TST: Fixing broken doctests in pandas/core

ci/code_checks.sh

@@ -107,44 +107,20 @@ fi
 ### DOCTESTS ###
 if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
 
-    MSG='Doctests for individual files' ; echo $MSG
-    pytest -q --doctest-modules \
-      pandas/core/accessor.py \
-      pandas/core/aggregation.py \
-      pandas/core/algorithms.py \
-      pandas/core/base.py \
-      pandas/core/construction.py \
-      pandas/core/frame.py \
-      pandas/core/generic.py \
-      pandas/core/indexers.py \
-      pandas/core/nanops.py \
-      pandas/core/series.py \
-      pandas/io/sql.py
-    RET=$(($RET + $?)) ; echo $MSG "DONE"
-
-    MSG='Doctests for directories' ; echo $MSG
-    pytest -q --doctest-modules \
+    MSG='Doctests' ; echo $MSG
+    python -m pytest --doctest-modules \
       pandas/_libs/ \
       pandas/api/ \
       pandas/arrays/ \
       pandas/compat/ \
-      pandas/core/array_algos/ \
-      pandas/core/arrays/ \
-      pandas/core/computation/ \
-      pandas/core/dtypes/ \
-      pandas/core/groupby/ \
-      pandas/core/indexes/ \
-      pandas/core/ops/ \
-      pandas/core/reshape/ \
-      pandas/core/strings/ \
-      pandas/core/tools/ \
-      pandas/core/window/ \
+      pandas/core \
       pandas/errors/ \
       pandas/io/clipboard/ \
       pandas/io/json/ \
       pandas/io/excel/ \
       pandas/io/parsers/ \
       pandas/io/sas/ \
+      pandas/io/sql.py \
       pandas/tseries/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 

pandas/core/flags.py

@@ -68,9 +68,9 @@ def allows_duplicate_labels(self) -> bool:
         Examples
         --------
         >>> df = pd.DataFrame({"A": [1, 2]}, index=['a', 'a'])
-        >>> df.allows_duplicate_labels
+        >>> df.flags.allows_duplicate_labels
         True
-        >>> df.allows_duplicate_labels = False
+        >>> df.flags.allows_duplicate_labels = False
         Traceback (most recent call last):
             ...
         pandas.errors.DuplicateLabelError: Index has duplicates.

pandas/core/internals/construction.py

@@ -712,13 +712,14 @@ def dataclasses_to_dicts(data):
 
     Examples
     --------
+    >>> from dataclasses import dataclass
     >>> @dataclass
-    >>> class Point:
+    ... class Point:
     ...     x: int
     ...     y: int
 
-    >>> dataclasses_to_dicts([Point(1,2), Point(2,3)])
-    [{"x":1,"y":2},{"x":2,"y":3}]
+    >>> dataclasses_to_dicts([Point(1, 2), Point(2, 3)])
+    [{'x': 1, 'y': 2}, {'x': 2, 'y': 3}]
 
     """
     from dataclasses import asdict

pandas/core/resample.py

@@ -286,8 +286,9 @@ def pipe(
         """
     Examples
     --------
-    >>> s = pd.Series([1,2,3,4,5],
-                      index=pd.date_range('20130101', periods=5,freq='s'))
+    >>> s = pd.Series([1, 2, 3, 4, 5],
+    ...               index=pd.date_range('20130101', periods=5, freq='s'))
+    >>> s
     2013-01-01 00:00:00    1
     2013-01-01 00:00:01    2
     2013-01-01 00:00:02    3
@@ -296,27 +297,25 @@ def pipe(
     Freq: S, dtype: int64
 
     >>> r = s.resample('2s')
-    DatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,
-                            label=left, convention=start]
 
     >>> r.agg(np.sum)
     2013-01-01 00:00:00    3
     2013-01-01 00:00:02    7
     2013-01-01 00:00:04    5
     Freq: 2S, dtype: int64
 
-    >>> r.agg(['sum','mean','max'])
+    >>> r.agg(['sum', 'mean', 'max'])
                          sum  mean  max
     2013-01-01 00:00:00    3   1.5    2
     2013-01-01 00:00:02    7   3.5    4
     2013-01-01 00:00:04    5   5.0    5
 
-    >>> r.agg({'result' : lambda x: x.mean() / x.std(),
-               'total' : np.sum})
-                         total    result
-    2013-01-01 00:00:00      3  2.121320
-    2013-01-01 00:00:02      7  4.949747
-    2013-01-01 00:00:04      5       NaN
+    >>> r.agg({'result': lambda x: x.mean() / x.std(),
+    ...        'total': np.sum})
+                           result  total
+    2013-01-01 00:00:00  2.121320      3
+    2013-01-01 00:00:02  4.949747      7
+    2013-01-01 00:00:04       NaN      5
     """
     )
 
@@ -357,7 +356,20 @@ def transform(self, arg, *args, **kwargs):
 
         Examples
         --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: H, dtype: int64
+
+        >>> resampled = s.resample('15min')
         >>> resampled.transform(lambda x: (x - x.mean()) / x.std())
+        2018-01-01 00:00:00   NaN
+        2018-01-01 01:00:00   NaN
+        Freq: H, dtype: float64
         """
         return self._selected_obj.groupby(self.groupby).transform(arg, *args, **kwargs)