diff --git a/docs/philosophy.md b/docs/philosophy.md
index 9204ec599..bc9106317 100644
--- a/docs/philosophy.md
+++ b/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.