CSV and Parquet#

Upload CSV and Parquet files with add_tabular_data(). Each file needs one timestamp column. Every other column becomes a channel.

For creating datasets and waiting for ingestion, see Datasets.

Connect the client#

This guide assumes you have installed the client and setup your API keys.

from nominal.core import NominalClient

client = NominalClient.from_profile("default")

Upload a file#

The examples upload flight.csv. To run them as written, save this sample under that name:

time,altitude,temperature,tail_number
2026-09-10T14:38:19Z,100.0,20.0,N456QC
2026-09-10T14:38:20Z,101.5,20.2,N456QC
2026-09-10T14:38:21Z,103.0,20.4,N456QC

dataset = client.create_dataset("Frosty flight")
file = dataset.add_tabular_data("flight.csv", timestamp_column="time", timestamp_type="iso_8601")
file.poll_until_ingestion_completed()

add_tabular_data accepts .csv, .csv.gz, .parquet and .parquet.gz, plus .parquet.tar, .parquet.tar.gz and .parquet.zip archives.

CSV files must use commas between values and periods for decimals, such as 3.14. Convert files that use semicolons and decimal commas, such as 3,14, before you upload them.

To upload a pandas DataFrame without writing a file, use upload_dataframe_to_dataset().

Timestamp formats#

timestamp_type describes the values in the timestamp column:

Values

timestamp_type

2024-06-08T05:58:42.000Z

"iso_8601"

1717826322

"epoch_seconds"

1717826322000000

"epoch_microseconds"

Other units since the Unix epoch

"epoch_nanoseconds", "epoch_milliseconds", "epoch_minutes", and so on

Time since the start of the recording

ts.Relative

Any other absolute format

ts.Custom

Relative timestamps need the unit and the absolute time the recording started:

from datetime import datetime, timezone

from nominal import ts

recording_start = datetime(2026, 9, 10, 14, 38, 19, tzinfo=timezone.utc)
dataset.add_tabular_data(
    "bench.csv",
    timestamp_column="elapsed_s",
    timestamp_type=ts.Relative("seconds", start=recording_start),
)

A value of 1.0 in elapsed_s then means 2026-09-10T14:38:20Z. See Timestamps for all formats.

Tag the data#

Tags label the points in a file so you can filter them later, such as by test or by sensor. tags applies the same tags to every row, and tag_columns reads a tag’s value from a column:

dataset.add_tabular_data(
    "flight.csv",
    timestamp_column="time",
    timestamp_type="iso_8601",
    tags={"test_id": "flight-042"},
    tag_columns={"aircraft": "tail_number"},  # tag key: column name
)

See tags for filtering by them.