DOC: Fix errors detected by sphinx-lint (pandas-dev#46586)

* Avoid mixing tabs and space.

* fix rst issues found by sphinx-lint.

* Make flake8-rst happy.

* FIX: two hyperlinks in rst.

* Marc' suggestion

* Use sphinx-lint tag in pre-commit hook.

Author: matilda-me

.pre-commit-config.yaml

@@ -70,6 +70,10 @@ repos:
       - id: rst-inline-touching-normal
         types: [text]  # overwrite types: [rst]
         types_or: [python, rst]
+-   repo: https://github.com/sphinx-contrib/sphinx-lint
+    rev: v0.2
+    hooks:
+    - id: sphinx-lint
 -   repo: https://github.com/asottile/yesqa
     rev: v1.3.0
     hooks:

doc/source/development/contributing_codebase.rst

@@ -223,7 +223,7 @@ In some cases you may be tempted to use ``cast`` from the typing module when you
            ...
        else:  # Reasonably only str objects would reach this but...
            obj = cast(str, obj)  # Mypy complains without this!
-	   return obj.upper()
+           return obj.upper()
 
 The limitation here is that while a human can reasonably understand that ``is_number`` would catch the ``int`` and ``float`` types mypy cannot make that same inference just yet (see `mypy #5206 <https://github.com/python/mypy/issues/5206>`_. While the above works, the use of ``cast`` is **strongly discouraged**. Where applicable a refactor of the code to appease static analysis is preferable
 

doc/source/development/contributing_environment.rst

@@ -85,10 +85,10 @@ You will need `Build Tools for Visual Studio 2019
 <https://visualstudio.microsoft.com/downloads/>`_.
 
 .. warning::
-	You DO NOT need to install Visual Studio 2019.
-	You only need "Build Tools for Visual Studio 2019" found by
-	scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
-	In the installer, select the "C++ build tools" workload.
+        You DO NOT need to install Visual Studio 2019.
+        You only need "Build Tools for Visual Studio 2019" found by
+        scrolling down to "All downloads" -> "Tools for Visual Studio 2019".
+        In the installer, select the "C++ build tools" workload.
 
 You can install the necessary components on the commandline using
 `vs_buildtools.exe <https://download.visualstudio.microsoft.com/download/pr/9a26f37e-6001-429b-a5db-c5455b93953c/460d80ab276046de2455a4115cc4e2f1e6529c9e6cb99501844ecafd16c619c4/vs_BuildTools.exe>`_:

doc/source/ecosystem.rst

@@ -540,15 +540,15 @@ Pandas-Genomics provides extension types, extension arrays, and extension access
 `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.
 
 `Text Extensions for Pandas`_
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-``Text Extensions for Pandas <https://ibm.biz/text-extensions-for-pandas>``
+`Text Extensions for Pandas <https://ibm.biz/text-extensions-for-pandas>`_
 provides extension types to cover common data structures for representing natural language
 data, plus library integrations that convert the outputs of popular natural language
 processing libraries into Pandas DataFrames.

