQgAAAByGqmxCAACA5yqrbEIAAACzz6tsQgAAgH50rGxCAAAAShmtbEIAAIAVvq1sQgAAAOFirmxCAACArAevbEIAAAB4rK9sQgAAgENRsGxCAAAAD/awbEIAAIDamrFsQgAAAKY/smxCAACAceSybEIAAAA9ibNsQgAAgAgutGxCAAAA1NK0bEIAAICfd7VsQgAAAGsctmxCAACANsG2bEIAAAACZrdsQgAAgM0KuGxCAAAAma+4bEIAAIBkVLlsQgAAADD5uWxCAACA+526bEIAAADHQrtsQgAAgJLnu2xCAAAAXoy8bEIAAIApMb1sQgAAAPXVvWxCAACAwHq+bEIAAACMH79sQgAAgFfEv2xCAAAAI2nAbEIAAIDuDcFsQgAAALqywWxCAACAhVfCbEIAAABR/MJsQgAAgByhw2xCAAAA6EXEbEIAAICz6sRsQgAAAH+PxWxCAACASjTGbEIAAAAW2cZsQgAAgOF9x2xCAAAArSLIbEIAAIB4x8hsQgAAAERsyWxCAACADxHKbEIAAADbtcpsQgAAgKZay2xCAAAAcv/LbEIAAIA9pMxsQgAAAAlJzWxCAACA1O3NbEIAAACgks5sQgAAgGs3z2xCAAAAN9zPbEIAAIACgdBsQgAAAM4l0WxCAACAmcrRbEIAAABlb9JsQgAAgDAU02xCAAAA/LjTbEIAAIDHXdRsQgAAAJMC1WxCAACAXqfVbEIAAAAqTNZsQgAAgPXw1mxCAAAAwZXXbEIAAICMOthsQgAAAFjf2GxCAACAI4TZbEIAAADvKNpsQgAAgLrN2mxCAAAAhnLbbEIAAIBRF9xsQgAAAB283GxCAACA6GDdbEIAAAC0Bd5sQgAAgH+q3mxCAAAAS0/fbEIAAIAW9N9sQgAAAOKY4GxCAACArT3hbEIAAAB54uFsQgAAgESH4mxCAAAAECzjbEIAAIDb0ONsQgAAAKd15GxCAACAchrlbEIAAAA+v+VsQgAAgAlk5mxCAAAA1QjnbEIAAICgredsQgAAAGxS6GxCAACAN/fobEIAAAADnOlsQgAAgM5A6mxCAAAAmuXqbEIAAIBliutsQgAAADEv7GxCAACA/NPsbEIAAADIeO1sQgAAgJMd7mxCAAAAX8LubEIAAIAqZ+9sQgAAAPYL8GxCAACAwbDwbEIAAACNVfFsQgAAgFj68WxCAAAAJJ/ybEIAAIDvQ/NsQgAAALvo82xCAACAho30bEIAAABSMvVsQgAAgB3X9WxCAAAA6Xv2bEIAAIC0IPdsQgAAAIDF92xCAACAS2r4bEIAAAAXD/lsQgAAgOKz+WxCAAAArlj6bEIAAIB5/fpsQgAAAEWi+2xCAACAEEf8bEIAAADc6/xsQgAAgKeQ/WxCAAAAczX+bEIAAIA+2v5sQgAAAAp//2xCAACA1SMAbUIAAAChyABtQgAAgGxtAW1CAAAAOBICbUIAAIADtwJtQgAAAM9bA21CAACAmgAEbUIAAABmpQRtQgAAgDFKBW1CAAAA/e4FbUIAAIDIkwZtQgAAAJQ4B21CAACAX90HbUIAAAArgghtQgAAgPYmCW1CAAAAwssJbUIAAICNcAptQgAAAFkVC21CAACAJLoLbUIAAADwXgxtQgAAgLsDDW1CAAAAh6gNbUIAAIBSTQ5tQgAAAB7yDm1CAACA6ZYPbUIAAAC1OxBtQgAAgIDgEG1CAAAATIURbUIAAIAXKhJtQgAAAOPOEm1CAACArnMTbUIAAAB6GBRtQgAAgEW9FG1CAAAAEWIVbUIAAIDcBhZtQgAAAKirFm1CAACAc1AXbUIAAAA/9RdtQgAAgAqaGG1CAAAA1j4ZbUIAAICh4xltQgAAAG2IGm1CAACAOC0bbUIAAAAE0httQgAAgM92HG1CAAAAmxsdbUIAAIBmwB1tQgAAADJlHm1CAACA/QkfbUIAAADJrh9tQgAAgJRTIG1CAAAAYPggbUIAAIArnSFtQgAAAPdBIm1CAACAwuYibUIAAACOiyNtQgAAgFkwJG1CAAAAJdUkbUIAAIDweSVtQgAAALweJm1CAACAh8MmbUIAAABTaCdtQgAAgB4NKG1CAAAA6rEobUIAAIC1ViltQgAAAIH7KW1CAACATKAqbUIAAAAYRSttQgAAgOPpK21CAAAAr44sbUIAAIB6My1tQgAAAEbYLW1CAACAEX0ubUIAAADdIS9tQgAAgKjGL21CAAAAdGswbUIAAIA/EDFtQgAAAAu1MW1CAACA1lkybUIAAACi/jJtQgAAgG2jM21CAAAAOUg0bUIAAIAE7TRtQgAAANCRNW1CAACAmzY2bUIAAABn2zZtQgAAgDKAN21CAAAA/iQ4bUIAAIDJyThtQgAAAJVuOW1CAACAYBM6bUIAAAAsuDptQgAAgPdcO21CAAAAwwE8bUIAAICOpjxtQgAAAFpLPW1CAACAJfA9bUIAAADxlD5tQgAAgLw5P21CAAAAiN4/bUIAAIBTg0BtQgAAAB8oQW1CAACA6sxBbUIAAAC2cUJtQgAAgIEWQ21CAAAATbtDbUIAAIAYYERtQgAAAOQERW1CAACAr6lFbUIAAAB7TkZtQgAAgEbzRm1CAAAAEphHbUIAAIDdPEhtQgAAAKnhSG1CAACAdIZJbUIAAABAK0ptQgAAgAvQSm1CAAAA13RLbUIAAICiGUxtQgAAAG6+TG1CAACAOWNNbUIAAAAFCE5tQgAAgNCsTm1CAAAAnFFPbUIAAIBn9k9tQgAAADObUG1CAACA/j9RbUIAAADK5FFtQgAAgJWJUm1CAAAAYS5TbUIAAIAs01NtQgAAAPh3VG1CAACAwxxVbUIAAACPwVVtQgAAgFpmVm1CAAAAJgtXbUIAAIDxr1dtQgAAAL1UWG1CAACAiPlYbUIAAABUnlltQgAAgB9DWm1CAAAA6+dabUIAAIC2jFttQgAAAIIxXG1CAACATdZcbUIAAAAZe11tQgAAgOQfXm1CAAAAsMRebUIAAIB7aV9tQgAAAEcOYG1CAACAErNgbUIAAADeV2FtQgAAgKn8YW1CAAAAdaFibUIAAIBARmNtQgAAAAzrY21CAACA149kbUIAAACjNGVtQgAAgG7ZZW1CAAAAOn5mbUIAAIAFI2dtQgAAANHHZ21CAACAnGxobUIAAABoEWltQgAAgDO2aW1CAAAA/1pqbUIAAIDK/2ptQgAAAJaka21CAACAYUlsbUIAAAAt7mxtQgAAgPiSbW1CAAAAxDdubUIAAICP3G5tQgAAAFuBb21CAACAJiZwbUIAAADyynBtQgAAgL1vcW1CAAAAiRRybUIAAIBUuXJtQgAAACBec21CAACA6wJ0bUIAAAC3p3RtQ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<h4>Lineplot with rangetool</h4>
