Modifies hardcoded links in docs to use reST syntax (stitchfix#309)

This is a user contribution from 37323048+CharityKithaka@users.noreply.github.com
where the clean up is around the hard coded external links that were not following
the reST syntax properly.

---------

Co-authored-by: Charity Kithaka <37323048+CharityKithaka@users.noreply.github.com>

docs/best-practices/using-within-your-etl-system.rst

@@ -12,21 +12,21 @@ Compatibility Matrix
 
    * - Framework / Scheduler
      - Compatibility
-   * - `Airflow <http://airflow.org/>`_
+   * - `Airflow <http://airflow.org>`_
      - ✅
-   * - `Dagster <https://dagster.io/>`_
+   * - `Dagster <https://dagster.io>`_
      - ✅
-   * - `Prefect <https://prefect.io/>`_
+   * - `Prefect <https://prefect.io>`_
      - ✅
-   * - `Kubeflow Pipelines <https://www.kubeflow.org/docs/components/pipelines/>`_
+   * - `Kubeflow Pipelines <https://www.kubeflow.org/docs/components/pipelines>`_
      - ✅
    * - `CRON <https://en.wikipedia.org/wiki/Cron>`_
      - ✅
-   * - `dbt <https://getdbt.com/>`_
+   * - `dbt <https://getdbt.com>`_
      - ❔  (dbt did not run python, but now it does so it should)
-   * - `kubernetes <https://kubernetes.io/>`_
-     - ✅ but you need to setup kubernetes to run an image that can run python code - e.g. see `https://medium.com/avmconsulting-blog/running-a-python-application-on-kubernetes-aws-56609e7cd88c <https://medium.com/avmconsulting-blog/running-a-python-application-on-kubernetes-aws-56609e7cd88c>`_
-   * - `docker <https://www.docker.com/>`_
+   * - `kubernetes <https://kubernetes.io>`_
+     - ✅ but you need to setup kubernetes to run an image that can run python code - e.g. see `Running a python application on kubernetes <https://medium.com/avmconsulting-blog/running-a-python-application-on-kubernetes-aws-56609e7cd88c>`_
+   * - `docker <https://www.docker.com>`_
      - ✅ but you need to setup a docker image that can execute python code.
    * - ... in general if it runs python 3.6+ ...
      - ✅

docs/extensions.rst

@@ -56,9 +56,9 @@ endeavor to ensure backwards compatible changes when they can be accommodated.
 Ray - Experimental!
 ===================
 
-`Ray <https://ray.io/>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
+`Ray <https://ray.io>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
 
-See `https://github.com/stitchfix/hamilton/tree/main/examples/ray <https://github.com/stitchfix/hamilton/tree/main/examples/ray>`_
+See `Scaling Hamilton on Ray <https://github.com/stitchfix/hamilton/tree/main/examples/ry>`_
 for an example of using Ray.
 
 Single Machine:
@@ -79,19 +79,18 @@ in terms of the amount of data it can process.
 Dask - Experimental!
 ====================
 
-`Ray <https://ray.io/>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
+`Ray <https://ray.io>`_ is a system to scale python workloads. Hamilton makes it very easy for you to use Ray.
 
-See `https://github.com/stitchfix/hamilton/tree/main/examples/dask <https://github.com/stitchfix/hamilton/tree/main/examples/dask>`_
+See `Scaling Hamilton on Dask <https://github.com/stitchfix/hamilton/tree/main/examples/dask>`_
 for an example of using Dask to scale Hamilton computation.
 
 Single Machine:
 ***************
 
-`Dask <https://dask.org/>`_ is a very easy way to enable multi-processing on a single machine. This enables you to
+`Dask <https://dask.org>`_ is a very easy way to enable multi-processing on a single machine. This enables you to
 easily make use of multiple CPU cores.
 
-What this doesn't help with is data scale, as you're still limited to what fits in memory on your machine.
-
+What this doesn't help with is data scale, as you're still limited to what fits in memory on your machine
 Distributed Computation:
 ************************
 
@@ -105,7 +104,7 @@ Koalas on Spark, a.k.a. Pandas API on Spark - Experimental!
 was the project code name to implement the `Pandas API on top of Spark <https://spark.apache.org/docs/latest/api/python/user\_guide/pandas\_on\_spark/index.html>`_.
 Hamilton makes it very easy for you to use Koalas on Spark.
 
-See `https://github.com/stitchfix/hamilton/tree/main/examples/spark <https://github.com/stitchfix/hamilton/tree/main/examples/spark>`_
+See `Scaling Hamilton on Koalas <https://github.com/stitchfix/hamilton/tree/main/examples/spark>`_
 for an example of using Koalas on Spark to scale Hamilton computation.
 
 Single Machine:

docs/hamilton-community.rst

@@ -6,8 +6,7 @@ If you want to bounce ideas, ask questions, or just see what the small community
 
 We have a bunch of channels on Slack that are designed to help you get the most out of Hamilton!
 
