FIX-#3624: Reformat modin readthedocs theme to look closer to pandas. (…

…#3726) Signed-off-by: jeffreykennethli <jkli@ponder.io>
modin-project · Dec 1, 2021 · cc96f5d · cc96f5d
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diff --git a/docs/experimental_features/index.rst → docs/advanced_usage/index.rst b/docs/experimental_features/index.rst → docs/advanced_usage/index.rst
@@ -1,6 +1,21 @@
-Overview
-========
-Modin aims to not only optimize Pandas, but also provide a comprehensive, 
+Advanced Usage
+==============
+
+.. toctree::
+   :titlesonly:
+   :hidden:
+
+   spreadsheets_api
+   progress_bar
+   modin_sql
+   modin_xgboost
+   modin_in_the_cloud
+
+.. meta::
+    :description lang=en:
+        Description of Modin's advanced features.
+
+Modin aims to not only optimize pandas, but also provide a comprehensive, 
 integrated toolkit for data scientists. We are actively developing data science tools 
 such as DataFrame - spreadsheet integration, DataFrame algebra, progress bars, SQL queries
 on DataFrames, and more. Join the `Discourse`_ for the latest updates!
@@ -42,7 +57,7 @@ Read about Modin Dataframe support for SQL queries in this recent `blog post`_.
 Distributed XGBoost on Modin
 ----------------------------
 Modin provides an implementation of distributed XGBoost machine learning algorithm on Modin DataFrames. See our
-:doc:`Distributed XGBoost on Modin documentation </experimental_features/modin_xgboost>` for details about installation and usage, as well as
+:doc:`Distributed XGBoost on Modin documentation <modin_xgboost>` for details about installation and usage, as well as
 :doc:`Modin XGBoost architecture documentation </flow/modin/experimental/xgboost>` for information about implementation and
 internal execution flow.
 

diff --git a/...erimental_features/modin_in_the_cloud.rst → docs/advanced_usage/modin_in_the_cloud.rst b/...erimental_features/modin_in_the_cloud.rst → docs/advanced_usage/modin_in_the_cloud.rst
diff --git a/docs/experimental_features/modin_sql.rst → docs/advanced_usage/modin_sql.rst b/docs/experimental_features/modin_sql.rst → docs/advanced_usage/modin_sql.rst
diff --git a/docs/experimental_features/modin_xgboost.rst → docs/advanced_usage/modin_xgboost.rst b/docs/experimental_features/modin_xgboost.rst → docs/advanced_usage/modin_xgboost.rst
@@ -10,7 +10,7 @@ Install XGBoost on Modin
 
 Modin comes with all the dependencies except ``xgboost`` package by default.
 Currently, distributed XGBoost on Modin is only supported on the Ray execution engine, therefore, see
-the :doc:`installation page </installation>` for more information on installing Modin with the Ray engine.
+the :doc:`installation page </getting_started/index>` for more information on installing Modin with the Ray engine.
 To install ``xgboost`` package you can use ``pip``:
 
 .. code-block:: bash

diff --git a/docs/experimental_features/progress_bar.rst → docs/advanced_usage/progress_bar.rst b/docs/experimental_features/progress_bar.rst → docs/advanced_usage/progress_bar.rst
diff --git a/...xperimental_features/spreadsheets_api.rst → docs/advanced_usage/spreadsheets_api.rst b/...xperimental_features/spreadsheets_api.rst → docs/advanced_usage/spreadsheets_api.rst
diff --git a/docs/comparisons/spark.rst b/docs/comparisons/spark.rst
diff --git a/docs/conf.py b/docs/conf.py
@@ -12,12 +12,15 @@
 import types
 
 import ray
+
 # stub ray.remote to be a no-op so it doesn't shadow docstrings
 def noop_decorator(*args, **kwargs):
     if len(args) == 1 and len(kwargs) == 0 and callable(args[0]):
         # This is the case where the decorator is just @ray.remote without parameters.
         return args[0]
     return lambda cls_or_func: cls_or_func
+
+
 ray.remote = noop_decorator
 
 # fake modules if they're missing
@@ -107,13 +110,15 @@ def noop_decorator(*args, **kwargs):
 # -- Options for HTML output -------------------------------------------------
 