<p>```python
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()</p>
<p>df.plot_bokeh(rangetool=True)
```</p>
<p><img alt="rangetool" src="Documentation/Images/rangetool.gif" /></p>
<p><br></p>
<h2>Pointplot</h2>
<p>If you just wish to draw the date points for curves, the <strong>pointplot</strong> option is the right choice. It also accepts the <strong>kwargs</strong> of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.scatter">bokeh.plotting.figure.scatter</a> like <em>marker</em> or <em>size</em>:</p>
<p>```python
import numpy as np</p>
<p>x = np.arange(-3, 3, 0.1)
y2 = x<strong>2
y3 = x</strong>3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
df.plot_bokeh.point(
x="x",
xticks=range(-3, 4),
size=5,
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
marker="x")
```</p>
<div align="center">
<meta charset="utf-8">
<title>Bokeh Plot</title>
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<p><br></p>
<h2>Stepplot</h2>
<p>With a similar API as the line- & pointplots, one can generate a stepplot. Additional keyword arguments for this plot type are passes to <a href="bokeh.plotting.figure.scatter">bokeh.plotting.figure.step</a>, e.g. <strong>mode</strong> (before, after, center), see the following example </p>
<p>```python</p>
<p>import numpy as np</p>
<p>x = np.arange(-3, 3, 1)
y2 = x<strong>2
y3 = x</strong>3
df = pd.DataFrame({"x": x, "Parabula": y2, "Cube": y3})
df.plot_bokeh.step(
x="x",
xticks=range(-1, 1),
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
figsize=(800,300)
)</p>
<p>df.plot_bokeh.step(
x="x",
xticks=range(-1, 1),
colormap=["#009933", "#ff3399"],
title="Pointplot (Parabula vs. Cube)",
mode="after",
figsize=(800,300)
)
```</p>
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<p><em>Note that the step-plot API of Bokeh does so far not support a <a href="https://github.com/bokeh/bokeh/issues/7419">hovertool functionality</a></em>.</p>
<p><br></p>
<h2>Scatterplot</h2>
<p>A basic <strong>scatterplot</strong> can be created using the <em>kind="scatter"</em> option. For <strong>scatterplots</strong>, the <strong>x</strong> and <strong>y</strong> parameters have to be specified and the following optional keyword argument is allowed:</p>
<ul>
<li>
<p><strong>category</strong>: Determines the category column to use for coloring the scatter points</p>
</li>
<li>
<p><strong>kwargs</strong>**: Optional keyword arguments of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.scatter">bokeh.plotting.figure.scatter</a></p>
</li>
</ul>
