DOC: Removing tailing whitespaces in .rst files (pandas-dev#24281)

Author: datapythonista

doc/source/advanced.rst

@@ -54,7 +54,7 @@ can think of ``MultiIndex`` as an array of tuples where each tuple is unique. A
 ``MultiIndex`` can be created from a list of arrays (using
 :meth:`MultiIndex.from_arrays`), an array of tuples (using
 :meth:`MultiIndex.from_tuples`), a crossed set of iterables (using
-:meth:`MultiIndex.from_product`), or a :class:`DataFrame` (using 
+:meth:`MultiIndex.from_product`), or a :class:`DataFrame` (using
 :meth:`MultiIndex.from_frame`).  The ``Index`` constructor will attempt to return
 a ``MultiIndex`` when it is passed a list of tuples.  The following examples
 demonstrate different ways to initialize MultiIndexes.
@@ -81,7 +81,7 @@ to use the :meth:`MultiIndex.from_product` method:
    iterables = [['bar', 'baz', 'foo', 'qux'], ['one', 'two']]
    pd.MultiIndex.from_product(iterables, names=['first', 'second'])
 
-You can also construct a ``MultiIndex`` from a ``DataFrame`` directly, using 
+You can also construct a ``MultiIndex`` from a ``DataFrame`` directly, using
 the method :meth:`MultiIndex.from_frame`. This is a complementary method to
 :meth:`MultiIndex.to_frame`.
 

doc/source/categorical.rst

@@ -11,8 +11,8 @@ with R's ``factor``.
 
 `Categoricals` are a pandas data type corresponding to categorical variables in
 statistics. A categorical variable takes on a limited, and usually fixed,
-number of possible values (`categories`; `levels` in R). Examples are gender, 
-social class, blood type, country affiliation, observation time or rating via 
+number of possible values (`categories`; `levels` in R). Examples are gender,
+social class, blood type, country affiliation, observation time or rating via
 Likert scales.
 
 In contrast to statistical categorical variables, categorical data might have an order (e.g.
@@ -133,7 +133,7 @@ This conversion is likewise done column by column:
 Controlling Behavior
 ~~~~~~~~~~~~~~~~~~~~
 
-In the examples above where we passed ``dtype='category'``, we used the default 
+In the examples above where we passed ``dtype='category'``, we used the default
 behavior:
 
 1. Categories are inferred from the data.
@@ -170,8 +170,8 @@ are consistent among all columns.
     categories for each column, the ``categories`` parameter can be determined programmatically by
     ``categories = pd.unique(df.to_numpy().ravel())``.
 
-If you already have ``codes`` and ``categories``, you can use the 
-:func:`~pandas.Categorical.from_codes` constructor to save the factorize step 
+If you already have ``codes`` and ``categories``, you can use the
+:func:`~pandas.Categorical.from_codes` constructor to save the factorize step
 during normal constructor mode:
 
 .. ipython:: python
@@ -184,7 +184,7 @@ during normal constructor mode:
 Regaining Original Data
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-To get back to the original ``Series`` or NumPy array, use 
+To get back to the original ``Series`` or NumPy array, use
 ``Series.astype(original_dtype)`` or ``np.asarray(categorical)``:
 
 .. ipython:: python
@@ -222,7 +222,7 @@ This information can be stored in a :class:`~pandas.api.types.CategoricalDtype`.
 The ``categories`` argument is optional, which implies that the actual categories
 should be inferred from whatever is present in the data when the
 :class:`pandas.Categorical` is created. The categories are assumed to be unordered
-by default.      
+by default.
 
 .. ipython:: python
 
@@ -277,7 +277,7 @@ All instances of ``CategoricalDtype`` compare equal to the string ``'category'``
 Description
 -----------
 
-Using :meth:`~DataFrame.describe` on categorical data will produce similar 
+Using :meth:`~DataFrame.describe` on categorical data will produce similar
 output to a ``Series`` or ``DataFrame`` of type ``string``.
 
 .. ipython:: python
@@ -292,9 +292,9 @@ output to a ``Series`` or ``DataFrame`` of type ``string``.
 Working with categories
 -----------------------
 
-Categorical data has a `categories` and a `ordered` property, which list their 
-possible values and whether the ordering matters or not. These properties are 
-exposed as ``s.cat.categories`` and ``s.cat.ordered``. If you don't manually 
+Categorical data has a `categories` and a `ordered` property, which list their
+possible values and whether the ordering matters or not. These properties are
+exposed as ``s.cat.categories`` and ``s.cat.ordered``. If you don't manually
 specify categories and ordering, they are inferred from the passed arguments.
 
 .. ipython:: python
@@ -314,7 +314,7 @@ It's also possible to pass in the categories in a specific order:
 
 .. note::
 
-    New categorical data are **not** automatically ordered. You must explicitly 
+    New categorical data are **not** automatically ordered. You must explicitly
     pass ``ordered=True`` to indicate an ordered ``Categorical``.
 
 
@@ -338,8 +338,8 @@ It's also possible to pass in the categories in a specific order:
 Renaming categories
 ~~~~~~~~~~~~~~~~~~~
 
