Replace single with double backticks in RST file pandas-dev#36617 (pandas-dev#36632)

Author: Shubhamsm

.pre-commit-config.yaml

@@ -34,20 +34,6 @@ repos:
     rev: v1.6.0
     hooks:
       - id: rst-backticks
-        # these exclusions should be removed and the files fixed
-        exclude: (?x)(
-          categorical\.rst|
-          contributing\.rst|
-          contributing_docstring\.rst|
-          extending\.rst|
-          ecosystem\.rst|
-          comparison_with_sql\.rst|
-          install\.rst|
-          calculate_statistics\.rst|
-          combine_dataframes\.rst|
-          v0\.|
-          v1\.0\.|
-          v1\.1\.[012])
 -   repo: local
     hooks:
     -   id: pip_to_conda

doc/source/development/contributing.rst

@@ -31,13 +31,13 @@ comment letting others know they are working on an issue. While this is ok, you
 check each issue individually, and it's not possible to find the unassigned ones.
 
 For this reason, we implemented a workaround consisting of adding a comment with the exact
-text `take`. When you do it, a GitHub action will automatically assign you the issue
+text ``take``. When you do it, a GitHub action will automatically assign you the issue
 (this will take seconds, and may require refreshing the page to see it).
 By doing this, it's possible to filter the list of issues and find only the unassigned ones.
 
 So, a good way to find an issue to start contributing to pandas is to check the list of
 `unassigned good first issues <https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22+no%3Aassignee>`_
-and assign yourself one you like by writing a comment with the exact text `take`.
+and assign yourself one you like by writing a comment with the exact text ``take``.
 
 If for whatever reason you are not able to continue working with the issue, please try to
 unassign it, so other people know it's available again. You can check the list of
@@ -133,7 +133,7 @@ want to clone your fork to your machine::
     cd pandas-yourname
     git remote add upstream https://github.com/pandas-dev/pandas.git
 
-This creates the directory `pandas-yourname` and connects your repository to
+This creates the directory ``pandas-yourname`` and connects your repository to
 the upstream (main project) *pandas* repository.
 
 Note that performing a shallow clone (with ``--depth==N``, for some ``N`` greater
@@ -155,12 +155,12 @@ Using a Docker container
 
 Instead of manually setting up a development environment, you can use `Docker
 <https://docs.docker.com/get-docker/>`_ to automatically create the environment with just several
-commands. Pandas provides a `DockerFile` in the root directory to build a Docker image
+commands. Pandas provides a ``DockerFile`` in the root directory to build a Docker image
 with a full pandas development environment.
 
 **Docker Commands**
 
-Pass your GitHub username in the `DockerFile` to use your own fork::
+Pass your GitHub username in the ``DockerFile`` to use your own fork::
 
     # Build the image pandas-yourname-env
     docker build --tag pandas-yourname-env .
@@ -172,7 +172,7 @@ Even easier, you can integrate Docker with the following IDEs:
 **Visual Studio Code**
 
 You can use the DockerFile to launch a remote session with Visual Studio Code,
-a popular free IDE, using the `.devcontainer.json` file.
+a popular free IDE, using the ``.devcontainer.json`` file.
 See https://code.visualstudio.com/docs/remote/containers for details.
 
 **PyCharm (Professional)**
@@ -782,7 +782,7 @@ As part of :ref:`Continuous Integration <contributing.ci>` checks we run::
 
     isort --check-only pandas
 
-to check that imports are correctly formatted as per the `setup.cfg`.
+to check that imports are correctly formatted as per the ``setup.cfg``.
 
 If you see output like the below in :ref:`Continuous Integration <contributing.ci>` checks:
 
@@ -979,7 +979,7 @@ For example, quite a few functions in pandas accept a ``dtype`` argument. This c
    def as_type(dtype: Dtype) -> ...:
        ...
 
-This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like `axis`. Development of this module is active so be sure to refer to the source for the most up to date list of available types.
+This module will ultimately house types for repeatedly used concepts like "path-like", "array-like", "numeric", etc... and can also hold aliases for commonly appearing parameters like ``axis``. Development of this module is active so be sure to refer to the source for the most up to date list of available types.
 
