Change to https

README.md

@@ -7,7 +7,7 @@
 | Latest dev release | [![Github tag](https://img.shields.io/github/tag/holoviz/hvplot.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/hvplot/tags) [![dev-site](https://img.shields.io/website-up-down-green-red/https/pyviz-dev.github.io/hvplot.svg?label=dev%20website)](https://pyviz-dev.github.io/hvplot/) |
 | 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) |
 | Python | [![Python support](https://img.shields.io/pypi/pyversions/hvplot.svg)](https://pypi.org/project/hvplot/) |
-| Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/hvplot/gh-pages.svg)](https://github.com/holoviz/hvplot/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/hvplot.holoviz.org.svg)](http://hvplot.holoviz.org) |
+| Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/hvplot/gh-pages.svg)](https://github.com/holoviz/hvplot/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/http/hvplot.holoviz.org.svg)](https://hvplot.holoviz.org) |
 | Binder | [![Binder](https://img.shields.io/badge/launch%20v0.8.0-binder-579aca.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFkAAABZCAMAAABi1XidAAAB8lBMVEX///9XmsrmZYH1olJXmsr1olJXmsrmZYH1olJXmsr1olJXmsrmZYH1olL1olJXmsr1olJXmsrmZYH1olL1olJXmsrmZYH1olJXmsr1olL1olJXmsrmZYH1olL1olJXmsrmZYH1olL1olL0nFf1olJXmsrmZYH1olJXmsq8dZb1olJXmsrmZYH1olJXmspXmspXmsr1olL1olJXmsrmZYH1olJXmsr1olL1olJXmsrmZYH1olL1olLeaIVXmsrmZYH1olL1olL1olJXmsrmZYH1olLna31Xmsr1olJXmsr1olJXmsrmZYH1olLqoVr1olJXmsr1olJXmsrmZYH1olL1olKkfaPobXvviGabgadXmsqThKuofKHmZ4Dobnr1olJXmsr1olJXmspXmsr1olJXmsrfZ4TuhWn1olL1olJXmsqBi7X1olJXmspZmslbmMhbmsdemsVfl8ZgmsNim8Jpk8F0m7R4m7F5nLB6jbh7jbiDirOEibOGnKaMhq+PnaCVg6qWg6qegKaff6WhnpKofKGtnomxeZy3noG6dZi+n3vCcpPDcpPGn3bLb4/Mb47UbIrVa4rYoGjdaIbeaIXhoWHmZYHobXvpcHjqdHXreHLroVrsfG/uhGnuh2bwj2Hxk17yl1vzmljzm1j0nlX1olL3AJXWAAAAbXRSTlMAEBAQHx8gICAuLjAwMDw9PUBAQEpQUFBXV1hgYGBkcHBwcXl8gICAgoiIkJCQlJicnJ2goKCmqK+wsLC4usDAwMjP0NDQ1NbW3Nzg4ODi5+3v8PDw8/T09PX29vb39/f5+fr7+/z8/Pz9/v7+zczCxgAABC5JREFUeAHN1ul3k0UUBvCb1CTVpmpaitAGSLSpSuKCLWpbTKNJFGlcSMAFF63iUmRccNG6gLbuxkXU66JAUef/9LSpmXnyLr3T5AO/rzl5zj137p136BISy44fKJXuGN/d19PUfYeO67Znqtf2KH33Id1psXoFdW30sPZ1sMvs2D060AHqws4FHeJojLZqnw53cmfvg+XR8mC0OEjuxrXEkX5ydeVJLVIlV0e10PXk5k7dYeHu7Cj1j+49uKg7uLU61tGLw1lq27ugQYlclHC4bgv7VQ+TAyj5Zc/UjsPvs1sd5cWryWObtvWT2EPa4rtnWW3JkpjggEpbOsPr7F7EyNewtpBIslA7p43HCsnwooXTEc3UmPmCNn5lrqTJxy6nRmcavGZVt/3Da2pD5NHvsOHJCrdc1G2r3DITpU7yic7w/7Rxnjc0kt5GC4djiv2Sz3Fb2iEZg41/ddsFDoyuYrIkmFehz0HR2thPgQqMyQYb2OtB0WxsZ3BeG3+wpRb1vzl2UYBog8FfGhttFKjtAclnZYrRo9ryG9uG/FZQU4AEg8ZE9LjGMzTmqKXPLnlWVnIlQQTvxJf8ip7VgjZjyVPrjw1te5otM7RmP7xm+sK2Gv9I8Gi++BRbEkR9EBw8zRUcKxwp73xkaLiqQb+kGduJTNHG72zcW9LoJgqQxpP3/Tj//c3yB0tqzaml05/+orHLksVO+95kX7/7qgJvnjlrfr2Ggsyx0eoy9uPzN5SPd86aXggOsEKW2Prz7du3VID3/tzs/sSRs2w7ovVHKtjrX2pd7ZMlTxAYfBAL9jiDwfLkq55Tm7ifhMlTGPyCAs7RFRhn47JnlcB9RM5T97ASuZXIcVNuUDIndpDbdsfrqsOppeXl5Y+XVKdjFCTh+zGaVuj0d9zy05PPK3QzBamxdwtTCrzyg/2Rvf2EstUjordGwa/kx9mSJLr8mLLtCW8HHGJc2R5hS219IiF6PnTusOqcMl57gm0Z8kanKMAQg0qSyuZfn7zItsbGyO9QlnxY0eCuD1XL2ys/MsrQhltE7Ug0uFOzufJFE2PxBo/YAx8XPPdDwWN0MrDRYIZF0mSMKCNHgaIVFoBbNoLJ7tEQDKxGF0kcLQimojCZopv0OkNOyWCCg9XMVAi7ARJzQdM2QUh0gmBozjc3Skg6dSBRqDGYSUOu66Zg+I2fNZs/M3/f/Grl/XnyF1Gw3VKCez0PN5IUfFLqvgUN4C0qNqYs5YhPL+aVZYDE4IpUk57oSFnJm4FyCqqOE0jhY2SMyLFoo56zyo6becOS5UVDdj7Vih0zp+tcMhwRpBeLyqtIjlJKAIZSbI8SGSF3k0pA3mR5tHuwPFoa7N7reoq2bqCsAk1HqCu5uvI1n6JuRXI+S1Mco54YmYTwcn6Aeic+kssXi8XpXC4V3t7/ADuTNKaQJdScAAAAAElFTkSuQmCC)](https://mybinder.org/v2/gh/holoviz/hvplot/v0.8.0?urlpath=lab/tree/examples) |
 | Support | [![Discourse](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.holoviz.org)](https://discourse.holoviz.org/c/hvplot/8) |
 