-Renaming categories is done by assigning new values to the 
-``Series.cat.categories`` property or by using the 
+Renaming categories is done by assigning new values to the
+``Series.cat.categories`` property or by using the
 :meth:`~pandas.Categorical.rename_categories` method:
 
 
@@ -385,7 +385,7 @@ Categories must also not be ``NaN`` or a `ValueError` is raised:
 Appending new categories
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-Appending categories can be done by using the 
+Appending categories can be done by using the
 :meth:`~pandas.Categorical.add_categories` method:
 
 .. ipython:: python
@@ -397,8 +397,8 @@ Appending categories can be done by using the
 Removing categories
 ~~~~~~~~~~~~~~~~~~~
 
-Removing categories can be done by using the 
-:meth:`~pandas.Categorical.remove_categories` method. Values which are removed 
+Removing categories can be done by using the
+:meth:`~pandas.Categorical.remove_categories` method. Values which are removed
 are replaced by ``np.nan``.:
 
 .. ipython:: python
@@ -421,8 +421,8 @@ Removing unused categories can also be done:
 Setting categories
 ~~~~~~~~~~~~~~~~~~
 
-If you want to do remove and add new categories in one step (which has some 
-speed advantage), or simply set the categories to a predefined scale, 
+If you want to do remove and add new categories in one step (which has some
+speed advantage), or simply set the categories to a predefined scale,
 use :meth:`~pandas.Categorical.set_categories`.
 
 
@@ -618,10 +618,10 @@ When you compare two unordered categoricals with the same categories, the order
 Operations
 ----------
 
-Apart from :meth:`Series.min`, :meth:`Series.max` and :meth:`Series.mode`, the 
+Apart from :meth:`Series.min`, :meth:`Series.max` and :meth:`Series.mode`, the
 following operations are possible with categorical data:
 
-``Series`` methods like :meth:`Series.value_counts` will use all categories, 
+``Series`` methods like :meth:`Series.value_counts` will use all categories,
 even if some categories are not present in the data:
 
 .. ipython:: python
@@ -666,7 +666,7 @@ that only values already in `categories` can be assigned.
 Getting
 ~~~~~~~
 
-If the slicing operation returns either a ``DataFrame`` or a column of type 
+If the slicing operation returns either a ``DataFrame`` or a column of type
 ``Series``, the ``category`` dtype is preserved.
 
 .. ipython:: python
@@ -681,7 +681,7 @@ If the slicing operation returns either a ``DataFrame`` or a column of type
     df.loc["h":"j", "cats"]
     df[df["cats"] == "b"]
 
-An example where the category type is not preserved is if you take one single 
+An example where the category type is not preserved is if you take one single
 row: the resulting ``Series`` is of dtype ``object``:
 
 .. ipython:: python
@@ -702,7 +702,7 @@ of length "1".
     The is in contrast to R's `factor` function, where ``factor(c(1,2,3))[1]``
     returns a single value `factor`.
 
-To get a single value ``Series`` of type ``category``, you pass in a list with 
+To get a single value ``Series`` of type ``category``, you pass in a list with
 a single value:
 
 .. ipython:: python
@@ -756,7 +756,7 @@ That means, that the returned values from methods and properties on the accessor
 Setting
 ~~~~~~~
 
-Setting values in a categorical column (or ``Series``) works as long as the 
+Setting values in a categorical column (or ``Series``) works as long as the
 value is included in the `categories`:
 
 .. ipython:: python
@@ -836,9 +836,9 @@ Unioning
 
 .. versionadded:: 0.19.0
 
-If you want to combine categoricals that do not necessarily have the same 
+If you want to combine categoricals that do not necessarily have the same
 categories, the :func:`~pandas.api.types.union_categoricals` function will
-combine a list-like of categoricals. The new categories will be the union of 
+combine a list-like of categoricals. The new categories will be the union of
 the categories being combined.
 
