Implement backend options transformer

.gitignore

@@ -118,5 +118,8 @@ hvplot/.version
 # doit
 .doit.db
 
-# vscode
-.vscode
+# IDE
+/.vscode
+
+# MacOS
+.DS_Store

doc/index.rst

@@ -12,7 +12,7 @@
    :hidden:
    :maxdepth: 2
 
-   Introduction <self>
+   Home <self>
    User Guide <user_guide/index>
    Reference Gallery <reference/index>
    Topics <topics>

doc/user_guide/index.rst

@@ -41,8 +41,14 @@ rather than Matplotlib.
 * `Introduction <Introduction.html>`_
   Introduction to hvPlot and how to start using it.
 
-* `Plotting <Plotting.html>`_
-  Overview of plotting your data with hvPlot.
+* `Plotting with Bokeh <Plotting.html>`_
+  Overview of plotting your data with hvPlot and Bokeh.
+
+* `Plotting with Matplotlib <Plotting_with_Matplotlib.html>`_
+  Overview of plotting your data with hvPlot and Matplotib.
+
+* `Plotting with Plotly <Plotting_with_Plotly.html>`_
+  Overview of plotting your data with hvPlot and Plotly.
 
 * `Customization <Customization.html>`_
   Listing of available options to customize plots.
@@ -53,6 +59,9 @@ rather than Matplotlib.
 * `Widgets <Widgets.html>`_
   Adding and customizing interactivity using Panel widgets.
 
+* `Plotting Extensions <Plotting_Extensions.html>`_
+  Changing the plotting extension.
+
 * `Viewing <Viewing.html>`_
   Displaying and saving plots in the notebook, at the command prompt, or in scripts.
 
@@ -86,10 +95,13 @@ rather than Matplotlib.
     :maxdepth: 2
 
     Introduction <Introduction>
-    Plotting <Plotting>
+    Plotting with Bokeh <Plotting>
+    Plotting with Matplotlib <Plotting_with_Matplotlib>
+    Plotting with Plotly <Plotting_with_Plotly>
     Customization <Customization>
     Interactive <Interactive>
     Widgets <Widgets>
+    Plotting Extensions <Plotting_Extensions>
     Viewing <Viewing>
     Subplots <Subplots>
     Streaming <Streaming>