doc/source/user_guide/dsintro.rst

@@ -678,7 +678,7 @@ Boolean operators operate element-wise as well:
 Transposing
 ~~~~~~~~~~~
 
-To transpose, access the ``T`` attribute or :meth:`DataFrame.transpose``,
+To transpose, access the ``T`` attribute or :meth:`DataFrame.transpose`,
 similar to an ndarray:
 
 .. ipython:: python

doc/source/user_guide/groupby.rst

@@ -539,19 +539,19 @@ Some common aggregating functions are tabulated below:
     :widths: 20, 80
     :delim: ;
 
-	:meth:`~pd.core.groupby.DataFrameGroupBy.mean`;Compute mean of groups
-	:meth:`~pd.core.groupby.DataFrameGroupBy.sum`;Compute sum of group values
-	:meth:`~pd.core.groupby.DataFrameGroupBy.size`;Compute group sizes
-	:meth:`~pd.core.groupby.DataFrameGroupBy.count`;Compute count of group
-	:meth:`~pd.core.groupby.DataFrameGroupBy.std`;Standard deviation of groups
-	:meth:`~pd.core.groupby.DataFrameGroupBy.var`;Compute variance of groups
-	:meth:`~pd.core.groupby.DataFrameGroupBy.sem`;Standard error of the mean of groups
-	:meth:`~pd.core.groupby.DataFrameGroupBy.describe`;Generates descriptive statistics
-	:meth:`~pd.core.groupby.DataFrameGroupBy.first`;Compute first of group values
-	:meth:`~pd.core.groupby.DataFrameGroupBy.last`;Compute last of group values
-	:meth:`~pd.core.groupby.DataFrameGroupBy.nth`;Take nth value, or a subset if n is a list
-	:meth:`~pd.core.groupby.DataFrameGroupBy.min`;Compute min of group values
-	:meth:`~pd.core.groupby.DataFrameGroupBy.max`;Compute max of group values
+        :meth:`~pd.core.groupby.DataFrameGroupBy.mean`;Compute mean of groups
+        :meth:`~pd.core.groupby.DataFrameGroupBy.sum`;Compute sum of group values
+        :meth:`~pd.core.groupby.DataFrameGroupBy.size`;Compute group sizes
+        :meth:`~pd.core.groupby.DataFrameGroupBy.count`;Compute count of group
+        :meth:`~pd.core.groupby.DataFrameGroupBy.std`;Standard deviation of groups
+        :meth:`~pd.core.groupby.DataFrameGroupBy.var`;Compute variance of groups
+        :meth:`~pd.core.groupby.DataFrameGroupBy.sem`;Standard error of the mean of groups
+        :meth:`~pd.core.groupby.DataFrameGroupBy.describe`;Generates descriptive statistics
+        :meth:`~pd.core.groupby.DataFrameGroupBy.first`;Compute first of group values
+        :meth:`~pd.core.groupby.DataFrameGroupBy.last`;Compute last of group values
+        :meth:`~pd.core.groupby.DataFrameGroupBy.nth`;Take nth value, or a subset if n is a list
+        :meth:`~pd.core.groupby.DataFrameGroupBy.min`;Compute min of group values
+        :meth:`~pd.core.groupby.DataFrameGroupBy.max`;Compute max of group values
 
 
 The aggregating functions above will exclude NA values. Any function which

doc/source/user_guide/io.rst

@@ -5695,9 +5695,9 @@ for an explanation of how the database connection is handled.
 
 .. warning::
 
-	When you open a connection to a database you are also responsible for closing it.
-	Side effects of leaving a connection open may include locking the database or
-	other breaking behaviour.
+        When you open a connection to a database you are also responsible for closing it.
+        Side effects of leaving a connection open may include locking the database or
+        other breaking behaviour.
 
 Writing DataFrames
 ''''''''''''''''''

doc/source/user_guide/timeseries.rst

@@ -2405,9 +2405,9 @@ you can use the ``tz_convert`` method.
 
 .. warning::
 
-	Be wary of conversions between libraries. For some time zones, ``pytz`` and ``dateutil`` have different
-	definitions of the zone. This is more of a problem for unusual time zones than for
-	'standard' zones like ``US/Eastern``.
+        Be wary of conversions between libraries. For some time zones, ``pytz`` and ``dateutil`` have different
+        definitions of the zone. This is more of a problem for unusual time zones than for
+        'standard' zones like ``US/Eastern``.
 
 .. warning::
 

doc/source/user_guide/window.rst

@@ -624,13 +624,13 @@ average of ``3, NaN, 5`` would be calculated as
 
 .. math::
 
-	\frac{(1-\alpha)^2 \cdot 3 + 1 \cdot 5}{(1-\alpha)^2 + 1}.
+        \frac{(1-\alpha)^2 \cdot 3 + 1 \cdot 5}{(1-\alpha)^2 + 1}.
 
 Whereas if ``ignore_na=True``, the weighted average would be calculated as
 
 .. math::
 
-	\frac{(1-\alpha) \cdot 3 + 1 \cdot 5}{(1-\alpha) + 1}.
+        \frac{(1-\alpha) \cdot 3 + 1 \cdot 5}{(1-\alpha) + 1}.
 
