ENH: pd.Series.shift and .diff to accept a collection of numbers

doc/source/whatsnew/v1.4.0.rst

@@ -186,6 +186,35 @@ representation of :class:`DataFrame` objects (:issue:`4889`).
     df
     df.to_dict(orient='tight')
 
+.. _whatsnew_140.enhancements.shift:
+
+DataFrame.shift and Series.shift now accept an iterable for parameter ``'period'``
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :meth:`DataFrame.shift` and :meth:`Series.shift` functions can take in an iterable, such as a list, for the period parameter. When an iterable is passed
+to either function it returns a :class:`DataFrame` object with all of the shifted rows or columns concatenated with one another.
+The function applies a shift designated by each element in the iterable. The resulting :class:`DataFrame` object's columns will retain the
+names from the :class:`DataFrame` object that called shift, but postfixed with <name>_<num>, where name is the original
+column name and num correlates to the current element of the period iterable (:issue:`44424`).
+
+Usage within the :class:`DataFrame` class:
+.. ipython:: python
+
+    df = pd.DataFrame({
+        'a': [1, 2, 3],
+        'b': [4, 5, 6]
+    })
+    shifts = [0, 1, 2]
+    df.shift(shifts)
+
+Usage within the :class:`Series` class:
+.. ipython:: python
+
+    ser = pd.Series([1, 2, 3])
+    shifts = [0, 1, 2]
+
+    ser.shift(shifts)
+
 .. _whatsnew_140.enhancements.other:
 
 Other enhancements

pandas/core/frame.py

@@ -5444,6 +5444,34 @@ def shift(
     ) -> DataFrame:
         axis = self._get_axis_number(axis)
 
+        # Handle the case of multiple shifts
+        if is_list_like(periods):
+
+            new_df = DataFrame()
+
+            from pandas.core.reshape.concat import concat
+
+            for i in periods:
+                if not isinstance(i, int):
+                    raise TypeError(
+                        f"Value {i} in periods is not an integer, expected an integer"
+                    )
+
+                new_df = concat(
+                    [
+                        new_df,
+                        super()
+                        .shift(periods=i, freq=freq, axis=axis, fill_value=fill_value)
+                        .add_suffix(f"_{i}"),
+                    ],
+                    axis=1,
+                )
+
+            if new_df.empty:
+                return self
+
+            return new_df
+
         ncols = len(self.columns)
         if axis == 1 and periods != 0 and fill_value is lib.no_default and ncols > 0:
             # We will infer fill_value to match the closest column