DOC: Changed M to MS in examples (#53927)

Changed M to MS in examples

Author: DeaMariaLeon

pandas/core/groupby/groupby.py

@@ -764,8 +764,8 @@ def groups(self) -> dict[Hashable, np.ndarray]:
         2023-02-01    3
         2023-02-15    4
         dtype: int64
-        >>> ser.resample('M').groups
-        {Timestamp('2023-01-31 00:00:00'): 2, Timestamp('2023-02-28 00:00:00'): 4}
+        >>> ser.resample('MS').groups
+        {Timestamp('2023-01-01 00:00:00'): 2, Timestamp('2023-02-01 00:00:00'): 4}
         """
         return self.grouper.groups
 
@@ -818,9 +818,9 @@ def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]:
         2023-02-01    3
         2023-02-15    4
         dtype: int64
-        >>> ser.resample('M').indices
-        defaultdict(<class 'list'>, {Timestamp('2023-01-31 00:00:00'): [0, 1],
-        Timestamp('2023-02-28 00:00:00'): [2, 3]})
+        >>> ser.resample('MS').indices
+        defaultdict(<class 'list'>, {Timestamp('2023-01-01 00:00:00'): [0, 1],
+        Timestamp('2023-02-01 00:00:00'): [2, 3]})
         """
         return self.grouper.indices
 
@@ -1003,7 +1003,7 @@ def get_group(self, name, obj=None) -> DataFrame | Series:
         2023-02-01    3
         2023-02-15    4
         dtype: int64
-        >>> ser.resample('M').get_group('2023-01-31')
+        >>> ser.resample('MS').get_group('2023-01-01')
         2023-01-01    1
         2023-01-15    2
         dtype: int64
@@ -1085,13 +1085,13 @@ def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]:
         2023-02-01    3
         2023-02-15    4
         dtype: int64
-        >>> for x, y in ser.resample('M'):
+        >>> for x, y in ser.resample('MS'):
         ...     print(f'{x}\\n{y}\\n')
-        2023-01-31 00:00:00
+        2023-01-01 00:00:00
         2023-01-01    1
         2023-01-15    2
         dtype: int64
-        2023-02-28 00:00:00
+        2023-02-01 00:00:00
         2023-02-01    3
         2023-02-15    4
         dtype: int64

pandas/core/resample.py

@@ -519,10 +519,10 @@ def ffill(self, limit: int | None = None):
 
         Example for ``ffill`` with downsampling (we have fewer dates after resampling):
 
-        >>> ser.resample('M').ffill()
-        2023-01-31    2
-        2023-02-28    4
-        Freq: M, dtype: int64
+        >>> ser.resample('MS').ffill()
+        2023-01-01    1
+        2023-02-01    3
+        Freq: MS, dtype: int64
 
         Example for ``ffill`` with upsampling (fill the new dates with
         the previous value):