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pushthetempo
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astana_v2

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Tabular Classification

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train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
partitionScheme: string
prefix: string
description: string
parts: struct<2026-06-17T05: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
  child 0, 2026-06-17T05: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string>
      child 0, path: string
      child 1, rowCount: int64
      child 2, hourKey: timestamp[s]
      child 3, updatedAt: string
totalRows: int64
updatedAt: string
eventTime: string
stage: string
sourceName: string
transport: string
Latitude: double
sourceMode: string
city: string
Severity: string
trafficDensity: double
sourceId: string
location: string
vehicleType: string
eventType: string
streamKind: string
Speed_kmh: int64
sourceType: string
event_type: string
businessResult: string
sensorType: string
sensorKind: string
severity: string
Event_Type: string
longitude: double
Longitude: double
timestamp: string
vehicle_type: string
latitude: double
confidence: double
Traffic_Density: double
derivedFrom: string
activeModelId: string
speed_kmh: int64
traffic_density: double
eventId: string
speedKmh: int64
sourceTimestamp: string
to
{'stage': Value('string'), 'eventId': Value('string'), 'eventTime': Value('string'), 'sourceId': Value('string'), 'sourceName': Value('string'), 'sourceType': Value('string'), 'sourceMode': Value('string'), 'sensorType': Value('string'), 'location': Value('string'), 'activeModelId': Value('string'), 'city': Value('string'), 'Latitude': Value('float64'), 'Severity': Value('string'), 'latitude': Value('float64'), 'severity': Value('string'), 'speedKmh': Value('int64'), 'Longitude': Value('float64'), 'Speed_kmh': Value('int64'), 'eventType': Value('string'), 'longitude': Value('float64'), 'speed_kmh': Value('int64'), 'timestamp': Value('string'), 'transport': Value('string'), 'Event_Type': Value('string'), 'confidence': Value('float64'), 'event_type': Value('string'), 'sensorKind': Value('string'), 'streamKind': Value('string'), 'derivedFrom': Value('string'), 'vehicleType': Value('string'), 'vehicle_type': Value('string'), 'trafficDensity': Value('float64'), 'Traffic_Density': Value('float64'), 'sourceTimestamp': Value('string'), 'traffic_density': Value('float64'), 'businessResult': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              partitionScheme: string
              prefix: string
              description: string
              parts: struct<2026-06-17T05: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
                child 0, 2026-06-17T05: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string>
                    child 0, path: string
                    child 1, rowCount: int64
                    child 2, hourKey: timestamp[s]
                    child 3, updatedAt: string
              totalRows: int64
              updatedAt: string
              eventTime: string
              stage: string
              sourceName: string
              transport: string
              Latitude: double
              sourceMode: string
              city: string
              Severity: string
              trafficDensity: double
              sourceId: string
              location: string
              vehicleType: string
              eventType: string
              streamKind: string
              Speed_kmh: int64
              sourceType: string
              event_type: string
              businessResult: string
              sensorType: string
              sensorKind: string
              severity: string
              Event_Type: string
              longitude: double
              Longitude: double
              timestamp: string
              vehicle_type: string
              latitude: double
              confidence: double
              Traffic_Density: double
              derivedFrom: string
              activeModelId: string
              speed_kmh: int64
              traffic_density: double
              eventId: string
              speedKmh: int64
              sourceTimestamp: string
              to
              {'stage': Value('string'), 'eventId': Value('string'), 'eventTime': Value('string'), 'sourceId': Value('string'), 'sourceName': Value('string'), 'sourceType': Value('string'), 'sourceMode': Value('string'), 'sensorType': Value('string'), 'location': Value('string'), 'activeModelId': Value('string'), 'city': Value('string'), 'Latitude': Value('float64'), 'Severity': Value('string'), 'latitude': Value('float64'), 'severity': Value('string'), 'speedKmh': Value('int64'), 'Longitude': Value('float64'), 'Speed_kmh': Value('int64'), 'eventType': Value('string'), 'longitude': Value('float64'), 'speed_kmh': Value('int64'), 'timestamp': Value('string'), 'transport': Value('string'), 'Event_Type': Value('string'), 'confidence': Value('float64'), 'event_type': Value('string'), 'sensorKind': Value('string'), 'streamKind': Value('string'), 'derivedFrom': Value('string'), 'vehicleType': Value('string'), 'vehicle_type': Value('string'), 'trafficDensity': Value('float64'), 'Traffic_Density': Value('float64'), 'sourceTimestamp': Value('string'), 'traffic_density': Value('float64'), 'businessResult': Value('string')}
              because column names don't match

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Flowmatic Smart City Dataset

Flowmatic smart-city dataset with hourly UTC CSV partitions. 1 hour file(s), 143 total row(s). Files live under data/hourly/ and are indexed in data/hourly/manifest.json.