 Validating type hints
 ~~~~~~~~~~~~~~~~~~~~~
@@ -1302,7 +1302,7 @@ Or with one of the following constructs::
 
 Using `pytest-xdist <https://pypi.org/project/pytest-xdist>`_, one can
 speed up local testing on multicore machines. To use this feature, you will
-need to install `pytest-xdist` via::
+need to install ``pytest-xdist`` via::
 
     pip install pytest-xdist
 
@@ -1465,7 +1465,7 @@ The following defines how a commit message should be structured.  Please referen
 relevant GitHub issues in your commit message using GH1234 or #1234.  Either style
 is fine, but the former is generally preferred:
 
-* a subject line with `< 80` chars.
+* a subject line with ``< 80`` chars.
 * One blank line.
 * Optionally, a commit message body.
 

doc/source/development/contributing_docstring.rst

@@ -25,7 +25,7 @@ The next example gives an idea of what a docstring looks like:
         """
         Add up two integer numbers.
 
-        This function simply wraps the `+` operator, and does not
+        This function simply wraps the ``+`` operator, and does not
         do anything interesting, except for illustrating what
         the docstring of a very simple function looks like.
 
@@ -39,7 +39,7 @@ The next example gives an idea of what a docstring looks like:
         Returns
         -------
         int
-            The sum of `num1` and `num2`.
+            The sum of ``num1`` and ``num2``.
 
         See Also
         --------
@@ -126,9 +126,9 @@ backticks. The following are considered inline code:
 
     def add_values(arr):
         """
-        Add the values in `arr`.
+        Add the values in ``arr``.
 
-        This is equivalent to Python `sum` of :meth:`pandas.Series.sum`.
+        This is equivalent to Python ``sum`` of :meth:`pandas.Series.sum`.
 
         Some sections are omitted here for simplicity.
         """
@@ -144,13 +144,13 @@ backticks. The following are considered inline code:
 
         With several mistakes in the docstring.
 
-        It has a blank like after the signature `def func():`.
+        It has a blank like after the signature ``def func():``.
 
         The text 'Some function' should go in the line after the
         opening quotes of the docstring, not in the same line.
 
         There is a blank line between the docstring and the first line
-        of code `foo = 1`.
+        of code ``foo = 1``.
 
         The closing quotes should be in the next line, not in this one."""
 
@@ -269,11 +269,11 @@ after, and not between the line with the word "Parameters" and the one with
 the hyphens.
 
 After the title, each parameter in the signature must be documented, including
-`*args` and `**kwargs`, but not `self`.
+``*args`` and ``**kwargs``, but not ``self``.
 
 The parameters are defined by their name, followed by a space, a colon, another
 space, and the type (or types). Note that the space between the name and the
-colon is important. Types are not defined for `*args` and `**kwargs`, but must
+colon is important. Types are not defined for ``*args`` and ``**kwargs``, but must
 be defined for all other parameters. After the parameter definition, it is
 required to have a line with the parameter description, which is indented, and
 can have multiple lines. The description must start with a capital letter, and
@@ -285,13 +285,13 @@ comma at the end of the type. The exact form of the type in this case will be
 argument means, which can be added after a comma "int, default -1, meaning all
 cpus".
 
-In cases where the default value is `None`, meaning that the value will not be
-used. Instead of "str, default None", it is preferred to write "str, optional".
-When `None` is a value being used, we will keep the form "str, default None".
-For example, in `df.to_csv(compression=None)`, `None` is not a value being used,
+In cases where the default value is ``None``, meaning that the value will not be
+used. Instead of ``"str, default None"``, it is preferred to write ``"str, optional"``.
+When ``None`` is a value being used, we will keep the form "str, default None".
+For example, in ``df.to_csv(compression=None)``, ``None`` is not a value being used,
 but means that compression is optional, and no compression is being used if not
-provided. In this case we will use `str, optional`. Only in cases like
-`func(value=None)` and `None` is being used in the same way as `0` or `foo`
+provided. In this case we will use ``"str, optional"``. Only in cases like
+``func(value=None)`` and ``None`` is being used in the same way as ``0`` or ``foo``
 would be used, then we will specify "str, int or None, default None".
 
 **Good:**
@@ -331,13 +331,13 @@ would be used, then we will specify "str, int or None, default None".
             specified kind.
 
             Note the blank line between the parameters title and the first
-            parameter. Also, note that after the name of the parameter `kind`
+            parameter. Also, note that after the name of the parameter ``kind``
             and before the colon, a space is missing.
 
             Also, note that the parameter descriptions do not start with a
             capital letter, and do not finish with a dot.
 
-            Finally, the `**kwargs` parameter is missing.
+            Finally, the ``**kwargs`` parameter is missing.
 
             Parameters
