Ko ya346/update import doc

pandas/core/dtypes/concat.py

@@ -200,24 +200,22 @@ def union_categoricals(
 
     Examples
     --------
-    >>> from pandas.api.types import union_categoricals
-
     If you want to combine categoricals that do not necessarily have
     the same categories, `union_categoricals` will combine a list-like
     of categoricals. The new categories will be the union of the
     categories being combined.
 
     >>> a = pd.Categorical(["b", "c"])
     >>> b = pd.Categorical(["a", "b"])
-    >>> union_categoricals([a, b])
+    >>> pd.api.types.union_categoricals([a, b])
     ['b', 'c', 'a', 'b']
     Categories (3, object): ['b', 'c', 'a']
 
     By default, the resulting categories will be ordered as they appear
     in the `categories` of the data. If you want the categories to be
     lexsorted, use `sort_categories=True` argument.
 
-    >>> union_categoricals([a, b], sort_categories=True)
+    >>> pd.api.types.union_categoricals([a, b], sort_categories=True)
     ['b', 'c', 'a', 'b']
     Categories (3, object): ['a', 'b', 'c']
 
@@ -227,15 +225,15 @@ def union_categoricals(
 
     >>> a = pd.Categorical(["a", "b"], ordered=True)
     >>> b = pd.Categorical(["a", "b", "a"], ordered=True)
-    >>> union_categoricals([a, b])
+    >>> pd.api.types.union_categoricals([a, b])
     ['a', 'b', 'a', 'b', 'a']
     Categories (2, object): ['a' < 'b']
 
     Raises `TypeError` because the categories are ordered and not identical.
 
     >>> a = pd.Categorical(["a", "b"], ordered=True)
     >>> b = pd.Categorical(["a", "b", "c"], ordered=True)
-    >>> union_categoricals([a, b])
+    >>> pd.api.types.union_categoricals([a, b])
     Traceback (most recent call last):
         ...
     TypeError: to union ordered Categoricals, all categories must be the same
@@ -247,7 +245,7 @@ def union_categoricals(
 
     >>> a = pd.Categorical(["a", "b", "c"], ordered=True)
     >>> b = pd.Categorical(["c", "b", "a"], ordered=True)
-    >>> union_categoricals([a, b], ignore_order=True)
+    >>> pd.api.types.union_categoricals([a, b], ignore_order=True)
     ['a', 'b', 'c', 'c', 'b', 'a']
     Categories (3, object): ['a', 'b', 'c']
 
@@ -257,7 +255,7 @@ def union_categoricals(
 
     >>> a = pd.Series(["b", "c"], dtype='category')
     >>> b = pd.Series(["a", "b"], dtype='category')
-    >>> union_categoricals([a, b])
+    >>> pd.api.types.union_categoricals([a, b])
     ['b', 'c', 'a', 'b']
     Categories (3, object): ['b', 'c', 'a']
     """

pandas/io/stata.py

@@ -3157,21 +3157,22 @@ class StataWriter117(StataWriter):
 
     Examples
     --------
-    >>> from pandas.io.stata import StataWriter117
     >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
-    >>> writer = StataWriter117('./data_file.dta', data)
+    >>> writer = pd.io.stata.StataWriter117('./data_file.dta', data)
     >>> writer.write_file()
 
     Directly write a zip file
     >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
-    >>> writer = StataWriter117('./data_file.zip', data, compression=compression)
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file.zip', data, compression=compression
+    ...     )
     >>> writer.write_file()
 
     Or with long strings stored in strl format
     >>> data = pd.DataFrame([['A relatively long string'], [''], ['']],
     ...                     columns=['strls'])
-    >>> writer = StataWriter117('./data_file_with_long_strings.dta', data,
-    ...                         convert_strl=['strls'])
+    >>> writer = pd.io.stata.StataWriter117(
+    ...     './data_file_with_long_strings.dta', data, convert_strl=['strls'])
     >>> writer.write_file()
     """