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

# Publishing Batches of Events

> Dive into our guide on publishing batches of events. Learn how Fiddler supports multiple source formats when publishing batches of events.

Fiddler provides multiple options for publishing batches of production data, allowing you to choose the format and method that best suits your needs.

### Supported Data Formats

* pandas DataFrame
* CSV file (`.csv`),
* Parquet file (`.parquet`)

### Supported Data Locations

* In memory - pandas DataFrame
* Local disk - CSV, parquet

> **Note:**
>
> Fiddler's Python client offers the ability to integrate with Cloud data stores such as AWS S3. Refer to our [Integrations Guides](/integrations) for examples.

### Batch Publishing Examples

Publish a batch of inference events using a parquet file, CSV file, or DataFrame using `Model.publish_batch()`. This method executes asynchronously and returns a Job object. The job can be used to:

* Track by ID in the UI on the Jobs page
* Poll for status until completion
* Use the `wait()` method for synchronous behavior
* Log the job ID for reference

#### Parquet File

```python theme={null}
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)
publish_job = model.publish_batch(source='my_events_batch.parquet')

# publish_batch() is asynchronous. Use the publish job's wait() method
# if synchronous behavior is desired.
publish_job.wait()
```

#### CSV File

```python theme={null}
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)
publish_job = model.publish_batch(source='my_events_batch.csv')
```

#### Pandas DataFrame

```python theme={null}
import pandas as pd
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

my_events_df = pd.read_csv('my_events_batch.csv')
publish_job = model.publish_batch(source=my_events_df)
```

*Please allow a few minutes for events to populate the related charts.* *Total processing time is a function of both width and count of the inference events.*