<p>Note, that the <strong>pandas.DataFrame.plot_bokeh()</strong> method return per default a Bokeh figure, which can be embedded in Dashboard layouts with other figures and <strong>Bokeh</strong> objects (for more details about (sub)plot layouts and embedding the resulting Bokeh plots as HTML click <a href="#Layouts">here</a>).</p>
<p>In the example below, we use the building <em>grid layout</em> support of <strong>Pandas-Bokeh</strong> to display both the DataFrame (embedded in a <em>Div</em>) and the resulting <strong>scatterplot</strong>:</p>
<p>```python</p>
<h1>Load Iris Dataset from Scikit Learn:</h1>
<p>df = pd.read_csv(
r"https://raw.githubusercontent.com/PatrikHlobil/Pandas-Bokeh/master/docs/Testdata/iris/iris.csv"
)
df = df.sample(frac=1)</p>
<h1>Create Div with DataFrame:</h1>
<p>from bokeh.models import Div
div_df = Div(text=df.head(10).to_html(index=False), width=550)</p>
<h1>Create Scatterplot:</h1>
<p>p_scatter = df.plot_bokeh.scatter(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization",
show_figure=False)</p>
<h1>Combine Div and Scatterplot via grid layout:</h1>
<p>pandas_bokeh.plot_grid([[div_df, p_scatter]], plot_width=400, plot_height=350)
```
<p id="scatterplot_picture"> </p></p>
<div align="center">
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<title>Bokeh Plot</title>
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<p>A possible optional keyword parameters that can be passed to <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.scatter">bokeh.plotting.figure.scatter</a> is <strong>size</strong>. Below, we use the <em>sepal length</em> of the Iris data as reference for the size:
```python</p>
<h1>Change one value to clearly see the effect of the size keyword</h1>
<p>df.loc[13, "sepal length (cm)"] = 15</p>
<h1>Make scatterplot:</h1>
<p>p_scatter = df.plot_bokeh.scatter(
x="petal length (cm)",
y="sepal width (cm)",
category="species",
title="Iris DataSet Visualization with Size Keyword",
size="sepal length (cm)")
```</p>
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<p>In this example you can see, that the additional dimension <em>sepal length</em> cannot be used to clearly differentiate between the <em>virginica</em> and <em>versicolor</em> species.</p>
<p><br></p>
<h2>Barplot</h2>
<p>The <strong>barplot</strong> API has no special keyword arguments, but accepts optional <strong>kwargs</strong> of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.vbar">bokeh.plotting.figure.vbar</a> like <em>alpha</em>. It uses per default the index for the bar categories (however, also columns can be used as x-axis category using the <strong>x</strong> argument).</p>
<p>```python
data = {
'fruits':
['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'],
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 3, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]
}
df = pd.DataFrame(data).set_index("fruits")</p>
<p>p_bar = df.plot_bokeh.bar(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6)
```</p>
<p><img alt="Barplot" src="docs/Images/Barplot.gif" /></p>
