docstrings

Author: simonjayhawkins

pandas/core/arrays/string_.py

@@ -80,7 +80,7 @@ class StringDtype(ExtensionDtype):
     Examples
     --------
     >>> pd.StringDtype()
-    StringDtype
+    string[python]
     """
 
     #: StringDtype.na_value uses pandas.NA
@@ -263,7 +263,7 @@ class StringArray(PandasArray):
     >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
     <StringArray>
     ['This is', 'some text', <NA>, 'data.']
-    Length: 4, dtype: string
+    Length: 4, dtype: string[python]
 
     Unlike arrays instantiated with ``dtype="object"``, ``StringArray``
     will convert the values to strings.
@@ -275,7 +275,7 @@ class StringArray(PandasArray):
     >>> pd.array(['1', 1], dtype="string")
     <StringArray>
     ['1', '1']
-    Length: 2, dtype: string
+    Length: 2, dtype: string[python]
 
     However, instantiating StringArrays directly with non-strings will raise an error.
 

pandas/core/arrays/string_arrow.py

@@ -117,7 +117,7 @@ class ArrowStringArray(OpsMixin, ExtensionArray, ObjectStringArrayMixin):
 
     Examples
     --------
-    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string[arrow]")
+    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string[pyarrow]")
     <ArrowStringArray>
     ['This is', 'some text', <NA>, 'data.']
     Length: 4, dtype: string[pyarrow]

pandas/core/generic.py

@@ -6134,12 +6134,12 @@ def convert_dtypes(
         2  3  z   <NA>  <NA>    20  200.0
 
         >>> dfn.dtypes
-        a      Int32
-        b     string
-        c    boolean
-        d     string
-        e      Int64
-        f    Float64
+        a              Int32
+        b     string[python]
+        c            boolean
+        d     string[python]
+        e              Int64
+        f            Float64
         dtype: object
 
         Start with a Series of strings and missing data represented by ``np.nan``.
@@ -6157,7 +6157,7 @@ def convert_dtypes(
         0       a
         1       b
         2    <NA>
-        dtype: string
+        dtype: string[python]
         """
         if self.ndim == 1:
             return self._convert_dtypes(