-Slack space name: `https://hamilton-opensource.slack.com/ <https://hamilton-opensource.slack.com/>`_
-
-Slack invite link: `https://join.slack.com/t/hamilton-opensource/shared\_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg <https://join.slack.com/t/hamilton-opensource/shared\_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg>`_
+Slack space name: `hamilton-opensource <https://hamilton-opensource.slack.com>`_
 
+Slack invite link: `click me to join <https://join.slack.com/t/hamilton-opensource/shared\_invite/zt-1bjs72asx-wcUTgH7q7QX1igiQ5bbdcg>`_
 See you there!

docs/less-than-15-minutes-to-mastery/installing.rst

@@ -13,7 +13,7 @@ Hamilton is a lightweight framework with a variety of extensions/plugins. To get
 
 * ``pip``
 
-For help with python/pip/managing virtual environments see the `python docs <https://docs.python.org/3/tutorial/venv.html>`_.
+For help with python/pip/managing virtual environments see the `python docs <https://docs.python.org/3/tutorial/venv.html/>`_.
 
 Installing
 ----------
@@ -27,5 +27,5 @@ To use the DAG visualization functionality, instead install with
 ``pip install sf-hamilton[visualization]``
 
 *Note: for visualization you may additionally need to install graphviz externally -- see*
-`https://graphviz.org/download/ <https://graphviz.org/download/>`_ *for instructions on the correct way for your
+`graphviz <https://graphviz.org/download/>`_ *for instructions on the correct way for your
 operating system.*

docs/overview-of-concepts/the-hamilton-driver.rst

@@ -210,7 +210,7 @@ Visualizing Execution
 #####################
 
 Hamilton enables you to quickly and easily visualize your entire DAG, as well as the specific execution path to compute
-an output. Underneath we default to use [graphviz](https://graphviz.org/) for visualization.
+an output. Underneath we default to use `graphviz <https://graphviz.org/>`_ for visualization.
 
 Visualize just execution required to create outputs
 ***************************************************

docs/reference/api-reference/available-decorators.rst

@@ -4,7 +4,7 @@ Available Decorators
 
 While the 1:1 mapping of column -> function implementation is powerful, we've implemented a few decorators to promote
 business-logic reuse. The decorators we've defined are as follows (source can be found in
-`function\_modifiers <https://github.com/stitchfix/hamilton/blob/main/hamilton/function\_modifiers.py>`_):
+`function_modifiers <https://github.com/stitchfix/hamilton/blob/main/hamilton/function\_modifiers.py>`_):
 
 @tag
 ----
@@ -184,7 +184,7 @@ of the series, and one that checks whether the data is in a certain range.
 
 Note that you can also specify custom decorators using the ``@check_output_custom`` decorator.
 
-See `data\_quality <https://github.com/stitchfix/hamilton/blob/main/data\_quality.md>`_ for more information on
+See `data_quality <https://github.com/stitchfix/hamilton/blob/main/data\_quality.md>`_ for more information on
 available validators and how to build custom ones.
 
 @parameterize

docs/talks-or-podcasts-or-blogs-or-papers.rst

@@ -12,7 +12,7 @@ This page curates talks, podcasts, and blogs about Hamilton
      - Info / Links
    * - August 2022
      - Hamilton: a modular open source declarative paradigm for high level modeling of dataflows
-     - `Paper <https://cdmsworkshop.github.io/2022/Proceedings/ShortPapers/Paper6\_StefanKrawczyk.pdf>`_ at `CDMS Workshop <https://cdmsworkshop.github.io/2022/overview.html>`_ @ `VLDB2022 <https://vldb.org/2022/>`_
+     - `Paper <https://cdmsworkshop.github.io/2022/Proceedings/ShortPapers/Paper6\_StefanKrawczyk.pdf>`_ at `CDMS Workshop <https://cdmsworkshop.github.io/2022/overview.html>`_ @ `VLDB2022 <https://vldb.org/2022>`_
    * - July 2022
      - Hamilton: A Python Micro-Framework for Data / Feature Engineering @MLOPs World Bay Area
      - `Slides <https://github.com/skrawcz/talks/files/9213924/Hamilton\_.A.Python.Micro-Framework.for.Data.\_.Feature.Engineering.pdf>`_
@@ -35,14 +35,14 @@ This page curates talks, podcasts, and blogs about Hamilton
      - Functions and DAGs: Hamilton, a General Purpose Micro-Framework for Pandas Dataframe Generation
      - `Blog post <https://towardsdatascience.com/functions-dags-introducing-hamilton-a-microframework-for-dataframe-generation-more-8e34b84efc1d>`__
    * - February 2022
-     - [Open Source] Hamilton, a micro framework for creating dataframes, and its application at Stitch Fix @ `Apply(meetup) <https://www.applyconf.com/agenda/open-source-hamilton-a-micro-framework-for-creating-dataframes-and-its-application-at-stitch-fix/>`_
+     - [Open Source] Hamilton, a micro framework for creating dataframes, and its application at Stitch Fix @ `Apply(meetup) <https://www.applyconf.com/agenda/open-source-hamilton-a-micro-framework-for-creating-dataframes-and-its-application-at-stitch-fix>`_
      - 30 minute talk about Hamilton, it's origin, tips on using it, and some exciting extensions & plans - `Youtube <https://www.youtube.com/watch?v=CHfrT5OVjlM>`_. For slides, see below.
    * - October 2022
      - Functions & DAGs: introducing Hamilton, a microframework for dataframe generation
-     - Blog post on Hamilton origins. `https://multithreaded.stitchfix.com/blog/2021/10/14/functions-dags-hamilton/ <https://multithreaded.stitchfix.com/blog/2021/10/14/functions-dags-hamilton/>`_
+     - Blog post on Hamilton origins. `Blog post <https://multithreaded.stitchfix.com/blog/2021/10/14/functions-dags-hamilton>`_
    * - April 2021
      - Hamilton: a Micro Framework for Creating Dataframes @ applyconf
-     - 10 minute lightning talk on Hamilton `https://www.youtube.com/watch?v=B5Zp\_30Knoo <https://www.youtube.com/watch?v=B5Zp\_30Knoo>`_
+     - 10 minute lightning talk on Hamilton `Youtube video <https://www.youtube.com/watch?v=B5Zp\_30Knoo>`_
 