             ----------
@@ -361,9 +361,9 @@ boolean, etc):
 * str
 * bool
 
-For complex types, define the subtypes. For `dict` and `tuple`, as more than
+For complex types, define the subtypes. For ``dict`` and ``tuple``, as more than
 one type is present, we use the brackets to help read the type (curly brackets
-for `dict` and normal brackets for `tuple`):
+for ``dict`` and normal brackets for ``tuple``):
 
 * list of int
 * dict of {str : int}
@@ -512,8 +512,8 @@ This section is used to let users know about pandas functionality
 related to the one being documented. In rare cases, if no related methods
 or functions can be found at all, this section can be skipped.
 
-An obvious example would be the `head()` and `tail()` methods. As `tail()` does
-the equivalent as `head()` but at the end of the `Series` or `DataFrame`
+An obvious example would be the ``head()`` and ``tail()`` methods. As ``tail()`` does
+the equivalent as ``head()`` but at the end of the ``Series`` or ``DataFrame``
 instead of at the beginning, it is good to let the users know about it.
 
 To give an intuition on what can be considered related, here there are some
@@ -608,8 +608,8 @@ Examples in docstrings, besides illustrating the usage of the function or
 method, must be valid Python code, that returns the given output in a
 deterministic way, and that can be copied and run by users.
 
-Examples are presented as a session in the Python terminal. `>>>` is used to
-present code. `...` is used for code continuing from the previous line.
+Examples are presented as a session in the Python terminal. ``>>>`` is used to
+present code. ``...`` is used for code continuing from the previous line.
 Output is presented immediately after the last line of code generating the
 output (no blank lines in between). Comments describing the examples can
 be added with blank lines before and after them.
@@ -664,7 +664,7 @@ A simple example could be:
             4   Falcon
             dtype: object
 
-            With the `n` parameter, we can change the number of returned rows:
+            With the ``n`` parameter, we can change the number of returned rows:
 
             >>> s.head(n=3)
             0   Ant
@@ -742,7 +742,7 @@ positional arguments ``head(3)``.
 
         def fillna(self, value):
             """
-            Replace missing values by `value`.
+            Replace missing values by ``value``.
 
             Examples
             --------
@@ -771,7 +771,7 @@ positional arguments ``head(3)``.
 
         def contains(self, pattern, case_sensitive=True, na=numpy.nan):
             """
-            Return whether each value contains `pattern`.
+            Return whether each value contains ``pattern``.
 
             In this case, we are illustrating how to use sections, even
             if the example is simple enough and does not require them.
@@ -788,8 +788,8 @@ positional arguments ``head(3)``.
 
             **Case sensitivity**
 
-            With `case_sensitive` set to `False` we can match `a` with both
-            `a` and `A`:
+            With ``case_sensitive`` set to ``False`` we can match ``a`` with both
+            ``a`` and ``A``:
 
             >>> s.contains(pattern='a', case_sensitive=False)
             0     True
@@ -800,7 +800,7 @@ positional arguments ``head(3)``.
 
             **Missing values**
 
-            We can fill missing values in the output using the `na` parameter:
+            We can fill missing values in the output using the ``na`` parameter:
 
             >>> s.contains(pattern='a', na=False)
             0    False
@@ -824,9 +824,9 @@ positional arguments ``head(3)``.
         Try to use meaningful data, when it makes the example easier
         to understand.
 
-        Try to avoid positional arguments like in `df.method(1)`. They
+        Try to avoid positional arguments like in ``df.method(1)``. They
         can be all right if previously defined with a meaningful name,
-        like in `present_value(interest_rate)`, but avoid them otherwise.
+        like in ``present_value(interest_rate)``, but avoid them otherwise.
 
         When presenting the behavior with different parameters, do not place
         all the calls one next to the other. Instead, add a short sentence
@@ -914,7 +914,7 @@ plot will be generated automatically when building the documentation.
     class Series:
         def plot(self):
             """
-            Generate a plot with the `Series` data.
+            Generate a plot with the ``Series`` data.
 
             Examples
             --------

doc/source/development/extending.rst

@@ -61,7 +61,7 @@ This can be a convenient way to extend pandas objects without subclassing them.
 If you write a custom accessor, make a pull request adding it to our
 :ref:`ecosystem` page.
 
-We highly recommend validating the data in your accessor's `__init__`.
+We highly recommend validating the data in your accessor's ``__init__``.
 In our ``GeoAccessor``, we validate that the data contains the expected columns,
 raising an ``AttributeError`` when the validation fails.
 For a ``Series`` accessor, you should validate the ``dtype`` if the accessor

doc/source/ecosystem.rst

@@ -436,7 +436,7 @@ arrays can be stored inside pandas' Series and DataFrame.
 `Pint-Pandas`_
 ~~~~~~~~~~~~~~
 