doc/index.md

@@ -181,7 +181,7 @@ align: center
 
 ::::
 
-`.hvplot()` sources its power in the [HoloViz](https://holoviz.org/) ecosystem. With [HoloViews](https://holoviews.org/) you get the ability to easily layout and overlay plots, with [Panel](https://panel.holoviz.org) you can get more interactive control of your plots with widgets, with [DataShader](https://datashader.org/) you can visualize and interactively explore very large data, and with [GeoViews](http://geoviews.org/) you can create geographic plots.
+`.hvplot()` sources its power in the [HoloViz](https://holoviz.org/) ecosystem. With [HoloViews](https://holoviews.org/) you get the ability to easily layout and overlay plots, with [Panel](https://panel.holoviz.org) you can get more interactive control of your plots with widgets, with [DataShader](https://datashader.org/) you can visualize and interactively explore very large data, and with [GeoViews](https://geoviews.org/) you can create geographic plots.
 
 ::::{tab-set}
 

examples/datasets.yaml

@@ -18,7 +18,7 @@ sources:
     args:
       urlpath: '{{ CATALOG_DIR }}/data/crime.csv'
     metadata:
-      url: https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm
+      url: https://web.archive.org/web/20201031163816/https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm
       plots:
         example:
           kind: line

examples/getting_started/hvplot.ipynb

@@ -8,14 +8,14 @@
     "\n",
     "* [Pandas](https://pandas.pydata.org): DataFrame, Series (columnar/tabular data)\n",
     "* [Rapids cuDF](https://docs.rapids.ai/api/cudf/stable/): GPU DataFrame, Series (columnar/tabular data)\n",
-    "* [Dask](https://dask.pydata.org): DataFrame, Series (distributed/out of core arrays and columnar data)\n",
+    "* [Dask](https://www.dask.org): DataFrame, Series (distributed/out of core arrays and columnar data)\n",
     "* [XArray](https://xarray.pydata.org): Dataset, DataArray (labelled multidimensional arrays)\n",
     "* [Streamz](https://streamz.readthedocs.io): DataFrame(s), Series(s) (streaming columnar data)\n",
     "* [Intake](https://github.com/ContinuumIO/intake): DataSource (data catalogues)\n",
     "* [GeoPandas](https://geopandas.org): GeoDataFrame (geometry data)\n",
     "* [NetworkX](https://networkx.github.io/documentation/stable/): Graph (network graphs)\n",
     "\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",
+    "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](https://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](https://bokeh.pydata.org) and [HoloViews](https://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."
    ]
@@ -26,7 +26,7 @@
    "source": [
     "## Basic usage\n",
     "\n",
-    "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. Let's first create some data."
+    "hvPlot provides an alternative for the static plotting API provided by [Pandas](https://pandas.pydata.org) and other libraries, with by default an interactive [Bokeh](https://bokeh.pydata.org)-based plotting API that supports panning, zooming, hovering, and clickable/selectable legends. Let's first create some data."
    ]
   },
   {
@@ -115,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When used with [streamz](http://streamz.readthedocs.io) DataFrames, hvPlot can very easily plot streaming data to get a [live updating plot](../user_guide/Streaming.html):"
+    "When used with [streamz](https://streamz.readthedocs.io) DataFrames, hvPlot can very easily plot streaming data to get a [live updating plot](../user_guide/Streaming.html):"
    ]
   },
   {
@@ -244,7 +244,7 @@
    "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",
+    "hvPlot is designed to work well in and outside the Jupyter notebook, and thanks to built-in [Datashader](https://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>"
    ]

examples/user_guide/Customization.ipynb

@@ -231,7 +231,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In general, the objects returned by hvPlot are regular HoloViews objects, which can be overlaid, laid out, composed and customized like all other HoloViews objects.  The [HoloViews](http://holoviews.org) website explains all the functionality available, but what's on this hvPlot website should be enough to get you up and running for typical usage.  "
+    "In general, the objects returned by hvPlot are regular HoloViews objects, which can be overlaid, laid out, composed and customized like all other HoloViews objects.  The [HoloViews](https://holoviews.org) website explains all the functionality available, but what's on this hvPlot website should be enough to get you up and running for typical usage.  "
    ]
   }
  ],

examples/user_guide/Geographic_Data.ipynb

@@ -67,7 +67,7 @@
    "source": [
     "### Declaring a CRS\n",
     "\n",
-    "To declare a geographic plot we have to supply a ``cartopy.crs.CRS`` (or coordinate reference system).  Coordinate reference systems are described in the [GeoViews documentation](http://geoviews.org/user_guide/Projections.html) and the full list of available CRSs is in the [cartopy documentation](https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html). "
+    "To declare a geographic plot we have to supply a ``cartopy.crs.CRS`` (or coordinate reference system).  Coordinate reference systems are described in the [GeoViews documentation](https://geoviews.org/user_guide/Projections.html) and the full list of available CRSs is in the [cartopy documentation](https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html). "
    ]
   },
   {
@@ -247,7 +247,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see, hvPlot makes it simple to work with geographic data visually.  For more complex plot types and additional details, see the [GeoViews](http://geoviews.org) documentation."
+    "As you can see, hvPlot makes it simple to work with geographic data visually.  For more complex plot types and additional details, see the [GeoViews](https://geoviews.org) documentation."
    ]
   },
   {