 The :meth:`~Ewm.var`, :meth:`~Ewm.std`, and :meth:`~Ewm.cov` functions have a ``bias`` argument,
 specifying whether the result should contain biased or unbiased statistics.

doc/source/whatsnew/v0.15.0.rst

@@ -462,15 +462,15 @@ Rolling/expanding moments improvements
 
   .. code-block:: ipython
 
-	In [51]: ewma(s, com=3., min_periods=2)
-	Out[51]:
-	0         NaN
-	1         NaN
-	2    1.000000
-	3    1.000000
-	4    1.571429
-	5    2.189189
-	dtype: float64
+        In [51]: pd.ewma(s, com=3., min_periods=2)
+        Out[51]:
+        0         NaN
+        1         NaN
+        2    1.000000
+        3    1.000000
+        4    1.571429
+        5    2.189189
+        dtype: float64
 
   New behavior (note values start at index ``4``, the location of the 2nd (since ``min_periods=2``) non-empty value):
 
@@ -557,21 +557,21 @@ Rolling/expanding moments improvements
 
   .. code-block:: ipython
 
-	 In [89]: ewmvar(s, com=2., bias=False)
-	 Out[89]:
-	 0   -2.775558e-16
-	 1    3.000000e-01
-	 2    9.556787e-01
-	 3    3.585799e+00
-	 dtype: float64
-
-	 In [90]: ewmvar(s, com=2., bias=False) / ewmvar(s, com=2., bias=True)
-	 Out[90]:
-	 0    1.25
-	 1    1.25
-	 2    1.25
-	 3    1.25
-	 dtype: float64
+         In [89]: pd.ewmvar(s, com=2., bias=False)
+         Out[89]:
+         0   -2.775558e-16
+         1    3.000000e-01
+         2    9.556787e-01
+         3    3.585799e+00
+         dtype: float64
+
+         In [90]: pd.ewmvar(s, com=2., bias=False) / pd.ewmvar(s, com=2., bias=True)
+         Out[90]:
+         0    1.25
+         1    1.25
+         2    1.25
+         3    1.25
+         dtype: float64
 
   Note that entry ``0`` is approximately 0, and the debiasing factors are a constant 1.25.
   By comparison, the following 0.15.0 results have a ``NaN`` for entry ``0``,

doc/source/whatsnew/v0.18.1.rst

@@ -149,8 +149,8 @@ can return a valid boolean indexer or anything which is valid for these indexer'
    # callable returns list of labels
    df.loc[lambda x: [1, 2], lambda x: ["A", "B"]]
 
-Indexing with``[]``
-"""""""""""""""""""
+Indexing with ``[]``
+""""""""""""""""""""
 
 Finally, you can use a callable in ``[]`` indexing of Series, DataFrame and Panel.
 The callable must return a valid input for ``[]`` indexing depending on its

doc/source/whatsnew/v0.19.0.rst

@@ -1553,7 +1553,7 @@ Bug fixes
 - Bug in invalid datetime parsing in ``to_datetime`` and ``DatetimeIndex`` may raise ``TypeError`` rather than ``ValueError`` (:issue:`11169`, :issue:`11287`)
 - Bug in ``Index`` created with tz-aware ``Timestamp`` and mismatched ``tz`` option incorrectly coerces timezone (:issue:`13692`)
 - Bug in ``DatetimeIndex`` with nanosecond frequency does not include timestamp specified with ``end`` (:issue:`13672`)
-- Bug in ```Series`` when setting a slice with a ``np.timedelta64`` (:issue:`14155`)
+- Bug in ``Series`` when setting a slice with a ``np.timedelta64`` (:issue:`14155`)
 - Bug in ``Index`` raises ``OutOfBoundsDatetime`` if ``datetime`` exceeds ``datetime64[ns]`` bounds, rather than coercing to ``object`` dtype (:issue:`13663`)
 - Bug in ``Index`` may ignore specified ``datetime64`` or ``timedelta64`` passed as ``dtype``  (:issue:`13981`)
 - Bug in ``RangeIndex`` can be created without no arguments rather than raises ``TypeError`` (:issue:`13793`)

doc/source/whatsnew/v0.21.1.rst

@@ -125,7 +125,7 @@ Indexing
 IO
 ^^
 
-- Bug in class:`~pandas.io.stata.StataReader` not converting date/time columns with display formatting addressed (:issue:`17990`). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
+- Bug in :class:`~pandas.io.stata.StataReader` not converting date/time columns with display formatting addressed (:issue:`17990`). Previously columns with display formatting were normally left as ordinal numbers and not converted to datetime objects.
 - Bug in :func:`read_csv` when reading a compressed UTF-16 encoded file (:issue:`18071`)
 - Bug in :func:`read_csv` for handling null values in index columns when specifying ``na_filter=False`` (:issue:`5239`)
 - Bug in :func:`read_csv` when reading numeric category fields with high cardinality (:issue:`18186`)

doc/source/whatsnew/v0.23.0.rst

@@ -1126,7 +1126,7 @@ Removal of prior version deprecations/changes
 - The ``Panel`` class has dropped the ``to_long`` and ``toLong`` methods (:issue:`19077`)