 Slides
 ------

docs/tutorials/data-quality-checks.rst

@@ -53,8 +53,7 @@ This is easy with the ``check_output`` decorator!
         return avg_3wk_spend / signups
 
 It takes in a series of arguments -- you can discover these by exploring the code (more documentation of specific
-arguments to follow): `https://github.com/stitchfix/hamilton/blob/main/hamilton/data\_quality/default\_validators.py <https://github.com/stitchfix/hamilton/blob/main/hamilton/data\_quality/default\_validators.py>`_.
-
+arguments to follow): `DefaultValidation <https://github.com/stitchfix/hamilton/blob/main/hamilton/data\_quality/default\_validators.py/>`_
 You can also utilize ``pandera`` to the same effect, simply by providing a ``schema`` argument:
 
 .. code-block:: python
@@ -185,6 +184,6 @@ enabling you to programmatically react to them down the line.
 This concludes our brief lessons on managing data quality in Hamilton -- we hope you spin it up and give it a try.
 
 In the mean time, we have more detailed documentation here -
-`https://github.com/stitchfix/hamilton/blob/main/data\_quality.md <https://github.com/stitchfix/hamilton/blob/main/data\_quality.md>`_
+`data_quality.md <https://github.com/stitchfix/hamilton/blob/main/data\_quality.md/>`_
 and some more examples (including distributed systems support) -
-`https://github.com/stitchfix/hamilton/tree/main/examples/data\_quality <https://github.com/stitchfix/hamilton/tree/main/examples/data\_quality>`_.
+`data_quality examples <https://github.com/stitchfix/hamilton/tree/main/examples/data\_quality/>`_.

docs/tutorials/ray.rst

@@ -9,11 +9,11 @@ For those eager to just see code, please jump right ahead to a full hello world
 
 .. image:: ../_static/Hamilton_ray_post_image.png
 
-For those unfamiliar with `Ray <https://ray.io/>`_, it is an open source framework that can scale python applications
-that came out of `UC Berkeley <https://rise.cs.berkeley.edu/projects/ray/>`_. It has a growing ecosystem of tooling
+For those unfamiliar with `Ray <https://ray.io>`_, it is an open source framework that can scale python applications
+that came out of `UC Berkeley <https://rise.cs.berkeley.edu/projects/ray>`_. It has a growing ecosystem of tooling
 that helps with lots of machine learning related workflows. For example, it sells itself as enabling you to scale from
 your laptop to a cluster very easily, without having to change much code. In terms of real world use, we like to use
-Ray as a very quick way to implement `multiprocessing in python <https://machinelearningmastery.com/multiprocessing-in-python/>`_
+Ray as a very quick way to implement `multiprocessing in python <https://machinelearningmastery.com/multiprocessing-in-python>`_
 without worry about the details!
 
 Ray Primer
@@ -151,7 +151,7 @@ been very stable (hasn’t changed since launch), but to feel good about making
 think.
 #. We don’t expose all the functionality of Ray, but we could. E.g. memory aware scheduling, or specifying resources for
 specific functions. Let us know if you want something exposed — create an issue on github please — 
-`https://github.com/stitchfix/hamilton <https://github.com/stitchfix/hamilton>`_.
+`stitchfix/hamilton <https://github.com/stitchfix/hamilton>`_.
 
 To conclude
 -----------

docs/tutorials/using-hamilton-in-a-notebook.rst

@@ -177,7 +177,7 @@ Pro-tip: You can use ipython magic to autoreload code
 -----------------------------------------------------
 
 Open a Python module and a Jupyter notebook side-to-side, and then add
-`%autoreload ipython magic <https://ipython.org/ipython-doc/3/config/extensions/autoreload.html>`_ to the notebook to
+`autoreload ipython magic <https://ipython.org/ipython-doc/3/config/extensions/autoreload.html>`_ to the notebook to
 auto-reload the cell:
 
 .. code-block:: python