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"info": {
"author": "Patrik Hlobil",
"author_email": "patrik.hlobil@googlemail.com",
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"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Visualization"
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"description": "# Pandas Bokeh\n\n**Pandas Bokeh** provides a [Bokeh](https://bokeh.pydata.org/en/latest/) plotting backend for [Pandas](https://pandas.pydata.org/) and [GeoPandas](http://geopandas.org/), similar to the already existing [Visualization](https://pandas.pydata.org/pandas-docs/stable/visualization.html) feature of Pandas. Importing the library adds a complementary plotting method ***plot_bokeh()*** on **DataFrames** and **Series**. It also has native plotting backend support for Pandas >= 0.25.\n\nFor more information and examples have a look at the [Github Repository](https://github.com/PatrikHlobil/Pandas-Bokeh).\n\n---\n\n## Installation\n\n\nYou can install **Pandas Bokeh** from *PyPI* via **pip**:\n\n pip install pandas-bokeh\n\nor *conda*:\n\n conda install -c patrikhlobil pandas-bokeh \n\n**Pandas Bokeh** is officially supported on Python 3.5 and above.\n\n---\n\n## Description\n\nWith **Pandas Bokeh**, creating stunning, interactive, HTML-based visualization is as easy as calling:\n```python\ndf.plot_bokeh()\n```\n\nIn release **0.4**, the following plot types are supported:\n\n* line\n* step\n* point\n* scatter\n* bar\n* histogram\n* area\n* pie\n* mapplot\n\n
\n\nFurthermore, also **GeoPandas** and **Pyspark** have a new plotting backend as can be seen in the provided [examples](https://github.com/PatrikHlobil/Pandas-Bokeh#geoplots).\n\n
\n\n**Pandas Bokeh** is a high-level API for **Bokeh** on top of **Pandas** and **GeoPandas** that tries to figure out best, what the user wants to plot. Nevertheless, there are many options for customizing the plots, for example:\n\n* **figsize**: Choose width & height of the plot\n* **title**: Sets title of the plot\n* **xlim**/**ylim**: Set visible range of plot for x- and y-axis (also works for *datetime x-axis*)\n* **xlabel**/**ylabel**: Set x- and y-labels\n* **logx**/**logy**: Set log-scale on x-/y-axis\n* **xticks**/**yticks**: Explicitly set the ticks on the axes\n* **colormap**: Defines the colors to plot. Can be either a list of colors or the name of a [Bokeh color palette](https://bokeh.pydata.org/en/latest/docs/reference/palettes.html)\n* **hovertool_string**: For customization of hovertool content\n\nEach plot type like scatterplot or histogram further has many more additional customization options that is described [here](https://github.com/PatrikHlobil/Pandas-Bokeh).\n\n\n\n",
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