-`Pint-Pandas <https://github.com/hgrecco/pint-pandas>` provides an extension type for
+``Pint-Pandas <https://github.com/hgrecco/pint-pandas>`` provides an extension type for
 storing numeric arrays with units. These arrays can be stored inside pandas'
 Series and DataFrame. Operations between Series and DataFrame columns which
 use pint's extension array are then units aware.

doc/source/getting_started/comparison/comparison_with_sql.rst

@@ -19,7 +19,7 @@ As is customary, we import pandas and NumPy as follows:
     import numpy as np
 
 Most of the examples will utilize the ``tips`` dataset found within pandas tests.  We'll read
-the data into a DataFrame called `tips` and assume we have a database table of the same name and
+the data into a DataFrame called ``tips`` and assume we have a database table of the same name and
 structure.
 
 .. ipython:: python
@@ -429,7 +429,7 @@ Top n rows per group
          .query('rn < 3')
          .sort_values(['day', 'rn']))
 
-the same using `rank(method='first')` function
+the same using ``rank(method='first')`` function
 
 .. ipython:: python
 
@@ -453,7 +453,7 @@ the same using `rank(method='first')` function
 
 Let's find tips with (rank < 3) per gender group for (tips < 2).
 Notice that when using ``rank(method='min')`` function
-`rnk_min` remains the same for the same `tip`
+``rnk_min`` remains the same for the same ``tip``
 (as Oracle's RANK() function)
 
 .. ipython:: python

doc/source/getting_started/install.rst

@@ -179,7 +179,7 @@ In Linux/Mac you can run ``which python`` on your terminal and it will tell you
 using. If it's something like "/usr/bin/python", you're using the Python from the system, which is not recommended.
 
 It is highly recommended to use ``conda``, for quick installation and for package and dependency updates.
-You can find simple installation instructions for pandas in this document: `installation instructions </getting_started.html>`.
+You can find simple installation instructions for pandas in this document: ``installation instructions </getting_started.html>``.
 
 Installing from source
 ~~~~~~~~~~~~~~~~~~~~~~

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -197,7 +197,7 @@ on the grouped data as well:
    :align: center
 
 .. note::
-    The `Pclass` column contains numerical data but actually
+    The ``Pclass`` column contains numerical data but actually
     represents 3 categories (or factors) with respectively the labels ‘1’,
     ‘2’ and ‘3’. Calculating statistics on these does not make much sense.
     Therefore, pandas provides a ``Categorical`` data type to handle this

doc/source/getting_started/intro_tutorials/08_combine_dataframes.rst

@@ -123,9 +123,9 @@ concatenated tables to verify the operation:
 
 .. ipython:: python
 
-    print('Shape of the `air_quality_pm25` table: ', air_quality_pm25.shape)
-    print('Shape of the `air_quality_no2` table: ', air_quality_no2.shape)
-    print('Shape of the resulting `air_quality` table: ', air_quality.shape)
+    print('Shape of the ``air_quality_pm25`` table: ', air_quality_pm25.shape)
+    print('Shape of the ``air_quality_no2`` table: ', air_quality_no2.shape)
+    print('Shape of the resulting ``air_quality`` table: ', air_quality.shape)
 
 Hence, the resulting table has 3178 = 1110 + 2068 rows.