 .. ipython:: python
@@ -887,8 +887,8 @@ using the ``ignore_ordered=True`` argument.
     b = pd.Categorical(["c", "b", "a"], ordered=True)
     union_categoricals([a, b], ignore_order=True)
 
-:func:`~pandas.api.types.union_categoricals` also works with a 
-``CategoricalIndex``, or ``Series`` containing categorical data, but note that 
+:func:`~pandas.api.types.union_categoricals` also works with a
+``CategoricalIndex``, or ``Series`` containing categorical data, but note that
 the resulting array will always be a plain ``Categorical``:
 
 .. ipython:: python
@@ -1179,8 +1179,8 @@ Setting the index will create a ``CategoricalIndex``:
 Side Effects
 ~~~~~~~~~~~~
 
-Constructing a ``Series`` from a ``Categorical`` will not copy the input 
-``Categorical``. This means that changes to the ``Series`` will in most cases 
+Constructing a ``Series`` from a ``Categorical`` will not copy the input
+``Categorical``. This means that changes to the ``Series`` will in most cases
 change the original ``Categorical``:
 
 .. ipython:: python

doc/source/comparison_with_sas.rst

@@ -364,7 +364,7 @@ String Processing
 Length
 ~~~~~~
 
-SAS determines the length of a character string with the 
+SAS determines the length of a character string with the
 `LENGTHN <https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002284668.htm>`__
 and `LENGTHC <https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002283942.htm>`__
 functions. ``LENGTHN`` excludes trailing blanks and ``LENGTHC`` includes trailing blanks.
@@ -378,7 +378,7 @@ functions. ``LENGTHN`` excludes trailing blanks and ``LENGTHC`` includes trailin
    run;
 
 Python determines the length of a character string with the ``len`` function.
-``len`` includes trailing blanks.  Use ``len`` and ``rstrip`` to exclude 
+``len`` includes trailing blanks.  Use ``len`` and ``rstrip`` to exclude
 trailing blanks.
 
 .. ipython:: python
@@ -390,9 +390,9 @@ trailing blanks.
 Find
 ~~~~
 
-SAS determines the position of a character in a string with the 
+SAS determines the position of a character in a string with the
 `FINDW <https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a002978282.htm>`__ function.
-``FINDW`` takes the string defined by the first argument and searches for the first position of the substring 
+``FINDW`` takes the string defined by the first argument and searches for the first position of the substring
 you supply as the second argument.
 
 .. code-block:: sas
@@ -402,10 +402,10 @@ you supply as the second argument.
    put(FINDW(sex,'ale'));
    run;
 
-Python determines the position of a character in a string with the 
-``find`` function.  ``find`` searches for the first position of the 
-substring.  If the substring is found, the function returns its 
-position.  Keep in mind that Python indexes are zero-based and 
+Python determines the position of a character in a string with the
+``find`` function.  ``find`` searches for the first position of the
+substring.  If the substring is found, the function returns its
+position.  Keep in mind that Python indexes are zero-based and
 the function will return -1 if it fails to find the substring.
 
 .. ipython:: python
@@ -416,7 +416,7 @@ the function will return -1 if it fails to find the substring.
 Substring
 ~~~~~~~~~
 
-SAS extracts a substring from a string based on its position with the 
+SAS extracts a substring from a string based on its position with the
 `SUBSTR <https://www2.sas.com/proceedings/sugi25/25/cc/25p088.pdf>`__ function.
 
 .. code-block:: sas
@@ -427,7 +427,7 @@ SAS extracts a substring from a string based on its position with the
    run;
 
 With pandas you can use ``[]`` notation to extract a substring
-from a string by position locations.  Keep in mind that Python 
+from a string by position locations.  Keep in mind that Python
 indexes are zero-based.
 