examples/user_guide/Gridded_Data.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "hvPlot provides one API to explore data of many different types. Previous sections have exclusively worked with tabular data stored in pandas (or pandas-like) DataFrames. The other most common type of data are n-dimensional arrays. hvPlot aims to eventually support different array libraries but for now focuses on [xarray](http://xarray.pydata.org/en/stable/). XArray provides a convenient and very powerful wrapper to label the axis and coordinates of multi-dimensional (n-D) arrays. This user guide will cover how to leverage ``xarray`` and ``hvplot`` to visualize and explore data of different dimensionality ranging from simple 1D data, to 2D image-like data, to multi-dimensional cubes of data.\n",
+    "hvPlot provides one API to explore data of many different types. Previous sections have exclusively worked with tabular data stored in pandas (or pandas-like) DataFrames. The other most common type of data are n-dimensional arrays. hvPlot aims to eventually support different array libraries but for now focuses on [xarray](https://xarray.pydata.org/en/stable/). XArray provides a convenient and very powerful wrapper to label the axis and coordinates of multi-dimensional (n-D) arrays. This user guide will cover how to leverage ``xarray`` and ``hvplot`` to visualize and explore data of different dimensionality ranging from simple 1D data, to 2D image-like data, to multi-dimensional cubes of data.\n",
     "\n",
     "For these examples we’ll use the North American air temperature dataset:"
    ]
@@ -263,7 +263,7 @@
    "source": [
     "## Rasterizing\n",
     "\n",
-    "If you are plotting a large amount of data at once, you can consider using the hvPlot interface to [Datashader](http://datashader.org), which can be enabled simply by setting `rasterize=True`.\n",
+    "If you are plotting a large amount of data at once, you can consider using the hvPlot interface to [Datashader](https://datashader.org), which can be enabled simply by setting `rasterize=True`.\n",
     "\n",
     "Note that by declaring that the data should not be grouped by another coordinate variable, i.e. by setting `groupby=[]`, we can plot all the datapoints, showing us the spread of air temperatures in the dataset:"
    ]

examples/user_guide/Introduction.ipynb

@@ -8,14 +8,14 @@
     "\n",
     "* [Pandas](https://pandas.pydata.org): DataFrame, Series (columnar/tabular data)\n",
     "* [Rapids cuDF](https://docs.rapids.ai/api/cudf/stable/): GPU DataFrame, Series (columnar/tabular data)\n",
-    "* [Dask](https://dask.pydata.org): DataFrame, Series (distributed/out of core arrays and columnar data)\n",
+    "* [Dask](https://www.dask.org): DataFrame, Series (distributed/out of core arrays and columnar data)\n",
     "* [XArray](https://xarray.pydata.org): Dataset, DataArray (labelled multidimensional arrays)\n",
     "* [Streamz](https://streamz.readthedocs.io): DataFrame(s), Series(s) (streaming columnar data)\n",
     "* [Intake](https://github.com/ContinuumIO/intake): DataSource (data catalogues)\n",
     "* [GeoPandas](https://geopandas.org): GeoDataFrame (geometry data)\n",
     "* [NetworkX](https://networkx.github.io/documentation/stable/): Graph (network graphs)\n",
     "\n",
-    "Many 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 means that users miss out the benefits of modern, interactive plotting libraries for the web like [Bokeh](http://bokeh.pydata.org) and [HoloViews](http://holoviews.org).\n",
+    "Many 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](https://matplotlib.org), which provides a solid foundation, but means that users miss out the benefits of modern, interactive plotting libraries for the web like [Bokeh](https://bokeh.pydata.org) and [HoloViews](https://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 formats mentioned above.\n",
     "\n",
@@ -101,7 +101,7 @@
    "source": [
     "## .hvplot()\n",
     "\n",
-    "If we instead change `%matplotlib inline` to `import hvplot.pandas` and use the ``df.hvplot`` method, it will now display an interactively explorable [Bokeh](http://bokeh.pydata.org) plot with panning, zooming, hovering, and clickable/selectable legends:"
+    "If we instead change `%matplotlib inline` to `import hvplot.pandas` and use the ``df.hvplot`` method, it will now display an interactively explorable [Bokeh](https://bokeh.pydata.org) plot with panning, zooming, hovering, and clickable/selectable legends:"
    ]
   },
   {
@@ -148,7 +148,7 @@
    "source": [
     "## Switching the plotting extension to Matplotlib or Plotly\n",
     "\n",
-    "While the default plotting extension of hvPlot is [Bokeh](http://bokeh.pydata.org), it is possible to load either Matplotlib or Plotly with `.extension()` and later switch from a plotting library to another with `.output()`. More information about working with multiple plotting backends can be found in the [plotting extensions guide](Plotting_Extensions.ipynb)."
+    "While the default plotting extension of hvPlot is [Bokeh](https://bokeh.pydata.org), it is possible to load either Matplotlib or Plotly with `.extension()` and later switch from a plotting library to another with `.output()`. More information about working with multiple plotting backends can be found in the [plotting extensions guide](Plotting_Extensions.ipynb)."
    ]
   },
   {

examples/user_guide/NetworkX.ipynb

@@ -134,7 +134,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In addition to being able to set scalar style values hvPlot also supports the HoloViews concept of [style mapping](http://holoviews.org/user_guide/Style_Mapping.html#styling-mapping), which uses so called ``dim`` transforms to map attributes of the graph nodes and edges to vary the visual attributes of the plot. For example we might construct a graph with edge weights and node sizes as attributes. The plotting function will extract these attributes which means they can be used to scale visual properties of the plot such as the ``edge_width``, ``edge_color`` or ``node_size``:"
+    "In addition to being able to set scalar style values hvPlot also supports the HoloViews concept of [style mapping](https://holoviews.org/user_guide/Style_Mapping.html#styling-mapping), which uses so called ``dim`` transforms to map attributes of the graph nodes and edges to vary the visual attributes of the plot. For example we might construct a graph with edge weights and node sizes as attributes. The plotting function will extract these attributes which means they can be used to scale visual properties of the plot such as the ``edge_width``, ``edge_color`` or ``node_size``:"
    ]
   },
   {
@@ -380,7 +380,7 @@
    "source": [
     "### Circular Tree\n",
     "\n",
-    "URL: https://networkx.github.io/documentation/stable/auto_examples/drawing/plot_circular_tree.html"
+    "URL: https://networkx.org/documentation/stable/auto_examples/graphviz_layout/plot_circular_tree.html"
    ]
   },
   {
@@ -674,7 +674,7 @@
     "\n",
     "This example illustrates the sudden appearance of a giant connected component in a binomial random graph.\n",
     "\n",
-    "https://networkx.github.io/documentation/stable/auto_examples/drawing/plot_giant_component.html"
+    "https://networkx.org/documentation/stable/auto_examples/graphviz_layout/plot_giant_component.html"
    ]
   },
   {

examples/user_guide/Pandas_API.ipynb

@@ -943,7 +943,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Formatters can be set using format strings, by declaring bokeh TickFormatters, or using custom functions. See [HoloViews Tick Docs](http://holoviews.org/user_guide/Customizing_Plots.html#Axis-ticks) for more information."
+    "Formatters can be set using format strings, by declaring bokeh TickFormatters, or using custom functions. See [HoloViews Tick Docs](https://holoviews.org/user_guide/Customizing_Plots.html#Axis-ticks) for more information."
    ]
   },
   {