Documentation Index
Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
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The Pandas Client is used for batch logging of LLM traces, evaluations, and
traditional ML model predictions using pandas DataFrames.
from arize.pandas.logger import Client
client = Client(...)
log
The log() method migrates from client.log() to client.ml.log().
Parameter Reference
This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
| Parameter | v7 | v8 | Changes |
|---|
space_id | Client init | Required per call | Must pass explicitly |
model_id | Required | Required | Renamed to model_name |
dataframe | Required | Required | — |
schema | Required | Required | — |
environment | Required | Required | — |
model_type | Required | Required | — |
model_version | Optional | Optional | — |
batch_id | Optional | Optional | — |
validate | Optional | Optional | — |
metrics_validation | Optional | Optional | — |
surrogate_explainability | Optional | Optional | — |
timeout | Optional | Optional | — |
path | Optional | Optional | Renamed to tmp_dir for clarity |
sync | Optional | ❌ Removed | No longer supported |
verbose | Optional | ❌ Removed | Use Python logging config instead |
Side-by-Side Comparison
See the complete migration in action with this example showing both client initialization and logging model predictions.
from arize.pandas.logger import Client
from arize.utils.types import Environments, ModelTypes, Schema
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging model predictions
response = client.log(
dataframe=predictions_df,
schema=schema,
environment=Environments.PRODUCTION,
model_id="my-model",
model_type=ModelTypes.BINARY_CLASSIFICATION,
model_version="v1.0",
batch_id="batch-123",
metrics_validation=[Metrics.CLASSIFICATION],
validate=True,
path="/tmp/arize",
surrogate_explainability=False,
timeout=30.0,
sync=False,
verbose=True
)
log_spans
The log_spans() method migrates from client.log_spans() to client.spans.log().
Parameter Reference
This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
| Parameter | v7 | v8 | Notes / Changes |
|---|
space_id | Client init | Required per call | Must pass explicitly |
project_name | Optional | Required | Must pass explicitly |
dataframe | Required | Required | — |
evals_dataframe | Optional | Optional | — |
datetime_format | Optional | Optional | — |
validate | Optional | Optional | — |
timeout | Optional | Optional | — |
model_id | Deprecated | ❌ Removed | Use project_name instead |
model_version | Optional | ❌ Removed | No longer supported |
path | Optional | Optional | Renamed to tmp_dir for clarity |
verbose | Optional | ❌ Removed | Use Python logging config instead |
Side-by-Side Comparison
See the complete migration in action with this example showing both client initialization and logging spans with all common parameters.
from arize.pandas.logger import Client
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging spans
response = client.log_spans(
dataframe=spans_df,
model_id="my-project", # or project_name
model_version="v1.0", # optional
evals_dataframe=evals_df, # optional
datetime_format="%Y-%m-%dT%H:%M:%S.%f+00:00",
validate=True,
path="/tmp/arize",
timeout=30.0,
verbose=True,
project_name="my-project" # alternative to model_id
)
log_evaluations & log_evaluations_sync
In v7, there were two separate methods for logging evaluations: log_evaluations()
(async HTTP) and log_evaluations_sync() (sync gRPC). In v8, both consolidate
into a single update_evaluations() method that uses gRPC by default with an
optional force_http parameter.
Both v7 methods (log_evaluations and log_evaluations_sync) now use the same
v8 method: update_evaluations(), which uses Arrow Flight (gRPC) by default, matching
the behavior of v7’s log_evaluations_sync(). Use force_http=True if you need
HTTP transport (not recommended for large payloads).
Parameter Reference
This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
| Parameter | v7 | v8 | Notes / Changes |
|---|
space_id | Client init | Required per call | Must pass explicitly |
project_name | Optional | Required | Must pass explicitly |
dataframe | Required | Required | — |
validate | Optional | Optional | — |
timeout | Optional | Optional | — |
model_id | Deprecated | ❌ Removed | Use project_name instead |
model_version | Optional | ❌ Removed | No longer supported |
verbose | Optional | ❌ Removed | Use Python logging config instead |
tmp_dir | N/A | ✅ New | Temporary directory for file storage |
force_http | N/A | ✅ New | Force HTTP transport (defaults to False) |
Side-by-Side Comparison
See the complete migration in action with this example showing both client initialization and logging evaluations synchronously.
log_evaluations_sync
from arize.pandas.logger import Client
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging evaluations (sync via gRPC)
response = client.log_evaluations_sync(
dataframe=evals_df,
model_id="my-project", # or project_name
model_version="v1.0", # optional
validate=True,
timeout=30.0,
verbose=True,
project_name="my-project" # alternative to model_id
)
log_evaluations
from arize.pandas.logger import Client
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging evaluations
response = client.log_evaluations(
dataframe=evals_df,
model_id="my-project", # or project_name
model_version="v1.0", # optional
validate=True,
path="/tmp/arize",
timeout=30.0,
verbose=True,
project_name="my-project" # alternative to model_id
)
log_annotations
The log_annotations() method migrates from client.log_annotations() to client.spans.update_annotations().
Parameter Reference
This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
| Parameter | v7 | v8 | Notes / Changes |
|---|
space_id | Client init | Required per call | Must pass explicitly |
project_name | Required | Required | — |
dataframe | Required | Required | — |
validate | Optional | Optional | — |
verbose | Optional | ❌ Removed | Use Python logging config instead |
Side-by-Side Comparison
See the complete migration in action with this example showing both client initialization and logging annotations.
from arize.pandas.logger import Client
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging annotations
response = client.log_annotations(
dataframe=annotations_df,
project_name="my-project",
validate=True,
verbose=True
)
log_metadata() method migrates from client.log_metadata() to client.spans.update_metadata().
Parameter Reference
This table provides a complete mapping of all parameters between v7 and v8, including which parameters were removed, renamed, or remain unchanged.
| Parameter | v7 | v8 | Changes |
|---|
space_id | Client init | Required per call | Must pass explicitly |
project_name | Required | Required | — |
dataframe | Required | Required | — |
patch_document_column_name | Optional | Optional | — |
validate | Optional | Optional | — |
verbose | Optional | ❌ Removed | Use Python logging config instead |
Side-by-Side Comparison
See the complete migration in action with this example showing both client initialization and logging metadata updates.
from arize.pandas.logger import Client
# Client initialization
client = Client(
api_key="your-api-key",
space_id="your-space-id"
)
# Logging metadata
response = client.log_metadata(
dataframe=metadata_df,
project_name="my-project",
patch_document_column_name="patch_document",
validate=True,
verbose=True
)