 - The options ``display.line_with`` and ``display.height`` are removed in favor of ``display.width`` and ``display.max_rows`` respectively (:issue:`4391`, :issue:`19107`)
 - The ``labels`` attribute of the ``Categorical`` class has been removed in favor of :attr:`Categorical.codes` (:issue:`7768`)
-- The ``flavor`` parameter have been removed from func:`to_sql` method (:issue:`13611`)
+- The ``flavor`` parameter have been removed from :func:`to_sql` method (:issue:`13611`)
 - The modules ``pandas.tools.hashing`` and ``pandas.util.hashing`` have been removed (:issue:`16223`)
 - The top-level functions ``pd.rolling_*``, ``pd.expanding_*`` and ``pd.ewm*`` have been removed (Deprecated since v0.18).
   Instead, use the DataFrame/Series methods :attr:`~DataFrame.rolling`, :attr:`~DataFrame.expanding` and :attr:`~DataFrame.ewm` (:issue:`18723`)

doc/source/whatsnew/v0.25.0.rst

@@ -1121,7 +1121,7 @@ Indexing
 - Bug in which :meth:`DataFrame.to_csv` caused a segfault for a reindexed data frame, when the indices were single-level :class:`MultiIndex` (:issue:`26303`).
 - Fixed bug where assigning a :class:`arrays.PandasArray` to a :class:`pandas.core.frame.DataFrame` would raise error (:issue:`26390`)
 - Allow keyword arguments for callable local reference used in the :meth:`DataFrame.query` string (:issue:`26426`)
-- Fixed a ``KeyError`` when indexing a :class:`MultiIndex`` level with a list containing exactly one label, which is missing (:issue:`27148`)
+- Fixed a ``KeyError`` when indexing a :class:`MultiIndex` level with a list containing exactly one label, which is missing (:issue:`27148`)
 - Bug which produced ``AttributeError`` on partial matching :class:`Timestamp` in a :class:`MultiIndex`  (:issue:`26944`)
 - Bug in :class:`Categorical` and  :class:`CategoricalIndex` with :class:`Interval` values when using the ``in`` operator (``__contains``) with objects that are not comparable to the values in the ``Interval`` (:issue:`23705`)
 - Bug in :meth:`DataFrame.loc` and :meth:`DataFrame.iloc` on a :class:`DataFrame` with a single timezone-aware datetime64[ns] column incorrectly returning a scalar instead of a :class:`Series` (:issue:`27110`)

doc/source/whatsnew/v0.7.0.rst

@@ -190,11 +190,11 @@ been added:
     :header: "Method","Description"
     :widths: 40,60
 
-	``Series.iget_value(i)``, Retrieve value stored at location ``i``
-	``Series.iget(i)``, Alias for ``iget_value``
-	``DataFrame.irow(i)``, Retrieve the ``i``-th row
-	``DataFrame.icol(j)``, Retrieve the ``j``-th column
-	"``DataFrame.iget_value(i, j)``", Retrieve the value at row ``i`` and column ``j``
+        ``Series.iget_value(i)``, Retrieve value stored at location ``i``
+        ``Series.iget(i)``, Alias for ``iget_value``
+        ``DataFrame.irow(i)``, Retrieve the ``i``-th row
+        ``DataFrame.icol(j)``, Retrieve the ``j``-th column
+        "``DataFrame.iget_value(i, j)``", Retrieve the value at row ``i`` and column ``j``
 
 API tweaks regarding label-based slicing
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/whatsnew/v0.9.1.rst

@@ -54,44 +54,44 @@ New features
   - DataFrame has new ``where`` and ``mask`` methods to select values according to a
     given boolean mask (:issue:`2109`, :issue:`2151`)
 
-	DataFrame currently supports slicing via a boolean vector the same length as the DataFrame (inside the ``[]``).
-	The returned DataFrame has the same number of columns as the original, but is sliced on its index.
+        DataFrame currently supports slicing via a boolean vector the same length as the DataFrame (inside the ``[]``).
+        The returned DataFrame has the same number of columns as the original, but is sliced on its index.
 
         .. ipython:: python
 
-    	    df = DataFrame(np.random.randn(5, 3), columns = ['A','B','C'])
+            df = pd.DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])
 
-	    df
+            df
 
-	    df[df['A'] > 0]
+            df[df['A'] > 0]
 
-	If a DataFrame is sliced with a DataFrame based boolean condition (with the same size as the original DataFrame),
-	then a DataFrame the same size (index and columns) as the original is returned, with
-	elements that do not meet the boolean condition as ``NaN``. This is accomplished via
-	the new method ``DataFrame.where``. In addition, ``where`` takes an optional ``other`` argument for replacement.
