make available_date an optional field in the response of get_area_weighted_series_df()

Author: cn1036

groclient/client.py

@@ -2033,9 +2033,9 @@ def get_area_weighted_series_df(
         DataFrame
 
             Example::
-                start_date  value       end_date    available_date  region_id   item_id     metric_id   frequency_id    unit_id     source_id   weights
-            0   2016-04-26  0.502835    2016-04-26  2016-04-28      1215        321         70029       1               189         112         [{"weight_name": "Corn", "item_id": 274, "metric_id": 2120001, ...}, ...]
-            1   2016-04-27  0.509729    2016-04-27  2016-04-29      1215        321         70029       1               189         112         [{"weight_name": "Corn", "item_id": 274, "metric_id": 2120001, ...}, ...]
+                start_date  value       end_date    available_date(optional)  region_id   item_id     metric_id   frequency_id    unit_id     source_id     weights
+            0   2016-04-26  0.502835    2016-04-26  2016-04-28                  1215        321         70029           1           189         112         [{"weight_name": "Corn", "item_id": 274, "metric_id": 2120001, ...}, ...]
+            1   2016-04-27  0.509729    2016-04-27  2016-04-29                  1215        321         70029           1           189         112         [{"weight_name": "Corn", "item_id": 274, "metric_id": 2120001, ...}, ...]
         """
         return lib.get_area_weighted_series_df(
             self.access_token,

groclient/lib.py

@@ -926,6 +926,12 @@ def generate_payload_for_v2_area_weighting(
 
 
 def format_v2_area_weighting_response(response_content: Dict[str, Any]) -> pd.DataFrame:
+    DATETIME_COL_MAPPINGS = {
+        "start_date": "timestamp", # add start_date col which is equivalent to end_date
+        "end_date": "timestamp",
+        "available_date": "available_timestamp",
+    }
+
     try:
         data_points = response_content["data_points"]
         weighted_series_df = pd.DataFrame(data_points)
@@ -934,14 +940,10 @@ def format_v2_area_weighting_response(response_content: Dict[str, Any]) -> pd.Da
             return weighted_series_df
 
         # convert unix timestamps and rename date cols
-        datetime_col_mappings = {
-            "start_date": "timestamp", # add start_date col which is equivalent to end_date
-            "end_date": "timestamp",
-            "available_date": "available_timestamp",
-        }
-        for new_col, col in datetime_col_mappings.items():
-            weighted_series_df[new_col] = pd.to_datetime(weighted_series_df[col], unit='s').dt.strftime('%Y-%m-%d')
-        weighted_series_df = weighted_series_df.drop(columns=datetime_col_mappings.values())
+        for new_col, col in DATETIME_COL_MAPPINGS.items():
+            if col in weighted_series_df.columns:
+                weighted_series_df[new_col] = pd.to_datetime(weighted_series_df[col], unit='s').dt.strftime('%Y-%m-%d')
+        weighted_series_df = weighted_series_df.drop(columns=DATETIME_COL_MAPPINGS.values(), errors="ignore")
 
         # append selected fields of series metadata
         for key in ['item_id', 'metric_id', 'frequency_id', 'unit_id', 'source_id']: