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Doc improvements before the release (#1553)
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doc/_static/custom.css

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border: 0px;
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vertical-align: top;
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}
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.getting-started-button {
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border-radius: 6px !important;
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padding: 0.7em 3em !important;
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font-size: 1.2em;
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}
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{# Added because of https://github.com/pydata/pydata-sphinx-theme/issues/2088 #}
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{# Displays a link to the .rst source of the current page. #}
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{% if show_source and has_source and sourcename %}
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<div class="tocsection sourcelink">
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<a href="{{ pathto('_sources/' + sourcename, true)|e }}">
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<i class="fa-solid fa-file-lines"></i> {{ _("Show Source") }}
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</a>
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</div>
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{% endif %}

doc/about.md

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doc/conf.py

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import os
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import param
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import pydata_sphinx_theme
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param.parameterized.docstring_signature = False
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param.parameterized.docstring_describe_params = False
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html_static_path += ['_static'] # noqa
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if pydata_sphinx_theme.__version__ == '0.16.1':
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# See https://github.com/pydata/pydata-sphinx-theme/issues/2088
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templates_path.append('_static/patch_templates') # noqa
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html_css_files += ['custom.css'] # noqa
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html_js_files = [
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html_theme_options.update( # noqa
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{
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'use_edit_page_button': True,
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'github_url': 'https://github.com/holoviz/hvplot',
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'icon_links': [
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{
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}
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)
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# Without this .txt is appended to the files
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html_sourcelink_suffix = ''
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html_theme = 'pydata_sphinx_theme'
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html_logo = '_static/logo_horizontal.svg'
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html_favicon = '_static/favicon.ico'
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'user_guide/pandas_api': 'ref/api_compatibility/pandas/pandas_api',
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}
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html_extra_path = ['topics.html']
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html_context.update( # noqa
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{
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'last_release': f'v{release}',
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'github_user': 'holoviz',
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'github_repo': 'panel',
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'default_mode': 'light',
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# Useful for the edit button
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'github_user': 'holoviz',
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'github_repo': 'hvplot',
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'github_version': 'main',
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'doc_path': 'doc',
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}
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)
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# linkcheck
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linkcheck_ignore = [r'https://github.com/holoviz/hvplot/pull/\d+']
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# mystnb
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nb_execution_excludepatterns = [
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'user_guide/Streaming.ipynb',

doc/developer_guide.md

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doc/index.md

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doc/ref/api/hvplot_api/hvplot.hvPlot.explorer.ipynb

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doc/ref/api/index.md

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doc/ref/api_compatibility/pandas/Pandas_API.ipynb

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"metadata": {},
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"source": [
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":::{note}\n",
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"In hvPlot, 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.\n",
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"In hvPlot, 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.\n",
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":::"
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]
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},

doc/ref/data_libraries.ipynb

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"id": "7c01a461-5c22-48a3-b5e1-a2a3716c5bf4",
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"metadata": {},
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"source": [
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"`.hvplot()` supports [Intake](https://github.com/ContinuumIO/intake) `DataSource` objects."
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"`.hvplot()` supports [Intake](https://github.com/intake/intake) `DataSource` objects."
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]
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},
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{

doc/reference/tabular/heatmap.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If the value for each section of the heatmap is pre-computed, then use `x='index'` and `y='columns'` to plot those values. Note to see how to make this same plot in bokeh, see the [bokeh docs](https://docs.bokeh.org/en/latest/docs/user_guide/categorical.html#heat-maps)."
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"If the value for each section of the heatmap is pre-computed, then use `x='index'` and `y='columns'` to plot those values. Note to see how to make this same plot in bokeh, see the [bokeh docs](https://docs.bokeh.org/en/latest/docs/user_guide/topics/categorical.html#categorical-heatmaps)."
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]
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},
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{

doc/topics.html

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<html>
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<head>
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<noscript>
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<meta http-equiv="refresh" content="0; url=https://examples.holoviz.org"/>
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</noscript>
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</head>
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<body>
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<script>
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window.location.href = 'https://examples.holoviz.org' + (window.location.search || '') + (window.location.hash || '');
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</script>
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<p>You should have been redirected.</p>
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<a href="https://examples.holoviz.org">If not, click here to continue.</a>
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</body>
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</html>

doc/topics.md

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doc/tutorials/getting_started.ipynb

