Properly handle timestamps from "hourly" resolution data set.

Hourly data has a timestamp format of e.g. "2018121300".
So, let's parse it using a pattern like "%Y%m%d%H".

python_dwd/additionals/helpers.py

@@ -303,3 +303,7 @@ def create_stationdata_dtype_mapping(columns: List[str]) -> dict:
             stationdata_dtype_mapping[column] = float
 
     return stationdata_dtype_mapping
+
+
+def convert_datetime_hourly(value):
+    return pd.to_datetime(value, format='%Y%m%d%H')

python_dwd/parsing_data/parse_data_from_files.py

@@ -4,9 +4,10 @@
 from io import BytesIO
 import pandas as pd
 
-from python_dwd.additionals.helpers import create_stationdata_dtype_mapping
+from python_dwd.additionals.helpers import create_stationdata_dtype_mapping, convert_datetime_hourly
 from python_dwd.constants.column_name_mapping import GERMAN_TO_ENGLISH_COLUMNS_MAPPING
 from python_dwd.constants.metadata import NA_STRING, STATIONDATA_SEP
+from python_dwd.enumerations.column_names_enumeration import DWDMetaColumns
 
 log = logging.getLogger(__name__)
 
@@ -78,6 +79,9 @@ def _parse_dwd_data(filename_and_file: Tuple[str, BytesIO]) -> pd.DataFrame:
     # Assign meaningful column names (baseline).
     data = data.rename(columns=GERMAN_TO_ENGLISH_COLUMNS_MAPPING)
 
+    # Properly handle timestamps from "hourly" resolution.
+    data[DWDMetaColumns.DATE.value] = data[DWDMetaColumns.DATE.value].apply(convert_datetime_hourly)
+
     # Coerce the data types appropriately.
     data = data.astype(create_stationdata_dtype_mapping(data.columns))