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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"
)
| Argument | Data Type | Description |
|---|---|---|
api_key | str | (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_id | str | (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. |
uri | str | (Optional) URI endpoint required for on-prem customers. Defaults to “https://api.arize.com/v1” |
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!"
)