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"source": [
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"## Overview\n",
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"\n",
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"The core functionality provided by hvPlot is a **simple and high-level plotting interface** (API), modeled on [Pandas](https://pandas.pydata.org)'s `.plot` API and extended in various ways leveraging capabilities offered by the packages of the [HoloViz](https://holoviz.org) ecosystem, most notably [HoloViews](https://holoviews.org/). hvPlot can generate interactive plots with either [Bokeh](https://www.bokeh.org) (default) or [Plotly](https://plotly.com/python/), or static plots with [Matplotlib](https://matplotlib.org). hvPlot supports many data libraries of the Python ecosystem such as `pandas`, `xarray`, `dask` etc."
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"The core functionality provided by hvPlot is a **simple and high-level plotting interface** (API), modeled on [Pandas](https://pandas.pydata.org)'s `.plot` API and extended in various ways leveraging capabilities offered by the packages of the [HoloViz](https://holoviz.org) ecosystem, most notably [HoloViews](https://holoviews.org/). hvPlot can generate interactive plots with either [Bokeh](https://bokeh.org) (default) or [Plotly](https://plotly.com/python/), or static plots with [Matplotlib](https://matplotlib.org). hvPlot supports many data libraries of the Python ecosystem such as `pandas`, `xarray`, `dask` etc."
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]
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},
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{

doc/user_guide/Gridded_Data.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"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",
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"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://docs.xarray.dev/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",
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"\n",
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"For these examples we’ll use the North American air temperature dataset:"
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]

doc/user_guide/Introduction.ipynb

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"* [GeoPandas](https://geopandas.org): GeoDataFrame (geometry data)\n",
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"* [NetworkX](https://networkx.github.io/documentation/stable/): Graph (network graphs)\n",
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"\n",
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"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",
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"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://docs.bokeh.org) and [HoloViews](https://holoviews.org).\n",
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"\n",
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"**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",
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"\n",

doc/user_guide/Plotting.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As we learned the hvPlot API closely mirrors the [Pandas plotting API](https://pandas.pydata.org/pandas-docs/stable/visualization.html), but instead of generating static images when used in a notebook, it uses HoloViews to generate either static or dynamically streaming Bokeh plots. Static plots can be used in any context, while streaming plots require a live [Jupyter notebook](https://jupyter.org), a deployed [Bokeh Server app](https://bokeh.pydata.org/en/latest/docs/user_guide/server.html), or a deployed [Panel](https://panel.holoviz.org) app.\n",
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"As we learned the hvPlot API closely mirrors the [Pandas plotting API](https://pandas.pydata.org/pandas-docs/stable/visualization.html), but instead of generating static images when used in a notebook, it uses HoloViews to generate either static or dynamically streaming Bokeh plots. Static plots can be used in any context, while streaming plots require a live [Jupyter notebook](https://jupyter.org), a deployed [Bokeh Server app](https://docs.bokeh.org/en/latest/docs/user_guide/server.html), or a deployed [Panel](https://panel.holoviz.org) app.\n",
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"\n",
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"HoloViews provides an extensive, very rich set of objects along with a powerful set of operations to apply, as you can find out in the [HoloViews User Guide](https://holoviews.org/user_guide/index.html). But here we will focus on the most essential mechanisms needed to make your data visualizable, without having to worry about the mechanics going on behind the scenes.\n",
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"\n",

doc/user_guide/Plotting_with_Matplotlib.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As we learned the hvPlot API closely mirrors the [Pandas plotting API](https://pandas.pydata.org/pandas-docs/stable/visualization.html), but instead of generating static images when used in a notebook, it uses HoloViews to generate either static or dynamically streaming Bokeh plots. Static plots can be used in any context, while streaming plots require a live [Jupyter notebook](https://jupyter.org), a deployed [Bokeh Server app](https://bokeh.pydata.org/en/latest/docs/user_guide/server.html), or a deployed [Panel](https://panel.pyviz.org) app.\n",
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"As we learned the hvPlot API closely mirrors the [Pandas plotting API](https://pandas.pydata.org/pandas-docs/stable/visualization.html), but instead of generating static images when used in a notebook, it uses HoloViews to generate either static or dynamically streaming Bokeh plots. Static plots can be used in any context, while streaming plots require a live [Jupyter notebook](https://jupyter.org), a deployed [Bokeh Server app](https://docs.bokeh.org/en/latest/docs/user_guide/server.html), or a deployed [Panel](https://panel.holoviz.org) app.\n",
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"\n",
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"HoloViews provides an extensive, very rich set of objects along with a powerful set of operations to apply, as you can find out in the [HoloViews User Guide](https://holoviews.org/user_guide/index.html). But here we will focus on the most essential mechanisms needed to make your data visualizable, without having to worry about the mechanics going on behind the scenes.\n",
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"\n",

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