Does Labelbox offer native support for annotating semi-structured data (i.e. CSV data)?
Screenshot attached above.
Use case would be to bring in a CSV, and then Annotate using a rules-based system (i.e. if Temperature > 90 Degrees F AND Humidity > 90%), then label/categorize the data row as ‘ASSET AT RISK OF FAILURE’.
Hey @Kush,
So at the moment, the only workaround I would have is to use tabulate to import table (will be recognised as text in Labelbox).
import labelbox as lb
import os
import tabulate as tb
api_key = None
client = lb.Client(api_key)
table_sample = [["Timestamp","Temperature","Humidity", "Wind Speed"],
["2023-09-01 00:00:00",90.5,90.7, 5 ],
["2023-09-01 01:00:00",20.2,36, 2.1],
["2023-09-01 02:00:00",21.7,20, 3.4],
["2023-09-01 03:00:00",24.9,39, 7.3],
["2023-09-01 04:00:00",19.0,40, 5]]
table_tb = tb.tabulate(table_sample)
print(table_tb)
dataset = client.create_dataset(name="test table", iam_integration=None)
create_data_row = dataset.create_data_row(row_data=table_tb)
If you have a CSV, you can workaround as well.
Filtering would have to be done before uploading.
It would look like :
Many thanks,
PT
Labelbox Support
