Metadata-Version: 2.1
Name: pandas-bokeh
Version: 0.4.3
Summary: Bokeh plotting backend for Pandas, GeoPandas & Pyspark
Home-page: https://github.com/PatrikHlobil/Pandas-Bokeh
Author: Patrik Hlobil
Author-email: patrik.hlobil@googlemail.com
License: UNKNOWN
Description: # Pandas Bokeh
        
        **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.
        
        For more information and examples have a look at the [Github Repository](https://github.com/PatrikHlobil/Pandas-Bokeh).
        
        ---
        
        ## Installation
        
        
        You can install **Pandas Bokeh** from *PyPI* via **pip**:
        
            pip install pandas-bokeh
        
        or *conda*:
        
            conda install -c patrikhlobil pandas-bokeh 
        
        **Pandas Bokeh** is officially supported on Python 3.5 and above.
        
        ---
        
        ## Description
        
        With **Pandas Bokeh**, creating stunning, interactive, HTML-based visualization is as easy as calling:
        ```python
        df.plot_bokeh()
        ```
        
        In release **0.4.3**, the following plot types are supported:
        
        * line
        * step
        * point
        * scatter
        * bar
        * histogram
        * area
        * pie
        * mapplot
        
        <br>
        
        Furthermore, also **GeoPandas** and **Pyspark** have a new plotting backend as can be seen in the provided [examples](https://github.com/PatrikHlobil/Pandas-Bokeh#geoplots).
        
        <br>
        
        **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:
        
        * **figsize**: Choose width & height of the plot
        * **title**: Sets title of the plot
        * **xlim**/**ylim**: Set visible range of plot for x- and y-axis (also works for *datetime x-axis*)
        * **xlabel**/**ylabel**: Set x- and y-labels
        * **logx**/**logy**: Set log-scale on x-/y-axis
        * **xticks**/**yticks**: Explicitly set the ticks on the axes
        * **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)
        * **hovertool_string**: For customization of hovertool content
        
        Each plot type like scatterplot or histogram further has many more additional customization options that is described [here](https://github.com/PatrikHlobil/Pandas-Bokeh).
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.6
Description-Content-Type: text/markdown
