Cleanup philosophy doc (#634)

* typo fix: 'too' -> 'to'

* standardise spaces after '.' within paragraph

All other ends of sentences (period/full stop, '.') are followed by two
spaces - these are followed by one. Making these match.

Note this has no impact on rendering, Markdown only creates a single
rendered space after a '.' in either case.

* remove trailing whitespace in paragraph

All other lines in this paragraph have no trailing whitespace, remove
this single trailing whitespace to match.

Note that this has no impact on the rendering of the Markdown.

* standardise spaces after '.' for whole philsophy doc

As with previous commit, but for the whole document.

Found using the regex "\. ([^ ])" and replacing with ".  $1".

All other ends of sentences (period/full stop, '.') are followed by two
spaces - these are followed by one. Making these match.

Note this has no impact on rendering, Markdown only creates a single
rendered space after a '.' in either case.

* remove trailing whitespace for whole philosophy doc

As with previous commit, but for the whole document.

Found using the regex "\s$".

All other lines have no trailing whitespace, remove these single
trailing whitespaces to match.

Note that this has no impact on the rendering of the Markdown.

docs/philosophy.md

@@ -1,12 +1,12 @@
 # pandas-stubs Type Checking Philosophy
 
 The goal of the pandas-stubs project is to provide type stubs for the public API
-that represent the recommended ways of using pandas. This is opposed to the 
+that represent the recommended ways of using pandas.  This is opposed to the
 philosophy within the pandas source, as described [here](https://pandas.pydata.org/docs/development/contributing_codebase.html?highlight=typing#type-hints), which
 is to assist with the development of the pandas source code to ensure type safety within
 that source.
 
-Due to the methodology used by Microsoft to develop the original stubs, there are internal 
+Due to the methodology used by Microsoft to develop the original stubs, there are internal
 classes, methods and functions that are annotated within the pandas-stubs project
 that are incorrect with respect to the pandas source, but that have no effect on type
 checking user code that calls the public API.
@@ -27,12 +27,12 @@ s = pd.Series([1, 2, 3])
 lt = s < 3
 ```
 
-In the pandas source, `lt` is a `Series` with a `dtype` of `bool`. In the pandas-stubs,
-the type of `lt` is `Series[bool]`. This allows further type checking to occur in other
+In the pandas source, `lt` is a `Series` with a `dtype` of `bool`.  In the pandas-stubs,
+the type of `lt` is `Series[bool]`.  This allows further type checking to occur in other
 pandas methods.  Note that in the above example, `s` is typed as `Series[Any]` because
 its type cannot be statically inferred.
 
-This also allows type checking for operations on series that contain date/time data. Consider
+This also allows type checking for operations on series that contain date/time data.  Consider
 the following example that creates two series of datetimes with corresponding arithmetic.
 
 ```python
@@ -74,22 +74,22 @@ interval of `Timestamp`s.
 A set of (most likely incomplete) tests for testing the type stubs is in the pandas-stubs
 repository in the `tests` directory.  The tests are used with `mypy` and `pyright` to
 validate correct typing, and also with `pytest` to validate that the provided code
-actually executes. The recent decision for Python 3.11 to include `assert_type()`,
+actually executes.  The recent decision for Python 3.11 to include `assert_type()`,
 which is supported by `typing_extensions` version 4.2 and beyond makes it easier
-to test to validate the return types of functions and methods. Future work
+to test to validate the return types of functions and methods.  Future work
 is intended to expand the use of `assert_type()` in the test code.
 
 ## Narrow vs. Wide Arguments
 
-A consideration in creating stubs is too make the set of type annotations for
+A consideration in creating stubs is to make the set of type annotations for
 function arguments "just right", i.e.,
 not too narrow and not too wide.  A type annotation to an argument to a function or
 method is too narrow if it disallows valid arguments.  A type annotation to
 an argument to a function or method is too wide if
 it allows invalid arguments.  Testing for type annotations that are too narrow is rather
-straightforward. It is easy to create an example for which the type checker indicates
+straightforward.  It is easy to create an example for which the type checker indicates
 the argument is incorrect, and add it to the set of tests in the pandas-stubs
-repository after fixing the appropriate stub. However, testing for when type annotations 
+repository after fixing the appropriate stub.  However, testing for when type annotations
 are too wide is a bit more complicated.
 In this case, the test will fail when using `pytest`, but it is also desirable to
 have type checkers report errors for code that is expected to fail type checking.
@@ -108,9 +108,9 @@ Here is an example that illustrates this concept, from `tests/test_interval.py`:
 In this particular example, the stubs consider that `i1` will have the type
 `pd.Interval[pd.Timestamp]`.  It is incorrect code to add a `Timestamp` to a
 time-based interval.  Without the `if TYPE_CHECKING_INVALID_USAGE` construct, the
-code would fail at runtime. Further, type checkers should report an error for this
-incorrect code. By placing the `# type: ignore[operator] # pyright: ignore[reportGeneralTypeIssues]`
-on the line, type checkers are told to ignore the type error. To ensure that the
+code would fail at runtime.  Further, type checkers should report an error for this
+incorrect code.  By placing the `# type: ignore[operator] # pyright: ignore[reportGeneralTypeIssues]`
+on the line, type checkers are told to ignore the type error.  To ensure that the
 pandas-stubs annotations are not too wide (allow adding a `Timestamp` to a
 time-based interval), mypy and pyright are configured to report unused ignore
 statements.