examples/homepage.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "**A high-level plotting API for the PyData ecosystem built on HoloViews.**\n",
     "\n",
-    "<img src=\"./assets/diagram.png\"  width=\"70%\"></img>\n",
+    "<img src=\"./assets/diagram.svg\"  width=\"70%\"></img>\n",
     "\n",
     "The PyData ecosystem has a number of core Python data containers that allow users to work with a wide array of datatypes, including:\n",
     "\n",
@@ -21,7 +21,7 @@
     "\n",
     "Several of these libraries have the concept of a high-level plotting API that lets a user generate common plot types very easily. The native plotting APIs are generally built on [Matplotlib](http://matplotlib.org), which provides a solid foundation, but it means that users miss out on the benefits of modern, interactive plotting libraries built for the web like [Bokeh](http://bokeh.pydata.org) and [HoloViews](http://holoviews.org).\n",
     "\n",
-    "hvPlot provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data in all the abovementioned formats. hvPlot can integrate neatly with the individual libraries if an extension mechanism for the native plot APIs is offered, or it can be used as a standalone component. To get started jump straight into the [installation instructions](#installation) and check out the current functionality in the [User Guide.](user_guide/index.html)"
+    "**hvPlot** provides a high-level plotting API built on HoloViews that provides a general and consistent API for plotting data in all the abovementioned formats. hvPlot can integrate neatly with the individual libraries if an extension mechanism for the native plot APIs is offered, or it can be used as a standalone component. To get started jump straight into the [installation instructions](#installation) and check out the current functionality in the [User Guide.](user_guide/index.html)"
    ]
   },
   {
@@ -30,7 +30,7 @@
    "source": [
     "## Usage\n",
     "\n",
-    "hvPlot provides an alternative for the static plotting API provided by [Pandas](http://pandas.pydata.org) and other libraries, with an interactive [Bokeh](http://bokeh.pydata.org)-based plotting API that supports panning, zooming, hovering, and clickable/selectable legends:"
+    "hvPlot provides an alternative for the static plotting API provided by [Pandas](http://pandas.pydata.org) and other libraries, with by default an interactive [Bokeh](http://bokeh.pydata.org)-based plotting API that supports panning, zooming, hovering, and clickable/selectable legends:"
    ]
   },
   {
@@ -160,6 +160,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "hvPlots will show widgets like the \"Time\" slider here whenever your data is indexed by dimensions that are not mapped onto the plot axes, allowing you to explore complex datasets much more easily than with the default plotting support.\n",
+    "\n",
     "Lastly, hvPlot also provides drop-in replacements for the NetworkX plotting functions, making it trivial to generate interactive plots of [network graphs](user_guide/NetworkX.html):"
    ]
   },
@@ -181,12 +183,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "hvPlots will show widgets like the \"Time\" slider here whenever your data is indexed by dimensions that are not mapped onto the plot axes, allowing you to explore complex datasets much more easily than with the default plotting support.\n",
-    "\n",
+    "hvPlot offers the possibility to create [Matplotlib](https://matplotlib.org/) and [Plotly](https://plotly.com/) plots. Switching the underlying plotting library/backend is achieved with the `extension` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.extension('matplotlib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "air_ds.air.isel(time=slice(0, 9, 3)).hvplot.quadmesh(\n",
+    "    'lon', 'lat', projection=proj, project=True, global_extent=True, \n",
+    "    cmap='viridis', rasterize=True, dynamic=False, coastline=True,\n",
+    "    xaxis=None, yaxis=None, width=500\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "hvPlot is designed to work well in and outside the Jupyter notebook, and thanks to built-in [Datashader](http://datashader.org) support scales easily to millions or even billions of datapoints:\n",
     "\n",
-    "<img src=\"./assets/console_server.gif\" style=\"display: table; margin: 0 auto;\" width=\"80%\"></img>\n",
-    "\n",
+    "<img src=\"./assets/console_server.gif\" style=\"display: table; margin: 0 auto;\" width=\"80%\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "For information on using hvPlot take a look at the [User Guide](user_guide/index.html) or the [announcement blog](http://blog.pyviz.org/hvplot_announcement.html).\n",
     "\n",
     "## Installation\n",
@@ -196,7 +230,7 @@
     "| Latest release | [![Github release](https://img.shields.io/github/release/holoviz/hvplot.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/hvplot/releases) [![PyPI version](https://img.shields.io/pypi/v/hvplot.svg?colorB=cc77dd)](https://pypi.python.org/pypi/hvplot) [![hvplot version](https://img.shields.io/conda/v/pyviz/hvplot.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/hvplot) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/hvplot.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/hvplot) [![defaults version](https://img.shields.io/conda/v/anaconda/hvplot.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/hvplot) |\n",
     "| Python | [![Python support](https://img.shields.io/pypi/pyversions/hvplot.svg)](https://pypi.org/project/hvplot/) |\n",
     "\n",
-    "hvPlot supports Python 3.6, 3.7, 3.8 and 3.9 on Linux, Windows, or Mac.  The recommended way to install hvPlot is using the [conda](https://conda.io/en/latest/) command provided by [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html):\n",
+    "hvPlot supports Python 3.6, 3.7, 3.8, 3.9 and 3.10 on Linux, Windows, or Mac.  The recommended way to install hvPlot is using the [conda](https://conda.io/en/latest/) command provided by [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html):\n",
     "\n",
     "    conda install -c pyviz hvplot\n",
     "\n",

examples/reference/pandas/andrewscurves.ipynb

@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas  # noqa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Andrews curves provides a mechanism for visualising clusters of multivariate data.\n",
+    "\n",
+    "Andrews curves have the functional form:\n",
+    "\n",
+    "    f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) + x_4 sin(2t) + x_5 cos(2t) + ...\n",
+    "\n",
+    "Where *x* coefficients correspond to the values of each dimension and *t* is\n",
+    "linearly spaced between *-pi* and *+pi*. Each row of frame then corresponds to\n",
+    "a single curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bokeh.sampledata import iris\n",
+    "\n",
+    "iris = iris.flowers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.plotting.andrews_curves(\n",
+    "    iris,\n",
+    "    class_column='species',\n",
+    "    samples=20,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/reference/pandas/area.ipynb

@@ -19,9 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from bokeh.sampledata.degrees import data\n",

examples/reference/pandas/lagplot.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.pandas  # noqa\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lag plots are most commonly used to look for patterns in time series data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the following time series:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(5)\n",
+    "x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))\n",
+    "s = pd.Series(x, name='Time series')\n",
+    "\n",
+    "s.hvplot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A lag plot with `lag=1` returns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hvplot.plotting.lag_plot(s, lag=1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/reference/pandas/ohlc.ipynb

@@ -93,29 +93,9 @@
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,