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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<2026-05-26T15: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
  child 0, 2026-05-26T15: 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
timestamp: string
sourceType: string
eventId: string
location: string
sensorType: string
stage: string
transport: string
confidence: double
openSpaces: int64
sensorKind: string
occupancyPercent: int64
sourceName: string
eventTime: string
sourceId: string
sourceMode: 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'), 'timestamp': Value('string'), 'transport': Value('string'), 'confidence': Value('float64'), 'openSpaces': Value('int64'), 'sensorKind': Value('string'), 'occupancyPercent': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              partitionScheme: string
              prefix: string
              description: string
              parts: struct<2026-05-26T15: struct<path: string, rowCount: int64, hourKey: timestamp[s], updatedAt: string (... 2 chars omitted)
                child 0, 2026-05-26T15: 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
              timestamp: string
              sourceType: string
              eventId: string
              location: string
              sensorType: string
              stage: string
              transport: string
              confidence: double
              openSpaces: int64
              sensorKind: string
              occupancyPercent: int64
              sourceName: string
              eventTime: string
              sourceId: string
              sourceMode: 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'), 'timestamp': Value('string'), 'transport': Value('string'), 'confidence': Value('float64'), 'openSpaces': Value('int64'), 'sensorKind': Value('string'), 'occupancyPercent': Value('int64')}
              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), 65 total row(s). Files live under data/hourly/ and are indexed in data/hourly/manifest.json.

Pipeline run: cmpms7pcx00b7my3cmrccyl9h
Updated: 2026-05-26T15:20:15.339Z
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
2026-05-26 15:00–15:59 UTC data/hourly/2026-05-26T15.csv 65 2026-05-26T15:20:15.339Z

Preview (latest exported rows)

stage eventId eventTime sourceId sourceName sourceType sourceMode sensorType location timestamp transport confidence openSpaces sensorKind occupancyPercent
"cleaned" "cmpms7p3400b5my3c3qt2da2d" "2026-05-26T15:20:12.735Z" "cmpms3j580003my3cx0klbxa5" "astana" "WEBSOCKET" "SIMULATED" "parking" "Astana" "2026-05-26T15:20:12.735Z" "WEBSOCKET" 0.865 83 "parking" 66
"cleaned" "cmpms7obb00b3my3cw1268m2d" "2026-05-26T15:20:11.735Z" "cmpms3j580003my3cx0klbxa5" "astana" "WEBSOCKET" "SIMULATED" "parking" "Astana" "2026-05-26T15:20:11.735Z" "WEBSOCKET" 0.909 33 "parking" 29
"cleaned" "cmpms7njj00b1my3co4qz5ob7" "2026-05-26T15:20:10.734Z" "cmpms3j580003my3cx0klbxa5" "astana" "WEBSOCKET" "SIMULATED" "parking" "Astana" "2026-05-26T15:20:10.734Z" "WEBSOCKET" 0.92 27 "parking" 63
"cleaned" "cmpms7mrq00azmy3chajjug8z" "2026-05-26T15:20:09.733Z" "cmpms3j580003my3cx0klbxa5" "astana" "WEBSOCKET" "SIMULATED" "parking" "Astana" "2026-05-26T15:20:09.733Z" "WEBSOCKET" 0.957 73 "parking" 24
"cleaned" "cmpms7lzx00axmy3comjqb32v" "2026-05-26T15:20:08.733Z" "cmpms3j580003my3cx0klbxa5" "astana" "WEBSOCKET" "SIMULATED" "parking" "Astana" "2026-05-26T15:20:08.733Z" "WEBSOCKET" 0.821 41 "parking" 60

Usage

from datasets import load_dataset

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