> For the complete documentation index, see [llms.txt](https://docs.jua.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.jua.ai/api-v2/query-engine/examples/regional-queries.md). # 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"], # 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 Northern Ireland") print(df.head()) return df ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 572 rows for 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 %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.jua.ai/api-v2/query-engine/examples/regional-queries.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.