 .. ipython:: python
@@ -439,7 +439,7 @@ Scan
 ~~~~
 
 The SAS `SCAN <https://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htm#a000214639.htm>`__
-function returns the nth word from a string. The first argument is the string you want to parse and the 
+function returns the nth word from a string. The first argument is the string you want to parse and the
 second argument specifies which word you want to extract.
 
 .. code-block:: sas
@@ -452,10 +452,10 @@ second argument specifies which word you want to extract.
    John Smith;
    Jane Cook;
    ;;;
-   run;   
+   run;
 
-Python extracts a substring from a string based on its text 
-by using regular expressions. There are much more powerful 
+Python extracts a substring from a string based on its text
+by using regular expressions. There are much more powerful
 approaches, but this just shows a simple approach.
 
 .. ipython:: python

doc/source/computation.rst

@@ -13,9 +13,9 @@ Statistical Functions
 Percent Change
 ~~~~~~~~~~~~~~
 
-``Series``, ``DataFrame``, and ``Panel`` all have a method 
-:meth:`~DataFrame.pct_change` to compute the percent change over a given number 
-of periods (using ``fill_method`` to fill NA/null values *before* computing 
+``Series``, ``DataFrame``, and ``Panel`` all have a method
+:meth:`~DataFrame.pct_change` to compute the percent change over a given number
+of periods (using ``fill_method`` to fill NA/null values *before* computing
 the percent change).
 
 .. ipython:: python
@@ -35,7 +35,7 @@ the percent change).
 Covariance
 ~~~~~~~~~~
 
-:meth:`Series.cov` can be used to compute covariance between series 
+:meth:`Series.cov` can be used to compute covariance between series
 (excluding missing values).
 
 .. ipython:: python
@@ -44,7 +44,7 @@ Covariance
    s2 = pd.Series(np.random.randn(1000))
    s1.cov(s2)
 
-Analogously, :meth:`DataFrame.cov` to compute pairwise covariances among the 
+Analogously, :meth:`DataFrame.cov` to compute pairwise covariances among the
 series in the DataFrame, also excluding NA/null values.
 
 .. _computation.covariance.caveats:
@@ -87,7 +87,7 @@ Correlation
 ~~~~~~~~~~~
 
 Correlation may be computed using the :meth:`~DataFrame.corr` method.
-Using the ``method`` parameter, several methods for computing correlations are 
+Using the ``method`` parameter, several methods for computing correlations are
 provided:
 
 .. csv-table::
@@ -158,8 +158,8 @@ compute the correlation based on histogram intersection:
 
    frame.corr(method=histogram_intersection)
 
-A related method :meth:`~DataFrame.corrwith` is implemented on DataFrame to 
-compute the correlation between like-labeled Series contained in different 
+A related method :meth:`~DataFrame.corrwith` is implemented on DataFrame to
+compute the correlation between like-labeled Series contained in different
 DataFrame objects.
 
 .. ipython:: python
@@ -176,7 +176,7 @@ DataFrame objects.
 Data ranking
 ~~~~~~~~~~~~
 
-The :meth:`~Series.rank` method produces a data ranking with ties being 
+The :meth:`~Series.rank` method produces a data ranking with ties being
 assigned the mean of the ranks (by default) for the group:
 
 .. ipython:: python
@@ -185,8 +185,8 @@ assigned the mean of the ranks (by default) for the group:
    s['d'] = s['b']  # so there's a tie
    s.rank()
 
-:meth:`~DataFrame.rank` is also a DataFrame method and can rank either the rows 
-(``axis=0``) or the columns (``axis=1``). ``NaN`` values are excluded from the 
+:meth:`~DataFrame.rank` is also a DataFrame method and can rank either the rows
+(``axis=0``) or the columns (``axis=1``). ``NaN`` values are excluded from the
 ranking.
 
 .. ipython:: python
@@ -637,7 +637,7 @@ perform multiple computations on the data. These operations are similar to the :
    r = dfa.rolling(window=60, min_periods=1)
    r
 
-We can aggregate by passing a function to the entire DataFrame, or select a 
+We can aggregate by passing a function to the entire DataFrame, or select a
 Series (or multiple Series) via standard ``__getitem__``.
 
 .. ipython:: python