🎉 allow non datetimeindex for processing

tsflex/processing/series_pipeline.py

@@ -194,8 +194,7 @@ def process(
         for s in to_series_list(data):
             # Assert the assumptions we make!
             if len(s):
-                assert isinstance(s.index, pd.DatetimeIndex)
-            # TODO: also check monotonic increasing?
+                assert s.index.is_monotonic_increasing
 
             if s.name in self.get_required_series():
                 series_dict[str(s.name)] = s.copy() if copy else s

tsflex/processing/series_processor.py

@@ -321,9 +321,9 @@ def _handle_seriesprocessor_func_output(
         # Nothing has to be done! A pd.DataFrame can be added to a series_dict using
         # series_dict.update(df)
         # Note: converting this to a dictionary (to_dict()) is **very** inefficient!
-        # Assert that the DataFrame has a time-index
+        # Assert that the DataFrame has a time-index or a range-index
         if len(func_output):
-            assert isinstance(func_output.index, pd.DatetimeIndex)
+            assert isinstance(func_output.index, (pd.DatetimeIndex, pd.RangeIndex))
         # Assert that the DataFrame columns are named
         assert all(
             func_output.columns.values != [i for i in range(func_output.shape[1])]
@@ -334,9 +334,9 @@ def _handle_seriesprocessor_func_output(
         # Convert series to series_dict and return
         # => if func_output.name is in the required_dict, than the original series will
         #    be replaced by this new series.
-        # Assert that the series has a time-index
+        # Assert that the series has a time-index or a range-index
         if len(func_output):
-            assert isinstance(func_output.index, pd.DatetimeIndex)
+            assert isinstance(func_output.index, (pd.DatetimeIndex, pd.RangeIndex))
         # Assert (func_output.name is not None) | (len(required_dict) == 1)
         if func_output.name is None:
             # If a series without a name is returned that is constructed from just 1

tsflex/utils/data.py

@@ -42,9 +42,17 @@ def series_dict_to_df(series_dict: Dict[str, pd.Series]) -> pd.DataFrame:
         return pd.DataFrame(series_dict)
     # 1. Check if the time-indexes of the series are equal, to create the df efficiently
     try:
+
+        def _get_index_freq(index):  # type: ignore[no-untyped-def]
+            if isinstance(index, pd.DatetimeIndex):
+                return index.freq
+            elif isinstance(index, pd.RangeIndex):
+                return index.step
+            return None
+
         index_info = set(
             [
-                (s.index[0], s.index[-1], len(s), s.index.freq)
+                (s.index[0], s.index[-1], len(s), _get_index_freq(s.index))
                 for s in series_dict.values()
             ]
         )