Overview - DataFrames

For Data Scientists, a common format for storing and managing data in Python is using Pandas DataFrames.

Kelvin has a convenient function to convert your Kelvin data into a Pandas DataFrame.

data.to_df()

Example

Here is a simple example to retrieve data from the Kelvin Platform and convert it into a Pandas DataFrame for further analysis and manipulation.

from kelvin.api.client import Client

# Login
client = Client(config={"url": "https://<url.kelvin.ai>", "username": "<your_username>"})
client.login(password="<your_password>")

# Get Time Series Data
response = client.timeseries.get_timeseries_range(
    data={
        "start_time": "2024-04-18T12:00:00.000000Z",
        "end_time": "2024-04-19T12:00:00.000000Z",
        "selectors": [
            {
                "resource": "krn:ad:pcp_01/casing_pressure",
            }
        ]
    }
)

# Convert the response into a Pandas DataFrame
df = response.to_df()

# Print the result
print(df)