<p>Using the <strong>stacked</strong> keyword argument you also maked stacked barplots:
<code>python
p_stacked_bar = df.plot_bokeh.bar(
ylabel="Price per Unit [€]",
title="Fruit prices per Year",
stacked=True,
alpha=0.6)</code></p>
<p><img alt="Barplot2" src="docs/Images/Barplot2.png" /></p>
<p>Also horizontal versions of the above barplot are supported with the keyword <strong>kind="barh"</strong> or the accessor <strong>plot_bokeh.barh</strong>. You can still specify a column of the DataFrame as the bar category via the <strong>x</strong> argument if you do not wish to use the index.
```python</p>
<h1>Reset index, such that "fruits" is now a column of the DataFrame:</h1>
<p>df.reset_index(inplace=True)</p>
<h1>Create horizontal bar (via kind keyword):</h1>
<p>p_hbar = df.plot_bokeh(
kind="barh",
x="fruits",
xlabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6,
legend = "bottom_right",
show_figure=False)</p>
<h1>Create stacked horizontal bar (via barh accessor):</h1>
<p>p_stacked_hbar = df.plot_bokeh.barh(
x="fruits",
stacked=True,
xlabel="Price per Unit [€]",
title="Fruit prices per Year",
alpha=0.6,
legend = "bottom_right",
show_figure=False)</p>
<h1>Plot all barplot examples in a grid:</h1>
<p>pandas_bokeh.plot_grid([[p_bar, p_stacked_bar],
[p_hbar, p_stacked_hbar]],
plot_width=450)
```</p>
<p><img alt="Barplot3" src="docs/Images/Barplot3.png" /></p>
<h2>Histogram</h2>
<p>For drawing <strong>histograms</strong> (kind="hist"), <strong>Pandas-Bokeh</strong> has a lot of customization features. Optional keyword arguments for <em>histogram plots</em> are:
* <strong>bins</strong>: Determines bins to use for the histogram. If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. If bins is a string, it defines the method used to calculate the optimal bin width, as defined by <a href="https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges">histogram_bin_edges</a>.
* <strong>histogram_type</strong>: Either <em>"sidebyside"</em>, <em>"topontop"</em> or <em>"stacked"</em>. Default: <em>"topontop"</em>
* <strong>stacked</strong>: Boolean that overrides the <em>histogram_type</em> as <em>"stacked"</em> if given. Default: <em>False</em></p>
<ul>
<li><strong>kwargs</strong>**: Optional keyword arguments of <a href="https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.Figure.quad">bokeh.plotting.figure.quad</a></li>
</ul>
<p>Below examples of the different histogram types:
```python
import numpy as np</p>
<p>df_hist = pd.DataFrame({
'a': np.random.randn(1000) + 1,
'b': np.random.randn(1000),
'c': np.random.randn(1000) - 1
},
columns=['a', 'b', 'c'])</p>
<h1>Top-on-Top Histogram (Default):</h1>
<p>df_hist.plot_bokeh.hist(
bins=np.linspace(-5, 5, 41),
vertical_xlabel=True,
hovertool=False,
title="Normal distributions (Top-on-Top)",
line_color="black")</p>
<h1>Side-by-Side Histogram (multiple bars share bin side-by-side) also accessible via</h1>
<h1>kind="hist":</h1>
<p>df_hist.plot_bokeh(
kind="hist",
bins=np.linspace(-5, 5, 41),