+        If a DataFrame is sliced with a DataFrame based boolean condition (with the same size as the original DataFrame),
+        then a DataFrame the same size (index and columns) as the original is returned, with
+        elements that do not meet the boolean condition as ``NaN``. This is accomplished via
+        the new method ``DataFrame.where``. In addition, ``where`` takes an optional ``other`` argument for replacement.
 
-	.. ipython:: python
+        .. ipython:: python
 
-	   df[df>0]
+           df[df > 0]
 
-	   df.where(df>0)
+           df.where(df > 0)
 
-	   df.where(df>0,-df)
+           df.where(df > 0, -df)
 
-	Furthermore, ``where`` now aligns the input boolean condition (ndarray or DataFrame), such that partial selection
-	with setting is possible. This is analogous to partial setting via ``.ix`` (but on the contents rather than the axis labels)
+        Furthermore, ``where`` now aligns the input boolean condition (ndarray or DataFrame), such that partial selection
+        with setting is possible. This is analogous to partial setting via ``.ix`` (but on the contents rather than the axis labels)
 
-	.. ipython:: python
+        .. ipython:: python
 
-	   df2 = df.copy()
-   	   df2[ df2[1:4] > 0 ] = 3
-	   df2
+           df2 = df.copy()
+           df2[df2[1:4] > 0] = 3
+           df2
 
-	``DataFrame.mask`` is the inverse boolean operation of ``where``.
+        ``DataFrame.mask`` is the inverse boolean operation of ``where``.
 
-	.. ipython:: python
+        .. ipython:: python
 
-	   df.mask(df<=0)
+           df.mask(df <= 0)
 
   - Enable referencing of Excel columns by their column names (:issue:`1936`)
 

doc/source/whatsnew/v1.0.0.rst

@@ -525,7 +525,7 @@ Use :meth:`arrays.IntegerArray.to_numpy` with an explicit ``na_value`` instead.
 
    a.to_numpy(dtype="float", na_value=np.nan)
 
-**Reductions can return ``pd.NA``**
+**Reductions can return** ``pd.NA``
 
 When performing a reduction such as a sum with ``skipna=False``, the result
 will now be ``pd.NA`` instead of ``np.nan`` in presence of missing values

doc/source/whatsnew/v1.1.0.rst

@@ -665,9 +665,9 @@ the previous index (:issue:`32240`).
    In [4]: result
    Out[4]:
         min_val
-    0 	x
-    1 	y
-    2 	z
+    0   x
+    1   y
+    2   z
 
 *New behavior*:
 

doc/source/whatsnew/v1.4.0.rst

@@ -826,7 +826,7 @@ Datetimelike
 - Bug in :meth:`Timestamp.to_pydatetime` failing to retain the ``fold`` attribute (:issue:`45087`)
 - Bug in :meth:`Series.mode` with ``DatetimeTZDtype`` incorrectly returning timezone-naive and ``PeriodDtype`` incorrectly raising (:issue:`41927`)
 - Fixed regression in :meth:`~Series.reindex` raising an error when using an incompatible fill value with a datetime-like dtype (or not raising a deprecation warning for using a ``datetime.date`` as fill value) (:issue:`42921`)
-- Bug in :class:`DateOffset`` addition with :class:`Timestamp` where ``offset.nanoseconds`` would not be included in the result (:issue:`43968`, :issue:`36589`)
+- Bug in :class:`DateOffset` addition with :class:`Timestamp` where ``offset.nanoseconds`` would not be included in the result (:issue:`43968`, :issue:`36589`)
 - Bug in :meth:`Timestamp.fromtimestamp` not supporting the ``tz`` argument (:issue:`45083`)
 - Bug in :class:`DataFrame` construction from dict of :class:`Series` with mismatched index dtypes sometimes raising depending on the ordering of the passed dict (:issue:`44091`)
 - Bug in :class:`Timestamp` hashing during some DST transitions caused a segmentation fault (:issue:`33931` and :issue:`40817`)

pandas/core/groupby/generic.py

@@ -1658,13 +1658,13 @@ def value_counts(
         ... })
 
         >>> df
-            gender 	education 	country
-        0 	male 	low 	    US
-        1 	male 	medium 	    FR
-        2 	female 	high 	    US
-        3 	male 	low 	    FR
-        4 	female 	high 	    FR
-        5 	male 	low 	    FR
+                gender  education   country
+        0       male    low         US
+        1       male    medium      FR
+        2       female  high        US
+        3       male    low         FR
+        4       female  high        FR
+        5       male    low         FR
 
         >>> df.groupby('gender').value_counts()
         gender  education  country