 # Maps git branches to Sphinx themes
-default_html_theme = "sphinx_rtd_theme"
+default_html_theme = "pydata_sphinx_theme"
 current_branch = "nature"
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = "sphinx_rtd_theme"
+html_theme = "pydata_sphinx_theme"
+
+html_favicon = "img/MODIN_ver2.ico"
 
 html_logo = "img/MODIN_ver2.png"
 
@@ -125,6 +130,35 @@ def noop_decorator(*args, **kwargs):
     "sidebarwidth": 270,
     "collapse_navigation": False,
     "navigation_depth": 4,
+    "show_toc_level": 2,
+    "github_url": "https://github.com/modin-project/modin",
+    "icon_links": [
+        {
+            "name": "PyPI",
+            "url": "https://pypi.org/project/modin",
+            "icon": "fab fa-python",
+        },
+        {
+            "name": "conda-forge",
+            "url": "https://anaconda.org/conda-forge/modin",
+            "icon": "fas fa-circle-notch",
+        },
+        {
+            "name": "Join the Slack",
+            "url": "https://modin.org/slack.html",
+            "icon": "fab fa-slack",
+        },
+        {
+            "name": "Discourse",
+            "url": "https://discuss.modin.org/",
+            "icon": "fab fa-discourse",
+        },
+        {
+            "name": "Mailing List",
+            "url": "https://groups.google.com/forum/#!forum/modin-dev",
+            "icon": "fas fa-envelope-square",
+        },
+    ],
 }
 
 # Custom sidebar templates, must be a dictionary that maps document names
@@ -135,8 +169,8 @@ def noop_decorator(*args, **kwargs):
 # default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
 # 'searchbox.html']``.
 #
-html_sidebars = {
-    "**": ["globaltoc.html", "relations.html", "sourcelink.html", "searchbox.html"]
-}
+# The default pydata_sphinx_theme sidebar templates are
+# sidebar-nav-bs.html and search-field.html.
+html_sidebars = {}
 
 issues_github_path = "modin-project/modin"
diff --git a/docs/contact.rst b/docs/contact.rst
@@ -1,23 +1,32 @@
 Contact
 =======
 
+Slack
+-----
+
+Join our `Slack`_ community to connect with Modin users and contributors,
+discuss, and ask questions about all things Modin-related.
+
+Discussion forum
+----------------
+Check out our `discussion forum`_ to discuss release announcements, general
+questions, issues, use-cases, and tutorials.
+
 Mailing List
 ------------
 
-https://groups.google.com/forum/#!forum/modin-dev
-
-General questions, potential contributors, and ideas should be directed to the
+General questions, potential contributors, and ideas can be directed to the
 `developer mailing list`_. It is an open Google Group, so feel free to join anytime! If
 you are unsure about where to ask or post something, the mailing list is a good place to
 ask as well.
 
 Issues
 ------
 
-https://github.com/modin-project/modin/issues
-
-Bug reports and feature requests should be directed to the issues_ page of the Modin
+Bug reports and feature requests can be directed to the issues_ page of the Modin
 GitHub repo.
 
+.. _Slack: https://modin.org/slack.html
+.. _discussion forum: https://discuss.modin.org/
 .. _developer mailing list: https://groups.google.com/forum/#!forum/modin-dev
 .. _issues: https://github.com/modin-project/modin/issues
diff --git a/docs/developer/architecture.rst b/docs/developer/architecture.rst
@@ -47,16 +47,16 @@ architecture is implemented using several interacting components:
 For the simplicity the other execution systems - Dask and MPI are omitted and only Ray execution is shown.
 
 * Dataframe subsystem is the backbone of the dataframe holding and query compilation. It is responsible for
-  dispatching the ingress/egress to the appropriate module, getting the Pandas API and calling the query
+  dispatching the ingress/egress to the appropriate module, getting the pandas API and calling the query
   compiler to convert calls to the internal intermediate Dataframe Algebra.
 * Data Ingress/Egress Module is working in conjunction with Dataframe and Partitions subsystem to read data
   split into partitions and send data into the appropriate node for storing.
-* Query Planner is subsystem that translates the Pandas API to intermediate Dataframe Algebra representation
+* Query Planner is subsystem that translates the pandas API to intermediate Dataframe Algebra representation
   DAG and performs an initial set of optimizations.
 * Query Executor is responsible for getting the Dataframe Algebra DAG, performing further optimizations based
   on a selected storage format and mapping or compiling the Dataframe Algebra DAG to and actual
   execution sequence.
-* Storage formats module is responsible for mapping the abstract operation to an actual executor call, e.g. Pandas,
+* Storage formats module is responsible for mapping the abstract operation to an actual executor call, e.g. pandas,
   PyArrow, custom format.
 * Orchestration subsystem is responsible for spawning and controlling the actual execution environment for the
   selected execution. It spawns the actual nodes, fires up the execution environment, e.g. Ray, monitors the state
@@ -109,21 +109,11 @@ The API is the outer-most layer that faces users. The majority of our current ef
 spent implementing the components of the pandas API. We have implemented a toy example
 for a sqlite API as a proof of concept, but this isn't ready for usage/testing. There
 are also plans to expose the Modin DataFrame API as a reduced API set that encompasses
-the entire pandas/dataframe API. See `experimental features`_ for more information.
+the entire pandas/dataframe API. See :doc:`advanced usage </advanced_usage/index>` for more information.
 
