> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Pandas batch logging

> Batch Logging - Designed for sending batches of data to Arize

Use the `arize` Python library to monitor machine learning predictions with a few lines of code in a Jupyter Notebook or a Python server that batch processes backend data

The most commonly used functions/objects are:

[`Client`](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/client) — Initialize to begin logging model data to Arize

[`Schema`](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/schema) — Organize and map column names containing model data within your Pandas dataframe.

[`log`](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/log) — Log inferences within a dataframe to Arize via a POST request.

### Python Pandas Example

For examples and interactive notebooks, see [Cookbooks](/ax/cookbooks)

```python theme={null}
# install and import dependencies
%pip install -q "arize<8.0.0"

import datetime

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

# create Arize client
SPACE_ID = "SPACE_ID"
API_KEY = "API_KEY"
arize_client = Client(space_id=SPACE_ID, api_key=API_KEY)

# define schema
schema = Schema(
    prediction_id_column_name="prediction_id",
    timestamp_column_name="prediction_ts",
    prediction_label_column_name="predicted_label",
    actual_label_column_name="actual_label",
    feature_column_names=feature_column_names,
    tag_column_names=TypedSchema(
        inferred=["tag1", "tag3"],
        to_int=["tag2"],
    )
)

#log data
response = arize_client.log(
    dataframe=df,
    schema=schema,
    model_id="binary-classification-metrics-only-batch-ingestion-tutorial",
    model_version="1.0.0",
    model_type=ModelTypes.BINARY_CLASSIFICATION,
    metrics_validation=[Metrics.CLASSIFICATION],
    validate=True,
    environment=Environments.PRODUCTION
)
```

Follow this example in Google Colab:

<Card horizontal icon="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/gc.ico" title="Google Collaboratory" href="https://colab.research.google.com/github/Arize-ai/tutorials_python/blob/main/Arize_Tutorials/Data_Ingestion/Binary_Classification/binary_classification_metrics_only_batch_ingestion_tutorial.ipynb" />

### Benchmark Tests

The ability to ingest data with low latency is important to many customers. Below is a benchmarking colab that demonstrates the efficiency with which Arize uploads data from a Python environment.

|                                                      |                                                                                                                              |
| ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| Sending 10 Million Inferences to Arize in 90 Seconds | [Colab Link](https://colab.research.google.com/github/Arize-ai/tutorials_python/blob/main/Benchmarks/arize_3_1_1_test.ipynb) |