documentation

sdpython · sdpython · commit 9bedb6284bd7 · 2022-11-27T00:32:39.000+01:00
diff --git a/README.rst b/README.rst
@@ -39,9 +39,6 @@ pandas_streaming: streaming API over pandas
     :target: http://www.xavierdupre.fr/app/pandas_streaming/helpsphinx/all_notebooks_coverage.html
     :alt: Notebook Coverage
 
-.. image:: https://api.codacy.com/project/badge/Grade/f53b7f4d6a0447aa9ce0c4ad5df659ef
-    :target: https://www.codacy.com/app/sdpython/pandas_streaming?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sdpython/pandas_streaming&amp;utm_campaign=Badge_Grade
-
 .. image:: https://pepy.tech/badge/pandas_streaming/month
     :target: https://pepy.tech/project/pandas_streaming/month
     :alt: Downloads
diff --git a/_doc/sphinxdoc/source/index.rst b/_doc/sphinxdoc/source/index.rst
@@ -40,9 +40,6 @@ pandas_streaming: streaming API over pandas
     :target: http://www.xavierdupre.fr/app/pandas_streaming/helpsphinx/all_notebooks_coverage.html
     :alt: Notebook Coverage
 
-.. image:: https://api.codacy.com/project/badge/Grade/f53b7f4d6a0447aa9ce0c4ad5df659ef
-    :target: https://www.codacy.com/app/sdpython/pandas_streaming?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sdpython/pandas_streaming&amp;utm_campaign=Badge_Grade
-
 .. image:: https://pepy.tech/badge/pandas_streaming
     :target: https://pypi.org/project/pandas_streaming/
     :alt: Downloads
diff --git a/pandas_streaming/df/dataframe.py b/pandas_streaming/df/dataframe.py
@@ -922,7 +922,8 @@ def __getitem__(self, *args):
         offers for the operator ``[]``.
         """
         if len(args) != 1:
-            raise NotImplementedError("Only a list of columns is supported.")
+            raise NotImplementedError(  # pragma: no cover
+                "Only a list of columns is supported.")
         cols = args[0]
         if isinstance(cols, str):
             # One column.
@@ -1022,7 +1023,7 @@ def add_column(self, col, value):
 
         """
         if not isinstance(col, str):
-            raise NotImplementedError(
+            raise NotImplementedError(  # pragma: no cover
                 "Only a column as a string is supported.")
 
         if isfunction(value):
@@ -1144,7 +1145,7 @@ def sort_values(self, by, axis=0, ascending=True, kind='quicksort',
         :return: streaming database
         """
         if not isinstance(by, str):
-            raise NotImplementedError(
+            raise NotImplementedError(  # pragma: no cover
                 f"Only one column can be used to sort not {by!r}.")
         keys = {}
         nans = []
@@ -1223,8 +1224,9 @@ def __init__(self, iter_creation, check_schema=True, stable=True):
         StreamingDataFrame.__init__(
             self, iter_creation, check_schema=check_schema, stable=stable)
         if len(self.columns) != 1:
-            raise RuntimeError(
-                f"A series can contain only one column not {len(self.columns)!r}.")
+            raise RuntimeError(  # pragma: no cover
+                f"A series can contain only one column not "
+                f"{len(self.columns)!r}.")
 
     def apply(self, *args, **kwargs) -> 'StreamingDataFrame':
         """
diff --git a/pandas_streaming/df/dataframe_helpers.py b/pandas_streaming/df/dataframe_helpers.py
@@ -166,7 +166,7 @@ def hash_floatl(c):
         elif t == object:
             df[c] = df[c].apply(hash_strl)
         else:
-            raise NotImplementedError(
+            raise NotImplementedError(  # pragma: no cover
                 f"Conversion of type {t} in column '{c}' is not implemented")
 
     return df
@@ -452,10 +452,10 @@ def pandas_groupby_nan(df, by, axis=0, as_index=False, suffix=None, nanback=True
                                 res.grouper.groupings[0]._cache['result_index'] = (
                                     index.__class__(new_index))
                             else:
-                                raise NotImplementedError(
+                                raise NotImplementedError(  # pragma: no cover
                                     "NaN values not implemented for multiindex.")
                     else:
-                        raise NotImplementedError(
+                        raise NotImplementedError(  # pragma: no cover
                             "Not implemented for type: {0}".format(
                                 type(res.grouper.groupings[0].grouper)))
                 res.grouper._cache['result_index'] = res.grouper.groupings[0]._group_index
diff --git a/pandas_streaming/df/dataframe_io_helpers.py b/pandas_streaming/df/dataframe_io_helpers.py
@@ -323,7 +323,8 @@ def enumerate_json_items(filename, encoding=None, lines=False, flatten=False, fL
                     current[curkey] = None  # pylint: disable=E1137
                     curkey = None
             else:
-                raise ValueError(f"Unknown event '{event}'")  # pragma: no cover
+                raise ValueError(
+                    f"Unknown event '{event}'")  # pragma: no cover
 
 
 class JsonIterator2Stream:
diff --git a/pandas_streaming/df/dataframe_split.py b/pandas_streaming/df/dataframe_split.py
@@ -41,7 +41,7 @@ def sklearn_train_test_split(self, path_or_buf=None, export_method="to_csv",
         mode *append* and allows stream.
     """
     if kwargs.get("stratify") is not None:
-        raise NotImplementedError(
+        raise NotImplementedError(  # pragma: no cover
             "No implementation yet for the stratified version.")
     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", category=ImportWarning)
