CSV (Comma Separated Values) is a widely used format for logging flight, sensor, and machine data. It is human-readable, editable with spreadsheets, and compatible with practically any data analysis tool.

Nominal is the leading platform for operationalizing test data.

This guide describes an automatable pipeline for uploading CSV files to Nominal.

Connect to Nominal

Get your Nominal API token from your User settings page.

See the Quickstart for more details on connecting to Nominal from Python.

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import nominal.nominal as nm
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nm.set_token(
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    base_url = 'https://api.gov.nominal.io/api',
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    token = '* * *' # Replace with your Access Token from 
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                    # https://app.gov.nominal.io/settings/user?tab=tokens
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)

Download sample data

For convenience and educational purposes, Nominal hosts test sample data on the Nominal Hugging Face account.

Let’s download a 1000 rows of data generated in the X-Plane flight simulator.

We’ll use the popular Polars library to download and inspect the data. Polars is installed automatically as part of the nominal Python library.

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import polars as pl
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df = pl.read_csv('hf://datasets/nominal-io/frosty-flight/frosty_flight_1k_rows.csv')
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df.write_csv('frosty_flight_1k_rows.csv')
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df.head().select(df.columns[:6])

Upload your data to Nominal

You can upload this CSV file to Nominal with the upload_csv() function.

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import nominal.nominal as nm
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dataset = nm.upload_csv(
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    'frosty_flight_1k_rows.csv',
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    name='Frosty Flight',
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    timestamp_column='source_time',
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    timestamp_type='iso_8601',
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)
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print('Uploaded dataset:', dataset.rid)

Equivalently, since your CSV is already loaded in the Polars dataframe df, you can upload it with the upload_polars() method:

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import nominal.nominal as nm
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dataset = nm.upload_polars(
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    df,
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    name='Frosty Flight',
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    timestamp_column='source_time',
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    timestamp_type='iso_8601',
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)
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print('Uploaded dataset:', dataset.rid)

After upload, navigate to Nominal’s Datasets page (login required). You’ll see your CSV at the top!