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Fixed documentation.
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docs/api/options/series/gantt.rst

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##########################################################################################
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:mod:`.gantt <highcharts_core.options.series.gantt>`
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##########################################################################################
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.. contents:: Module Contents
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:local:
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:depth: 3
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:backlinks: entry
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--------------
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.. module:: highcharts_core.options.series.gantt
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********************************************************************************************************************
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class: :class:`GanttSeries <highcharts_core.options.series.gantt.GanttSeries>`
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********************************************************************************************************************
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.. autoclass:: GanttSeries
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:members:
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:inherited-members:
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.. collapse:: Class Inheritance
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.. inheritance-diagram:: GanttSeries
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:top-classes: highcharts_core.metaclasses.HighchartsMeta
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:parts: -1
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docs/demos.rst

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################################
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Demos
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################################
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.. contents::
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:depth: 2
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:backlinks: entry
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-------------------
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***********************************************
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Trying the Highcharts for Python Toolkit
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***********************************************
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You are welcome to try the **Highcharts for Python** toolkit at your convenience and at no cost.
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You can do so by installing the libraries you need very simple:
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.. tabs::
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.. tab:: Highcharts Core for Python
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To install **Highcharts Core for Python**, just execute:
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.. code-block:: bash
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$ pip install highcharts-core
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.. tab:: Highcharts Stock for Python
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To install **Highcharts Stock for Python**, just execute:
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.. code-block:: bash
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$ pip install highcharts-stock
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.. tab:: Highcharts Maps for Python
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To install **Highcharts Maps for Python**, just execute:
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.. code-block:: bash
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$ pip install highcharts-maps
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.. tab:: Highcharts Gantt for Python
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To install **Highcharts Gantt for Python**, just execute:
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.. code-block:: bash
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$ pip install highcharts-gantt
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If you are evaluating the **Highcharts for Python Toolkit**, you are welcome
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to install this library and use it free of charge.
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However, if you are using it for professional purposes - either to use
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Highcharts for your work, or to build an application that integrates the library -
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then you have to pay for both Highcharts Core (JS) itself *and* for your right
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to use the **Highcharts for Python Toolkit**.
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You can purchase licenses for both from Highsoft A/S at:
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`https://shop.highcharts.com/ <https://shop.highcharts.com>`__.
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---------------
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********************************************************
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Demonstrating the Highcharts for Python Toolkit
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********************************************************
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We have prepared an extensive set of demos showcasing much of the code and
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functionality of the **Highcharts for Python** toolkit. To see the demos in action,
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we recommend that you click the following badge:
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.. image:: https://mybinder.org/badge_logo.svg
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:target: https://mybinder.org/v2/gh/highcharts-for-python/highcharts-for-python-demos/HEAD
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:alt: Binder: Highcharts for Python Demos
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This will clone the `Highcharts for Python Demos <https://github.com/highcharts-for-python-demos>`__
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source repository within a Docker image, and launch `Jupyter Lab <https://jupyter.org>`__ within that
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Docker container. This will then let you browse, edit, and run any of the Jupyter Notebooks contained
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within the `Highcharts for Python Demos <https://github.com/highcharts-for-python-demos>`__ repo.
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How the Demos are Organized
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==============================
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.. sidebar::
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You should be aware that the demos use a variety of Highcharts for Python features, including various
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ways of de-serializing chart configurations. Some might load data from files, others might load them
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from ``snake_case`` :class:`dict <python:dict>`, others from ``camelCase`` Javascript Object Literal
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Notation :class:`str <python:str>` values, and more.
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To see the full flexibility that the toolkit provides, we recommend reviewing several of the demos.
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Once you have launched the Binder, you can browse the folders and Notebooks in the Jupyter Lab
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environment. You'll notice that the Jupyter Lab environment has one folder for each of the core
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Highcharts for Python libraries, respectively: ``highcharts-core``, ``highcharts-stock``,
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``highcharts-maps``, and ``highcharts-gantt``. Within each of these folders, you will find sub-folders
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labeled in a way to describe their contents.
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For example:
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* the sub-folder ``line-charts`` would contain various Notebooks that demonstrate the
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generation of various Line Charts using the **Highcharts for Python** toolkit
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* the sub-folder ``python-features`` would contain various Notebooks that demonstrate
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the use of Python-specific features like the ``.from_pandas()`` convenience methods
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* etc.
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Navigating the demos should be fairly intuitive - just pay attention to the folder names and the filenames of the Jupyter Notebooks.
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Executing a Demo
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===================
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Once you are reviewing the demos, you can run a demo either by stepping through the cells in the Notebook, or
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by running all the cells in sequence.
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------------------
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*******************************
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Running Demos Locally
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*******************************
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.. info::
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You can run the demos locally by following instructions in the
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`Highcharts for Python Demos <https://github.com/highcharts-for-python-demos>`__ Github repo's README.

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