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View Source on Github Arize class to begin logging predictions and actuals from a Pandas.DataFrame. Import and initialize Arize Client from arize.pandas.logger 
from arize.pandas.logger import Client

class Client(
    api_key: str #from Arize platform
    space_id: str
    uri: Optional[str] = "https://api.arize.com/v1"
)
ArgumentData TypeDescription
api_keystr(Required) Arize-provided api key associated with your service/space. Click “Show API Key” in the “Upload Data” page in the Arize UI to copy the key.
space_idstr(Required) Arize-provided identifier for relating records to spaces. Click “Show API Key” in the “Upload Data” page in the Arize UI to copy the key.
uristr(Optional) URI endpoint required for on-prem customers. Defaults to “https://api.arize.com/v1”

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Code Example

from arize.pandas.logger import Client, Schema
from arize.utils.types import ModelTypes, Environments, Schema, Metrics
import pandas as pd

SPACE_ID = "SPACE_ID" #  update value here with your Space ID
API_KEY = "API_KEY"  # update value here with your API key

arize_client = Client(space_id=SPACE_ID, api_key=API_KEY)

if SPACE_ID == "SPACE_ID" or API_KEY == "API_KEY":
    raise ValueError("❌ NEED TO CHANGE SPACE_ID AND/OR API_KEY")
else:
    print(
        "✅ Import and Setup Arize Client Done! Now we can start using Arize!"
    )