# Regional Queries {% hint style="warning" %} Regional queries without aggregation can end-up consuming a lot of credits. The `request_credit_limit` was increased for all of the example queries. Use with care! {% endhint %} ### Countries & Market Zones #### Country {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "country_key", "value": "CH", # Switzerland }, "variables": ["air_temperature_at_height_level_2m"], "init_time": "latest", "prediction_timedelta": [0, 24, 48], # 0, 1 day, 2 days "order_by": [ "model", "init_time", "prediction_timedelta", "latitude", "longitude", ], } # Query parameters for arrow format and credit limit params = {"format": "arrow", "request_credit_limit": 100} response = requests.post(url, headers=headers, json=query, params=params) response.raise_for_status() # Read arrow format response arrow_buffer = pa.py_buffer(response.content) with pa_ipc.open_stream(arrow_buffer) as reader: table = reader.read_all() df = table.to_pandas() print(f"✅ Retrieved {len(df)} rows for Switzerland") print(df.head()) ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 2106 rows for Switzerland model init_time ... longitude air_temperature_at_height_level_2m 0 ept2 2025-10-23 06:00:00 ... 8.916667 284.200012 1 ept2 2025-10-23 06:00:00 ... 7.083333 273.100006 2 ept2 2025-10-23 06:00:00 ... 7.166667 273.300018 3 ept2 2025-10-23 06:00:00 ... 7.250000 273.100006 4 ept2 2025-10-23 06:00:00 ... 8.916667 283.899994 [5 rows x 6 columns] ``` {% endtab %} {% endtabs %} #### Market Zone {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "market_zone", "value": ["GB-NIR"], # Ireland and Northern Ireland }, "variables": ["wind_speed_at_height_level_100m"], "init_time": "latest", "prediction_timedelta": [0, 24], # 0 and 1 day ahead "order_by": [ "model", "init_time", "prediction_timedelta", "latitude", "longitude", ], } # Query parameters for arrow format and credit limit params = {"format": "arrow", "request_credit_limit": 100} response = requests.post(url, headers=headers, json=query, params=params) response.raise_for_status() # Read arrow format response arrow_buffer = pa.py_buffer(response.content) with pa_ipc.open_stream(arrow_buffer) as reader: table = reader.read_all() df = table.to_pandas() print(f"✅ Retrieved {len(df)} rows for Ireland and Northern Ireland") print(df.head()) return df ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 572 rows for Ireland and Northern Ireland model init_time ... longitude wind_speed_at_height_level_100m 0 ept2 2025-10-23 06:00:00 ... -6.666667 10.300000 1 ept2 2025-10-23 06:00:00 ... -6.583333 10.200000 2 ept2 2025-10-23 06:00:00 ... -6.500000 9.900001 3 ept2 2025-10-23 06:00:00 ... -6.416667 9.900001 4 ept2 2025-10-23 06:00:00 ... -6.166667 9.900001 [5 rows x 6 columns] ``` {% endtab %} {% endtabs %} ### Bounding Boxes {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "bounding_box", "value": [ [ [45.8, 5.9], # [lat_min, lon_min] - Southwest corner [47.8, 10.5], # [lat_max, lon_max] - Northeast corner ] ], # Switzerland region }, "variables": ["air_temperature_at_height_level_2m"], "init_time": "latest", "prediction_timedelta": [0], # IC only "order_by": [ "model", "init_time", "prediction_timedelta", "latitude", "longitude", ], } # Query parameters for arrow format and credit limit params = {"format": "arrow", "request_credit_limit": 150} response = requests.post(url, headers=headers, json=query, params=params) response.raise_for_status() # Read arrow format response arrow_buffer = pa.py_buffer(response.content) with pa_ipc.open_stream(arrow_buffer) as reader: table = reader.read_all() df = table.to_pandas() print(f"✅ Retrieved {len(df)} rows for bounding box") print(df.head()) return df ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 1344 rows for bounding box model init_time ... longitude air_temperature_at_height_level_2m 0 ept2 2025-10-23 06:00:00 ... 5.916667 287.000000 1 ept2 2025-10-23 06:00:00 ... 6.000000 286.700012 2 ept2 2025-10-23 06:00:00 ... 6.083333 286.200012 3 ept2 2025-10-23 06:00:00 ... 6.166667 285.399994 4 ept2 2025-10-23 06:00:00 ... 6.250000 284.200012 [5 rows x 6 columns] ``` {% endtab %} {% endtabs %} ### Polygons {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "polygon", "value": [ [ [47.3, 8.0], # Southwest corner [47.6, 8.0], # Northwest corner [47.6, 8.8], # Northeast corner [47.3, 8.8], # Southeast corner [47.3, 8.0], # Close the polygon (same as first point) ] ], # Polygon around Zurich region }, "variables": [ "air_temperature_at_height_level_2m", ], "init_time": "latest", "prediction_timedelta": [0], # IC only "order_by": [ "model", "init_time", "prediction_timedelta", "latitude", "longitude", ], } # Query parameters for arrow format and credit limit params = {"format": "arrow", "request_credit_limit": 100} response = requests.post(url, headers=headers, json=query, params=params) response.raise_for_status() # Read arrow format response arrow_buffer = pa.py_buffer(response.content) with pa_ipc.open_stream(arrow_buffer) as reader: table = reader.read_all() df = table.to_pandas() print(f"✅ Retrieved {len(df)} rows for polygon") print(df.head()) return df ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 40 rows for polygon model init_time ... longitude air_temperature_at_height_level_2m 0 ept2 2025-10-23 06:00:00 ... 8.000000 285.100006 1 ept2 2025-10-23 06:00:00 ... 8.083333 284.600006 2 ept2 2025-10-23 06:00:00 ... 8.166667 284.200012 3 ept2 2025-10-23 06:00:00 ... 8.250000 283.899994 4 ept2 2025-10-23 06:00:00 ... 8.333333 283.800018 [5 rows x 6 columns] ``` {% endtab %} {% endtabs %}