 .. toctree::
-   :caption: Base Pandas Dataset API
-
    /flow/modin/pandas/base
-
-.. toctree::
-   :caption: modin.pandas.DataFrame API
-
    /flow/modin/pandas/dataframe
-
-.. toctree::
-   :caption: modin.pandas.Series API
-
    /flow/modin/pandas/series
 
 Query Compiler
@@ -254,18 +244,18 @@ Supported Execution Frameworks and Memory Formats
 
 This is the list of execution frameworks and memory formats supported in Modin. If you
 would like to contribute a new execution framework or memory format, please see the
-documentation page on :doc:`contributing </contributing>`.
+documentation page on :doc:`contributing </developer/contributing>`.
 
-- :doc:`Pandas on Ray </UsingPandasonRay/index>`
+- :doc:`pandas on Ray </developer/using_pandas_on_ray>`
     - Uses the Ray_ execution framework.
     - The compute kernel/in-memory format is a pandas DataFrame.
-- :doc:`Pandas on Dask </UsingPandasonDask/index>`
+- :doc:`pandas on Dask </developer/using_pandas_on_dask>`
     - Uses the `Dask Futures`_ execution framework.
     - The compute kernel/in-memory format is a pandas DataFrame.
-- :doc:`Omnisci </UsingOmnisci/index>`
+- :doc:`Omnisci </developer/using_omnisci>`
     - Uses OmniSciDB as an engine.
     - The compute kernel/in-memory format is a pyarrow Table or pandas DataFrame when defaulting to pandas.
-- :doc:`Pyarrow on Ray </UsingPyarrowonRay/index>` (experimental)
+- :doc:`Pyarrow on Ray </developer/using_pyarrow_on_ray>` (experimental)
     - Uses the Ray_ execution framework.
     - The compute kernel/in-memory format is a pyarrow Table.
 
@@ -348,4 +338,4 @@ by documentation for now, the rest is coming soon...).
 .. _issue: https://github.com/modin-project/modin/issues
 .. _Discourse: https://discuss.modin.org
 .. _task parallel: https://en.wikipedia.org/wiki/Task_parallelism
-.. _experimental features: /experimental_features/index.html
+.. _experimental features: /advanced_usage/index.html
diff --git a/docs/contributing.rst → docs/developer/contributing.rst b/docs/contributing.rst → docs/developer/contributing.rst
diff --git a/docs/developer/index.rst b/docs/developer/index.rst
@@ -0,0 +1,19 @@
+Developer
+=========
+
+.. toctree::
+    :maxdepth: 4
+
+    contributing
+    architecture
+    partition_api
+    using_pandas_on_ray
+    using_pandas_on_dask
+    using_omnisci
+    using_pyarrow_on_ray
+    using_sql_on_ray
+    troubleshooting
+
+.. meta::
+    :description lang=en:
+        Developer-specific documentation.
diff --git a/docs/developer/partition_api.rst b/docs/developer/partition_api.rst
@@ -14,7 +14,7 @@ You can pass the partitions having needed IPs to your function. It can help with
 Partition API implementations
 -----------------------------
 By default, a :py:class:`~modin.pandas.dataframe.DataFrame` stores underlying partitions as ``pandas.DataFrame`` objects.
-You can find the specific implementation of Modin's Partition Interface in :doc:`Pandas Partition API </flow/modin/distributed/dataframe/pandas>`.
+You can find the specific implementation of Modin's Partition Interface in :doc:`pandas Partition API </flow/modin/distributed/dataframe/pandas>`.
 
 .. toctree::
   :hidden: