> 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/aggregated-queries.md). # Aggregated Queries ### Daily Min & Max for a location {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "point", "value": [47.3784, 8.5387], # [latitude, longitude] - Zurich "method": "nearest", }, "variables": [ "air_temperature_at_height_level_2m", "wind_speed_at_height_level_100m", ], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 72}, # 0-72 hours ahead } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"✅ Retrieved {len(df)} rows") print(df.head()) ``` {% endtab %} {% tab title="Output" %} ``` ✅ Retrieved 73 rows model init_time ... air_temperature_at_height_level_2m wind_speed_at_height_level_100m 0 ept2 2025-10-23T06:00:00Z ... 284.600006 4.7 1 ept2 2025-10-23T06:00:00Z ... 285.399994 5.0 2 ept2 2025-10-23T06:00:00Z ... 286.399994 4.8 3 ept2 2025-10-23T06:00:00Z ... 286.500000 9.3 4 ept2 2025-10-23T06:00:00Z ... 284.800018 10.8 [5 rows x 7 columns] ``` {% endtab %} {% endtabs %} ### Hourly Wind Statistics in Germany {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "market_zone", "value": "DE", # Germany }, "variables": ["wind_speed_at_height_level_100m"], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 72}, # 3 days "group_by": [ { # Explicit use of the "to_start_of" transformation "field": "time", "transformation": "to_start_of", "parameters_list": ["hour"], } ], "aggregation": [ {"aggregation": "max"}, "min", "avg", "std", "quantile_(0.05)", "quantile_(0.95)", ], "include_time": True, "order_by": ["time"], } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"Retrieved {len(df)} rows") print(df.head(20)) return df ``` {% endtab %} {% tab title="Output" %} ``` Retrieved 73 rows time max__wind_speed_at_height_level_100m min__wind_speed_at_height_level_100m ... std__wind_speed_at_height_level_100m quantile_(0.05)__wind_speed_at_height_level_100m quantile_(0.95)__wind_speed_at_height_level_100m 0 2025-10-23T06:00:00Z 16.500000 2.2 ... 1.972303 6.400000 13.000000 1 2025-10-23T07:00:00Z 14.400001 4.3 ... 1.616050 6.800000 12.300000 2 2025-10-23T08:00:00Z 14.200000 4.4 ... 1.601147 6.600000 12.100000 3 2025-10-23T09:00:00Z 14.300000 3.5 ... 1.763546 5.900000 12.100000 4 2025-10-23T10:00:00Z 14.800000 2.5 ... 1.986278 4.800000 12.100000 5 2025-10-23T11:00:00Z 16.900000 0.8 ... 2.325559 4.300000 13.200000 6 2025-10-23T12:00:00Z 18.500000 0.3 ... 2.384850 7.200000 14.200000 7 2025-10-23T13:00:00Z 18.400000 3.0 ... 2.306951 7.400000 15.000000 8 2025-10-23T14:00:00Z 18.700001 2.8 ... 2.426426 7.300000 15.200000 9 2025-10-23T15:00:00Z 18.700001 2.0 ... 2.556340 7.100000 15.200000 10 2025-10-23T16:00:00Z 18.900000 2.0 ... 2.614214 7.100000 15.300000 11 2025-10-23T17:00:00Z 18.200001 2.1 ... 2.600091 7.500000 15.400001 12 2025-10-23T18:00:00Z 17.100000 2.4 ... 2.495059 7.600000 15.500000 13 2025-10-23T19:00:00Z 16.600000 3.0 ... 2.329726 7.900000 15.500000 14 2025-10-23T20:00:00Z 16.500000 3.0 ... 2.047417 8.500000 15.000000 15 2025-10-23T21:00:00Z 15.800000 3.4 ... 1.769095 8.700000 14.700000 16 2025-10-23T22:00:00Z 15.900001 3.8 ... 1.552549 8.800000 14.100000 17 2025-10-23T23:00:00Z 17.100000 4.1 ... 1.521312 8.900001 13.900001 18 2025-10-24T00:00:00Z 17.900000 4.1 ... 