CI/STYLE Fix misspellings exposed by codespell

pandas/_version.py

@@ -293,7 +293,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
         # TAG-NUM-gHEX
         mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
         if not mo:
-            # unparseable. Maybe git-describe is misbehaving?
+            # unparsable. Maybe git-describe is misbehaving?
             pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out
             return pieces
 

pandas/io/common.py

@@ -291,7 +291,7 @@ def _get_filepath_or_buffer(
         # urlopen function defined elsewhere in this module
         import urllib.request
 
-        # assuming storage_options is to be interpretted as headers
+        # assuming storage_options is to be interpreted as headers
         req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
         with urlopen(req_info) as req:
             content_encoding = req.headers.get("Content-Encoding", None)

pandas/io/excel/_base.py

@@ -188,7 +188,7 @@
     * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
       result 'foo'
 
-    If a column or index contains an unparseable date, the entire column or
+    If a column or index contains an unparsable date, the entire column or
     index will be returned unaltered as an object data type. If you don`t want to
     parse some cells as date just change their type in Excel to "Text".
     For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.

pandas/io/formats/format.py

@@ -919,7 +919,7 @@ class DataFrameRenderer:
     Parameters
     ----------
     fmt : DataFrameFormatter
-        Formatter with the formating options.
+        Formatter with the formatting options.
     """
 
     def __init__(self, fmt: DataFrameFormatter):

pandas/io/formats/info.py

@@ -683,7 +683,7 @@ def _gen_columns(self) -> Iterator[str]:
 
 def _get_dataframe_dtype_counts(df: DataFrame) -> Mapping[str, int]:
     """
-    Create mapping between datatypes and their number of occurences.
+    Create mapping between datatypes and their number of occurrences.
     """
     # groupby dtype.name to collect e.g. Categorical columns
     return df.dtypes.value_counts().groupby(lambda x: x.name).sum()

pandas/io/formats/style.py

@@ -1697,7 +1697,7 @@ def from_custom_template(cls, searchpath, name):
         """
         loader = jinja2.ChoiceLoader([jinja2.FileSystemLoader(searchpath), cls.loader])
 
-        # mypy doesnt like dynamically-defined class
+        # mypy doesn't like dynamically-defined classes
         # error: Variable "cls" is not valid as a type  [valid-type]
         # error: Invalid base class "cls"  [misc]
         class MyStyler(cls):  # type:ignore[valid-type,misc]

setup.cfg

@@ -67,7 +67,7 @@ filterwarnings =
 junit_family=xunit2
 
 [codespell]
-ignore-words-list=ba,blocs,coo,hist,nd,ser
+ignore-words-list=ba,blocs,coo,hist,nd,ser,mose,fo,ist
 
 [coverage:run]
 branch = False