Pipeline run: cmqhlxrr708tno42sajnwwxf6
Updated: 2026-06-17T05:05:27.655Z
Partition scheme: hourly-utc
Manifest: data/hourly/manifest.json

Hourly CSV layout

Rows are grouped by eventTime into one CSV per UTC hour:

Directory: data/hourly/
File pattern: YYYY-MM-DDTHH.csv (example: data/hourly/2026-05-21T14.csv)
Append exports merge only within the touched hour file(s), not the whole dataset.

Hour (UTC)	File	Rows	Updated
2026-06-17 05:00–05:59 UTC	`data/hourly/2026-06-17T05.csv`	143	2026-06-17T05:05:27.655Z

Preview (latest exported rows)

stage	eventId	eventTime	sourceId	sourceName	sourceType	sourceMode	sensorType	location	activeModelId	city	Latitude	Severity	latitude	severity	speedKmh	Longitude	Speed_kmh	eventType	longitude	speed_kmh	timestamp	transport	Event_Type	confidence	event_type	sensorKind	streamKind	derivedFrom	vehicleType	vehicle_type	trafficDensity	Traffic_Density	sourceTimestamp	traffic_density	businessResult
"business"	"AST-004073"	"2026-06-17T05:05:23.133Z"	"cmqhlqhey07upo42srcn4lw66"	"ast"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.08804	"Low"	51.08804	"Low"	11	71.431884	11	"Weather"	71.431884	11	"2026-06-17T05:05:23.133Z"	"WEBSOCKET"	"Weather"	0.976	"Weather"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Truck"	"Truck"	62.96	62.96	"2024-09-21T14:06:30.610Z"	62.96	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-004071"	"2026-06-17T05:05:22.133Z"	"cmqhlqhey07upo42srcn4lw66"	"ast"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.160878	"Medium"	51.160878	"Medium"	24	71.363354	24	"Congestion"	71.363354	24	"2026-06-17T05:05:22.132Z"	"WEBSOCKET"	"Congestion"	0.882	"Congestion"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Bus"	"Bus"	59.01	59.01	"2024-07-21T23:26:58.125Z"	59.01	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-004069"	"2026-06-17T05:05:21.133Z"	"cmqhlqhey07upo42srcn4lw66"	"ast"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.248194	"Medium"	51.248194	"Medium"	63	71.388475	63	"Congestion"	71.388475	63	"2026-06-17T05:05:21.132Z"	"WEBSOCKET"	"Congestion"	0.899	"Congestion"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Motorcycle"	"Motorcycle"	103.69	103.69	"2024-07-11T00:42:11.189Z"	103.69	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-004067"	"2026-06-17T05:05:20.131Z"	"cmqhlqhey07upo42srcn4lw66"	"ast"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.152615	"High"	51.152615	"High"	5	71.413434	5	"Roadwork"	71.413434	5	"2026-06-17T05:05:20.131Z"	"WEBSOCKET"	"Roadwork"	0.984	"Roadwork"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Truck"	"Truck"	106.48	106.48	"2024-07-25T17:16:09.055Z"	106.48	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"
"business"	"AST-004065"	"2026-06-17T05:05:19.131Z"	"cmqhlqhey07upo42srcn4lw66"	"ast"	"WEBSOCKET"	"SIMULATED"	"traffic"	"Astana"	"production:q1_astana_tranad_anomaly_detector"	"Astana"	51.169566	"Medium"	51.169566	"Medium"	10	71.395201	10	"Weather"	71.395201	10	"2026-06-17T05:05:19.130Z"	"WEBSOCKET"	"Weather"	0.985	"Weather"	"traffic"	"astana_semi_synthetic"	"astana_synthetic_data.csv"	"Motorcycle"	"Motorcycle"	27.39	27.39	"2024-07-20T12:04:51.648Z"	27.39	"{"activeModelId":"production:q1_astana_tranad_anomaly_detector","skipped":true,"reason":"Select a Hugging Face model in the core unit configuration"}"

Usage

from datasets import load_dataset

# Loads all hourly CSV parts as one split
ds = load_dataset("pushthetempo/astana_v2", data_files="data/hourly/*.csv", split="train")

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Total file size:

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