1.475617 9.000000 13.600000 19 2025-10-24T01:00:00Z 18.200001 4.4 ... 1.409908 9.200000 13.500000 [20 rows x 7 columns] ``` {% endtab %} {% endtabs %} ### Capacity Weighted Average Wind Speed in Germany {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "market_zone", "value": "DE", # Germany }, "variables": ["wind_speed_at_height_level_100m"], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 72}, # 3 days "weighting": {"type": "wind_capacity"}, "group_by": [ { "field": "time", "transformation": "to_start_of", "parameters_list": ["hour"], } ], "aggregation": [{"aggregation": "avg"}], "include_time": True, "order_by": ["time"], } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"Retrieved {len(df)} rows") print(df.head(20)) ``` {% endtab %} {% tab title="Output" %} ``` Retrieved 73 rows time avg__wind_speed_at_height_level_100m 0 2025-10-23T06:00:00Z 10.473292 1 2025-10-23T07:00:00Z 10.559825 2 2025-10-23T08:00:00Z 10.471903 3 2025-10-23T09:00:00Z 10.234161 4 2025-10-23T10:00:00Z 9.740825 5 2025-10-23T11:00:00Z 9.817058 6 2025-10-23T12:00:00Z 9.906179 7 2025-10-23T13:00:00Z 10.051497 8 2025-10-23T14:00:00Z 10.096489 9 2025-10-23T15:00:00Z 10.191436 10 2025-10-23T16:00:00Z 10.326234 11 2025-10-23T17:00:00Z 10.489385 12 2025-10-23T18:00:00Z 10.661165 13 2025-10-23T19:00:00Z 10.938301 14 2025-10-23T20:00:00Z 11.160419 15 2025-10-23T21:00:00Z 11.237437 16 2025-10-23T22:00:00Z 11.150809 17 2025-10-23T23:00:00Z 11.313920 18 2025-10-24T00:00:00Z 11.484743 19 2025-10-24T01:00:00Z 11.695437 ``` {% endtab %} {% endtabs %} ### Capacity Weighted Average in a Polygon {% tabs %} {% tab title="Example (Python)" %} ```python query = { "models": ["ept2"], "geo": { "type": "polygon", "value": [ [ [53.0, 8.0], [53.0, 12.0], [55.0, 12.0], [55.0, 8.0], [53.0, 8.0], ] ], }, "variables": ["wind_speed_at_height_level_100m"], "init_time": "latest", "prediction_timedelta": {"start": 0, "end": 48}, # 2 days "weighting": {"type": "wind_capacity"}, "group_by": [ { "field": "time", "transformation": "to_start_of", "parameters_list": ["hour"], } ], "aggregation": [{"aggregation": "avg"}], "include_time": True, "order_by": ["time"], } response = requests.post(url, headers=headers, json=query) response.raise_for_status() data = response.json() df = pd.DataFrame(data) print(f"Retrieved {len(df)} rows") print(df.head(20)) ``` {% endtab %} {% tab title="Output" %} ``` Retrieved 49 rows time avg__wind_speed_at_height_level_100m 0 2025-10-23T06:00:00Z 11.385012 1 2025-10-23T07:00:00Z 11.701327 2 2025-10-23T08:00:00Z 11.644365 3 2025-10-23T09:00:00Z 11.242075 4 2025-10-23T10:00:00Z 10.356984 5 2025-10-23T11:00:00Z 9.627563 6 2025-10-23T12:00:00Z 8.863163 7 2025-10-23T13:00:00Z 8.323935 8 2025-10-23T14:00:00Z 7.914524 9 2025-10-23T15:00:00Z 7.662574 10 2025-10-23T16:00:00Z 7.734694 11 2025-10-23T17:00:00Z 8.140329 12 2025-10-23T18:00:00Z 8.584207 13 2025-10-23T19:00:00Z 8.879365 14 2025-10-23T20:00:00Z 9.181939 15 2025-10-23T21:00:00Z 9.383254 16 2025-10-23T22:00:00Z 9.735419 17 2025-10-23T23:00:00Z 10.298345 18 2025-10-24T00:00:00Z 10.977109 19 2025-10-24T01:00:00Z 11.730488 ``` {% endtab %} {% endtabs %} --- # 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