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

# Exploring Resources with Dataframes

> When it comes to working with data for machine learning, dataframes are ubiquitous. Featureform simplifies interaction with its sources and transformations, allowing you to fetch them into local memory as dataframes using the *client.dataframe()* API.

```python theme={null}
@spark.df_transform(inputs=[upstream_data])
def transformation_name(upstream):
  ...
client.dataframe(transformation_name)
```

In scenarios where the dataset is sizable enough to exceed memory limits or when a sample suffices, you can include the *rows* parameter to restrict the number of pulled rows.

```python theme={null}
client.dataframe(transformation_name, rows=10000)
```

Fetching training sets as dataframes is also feasible. In this case, Featureform's serving API equips the training set object with a *.dataframe()* method.

```python theme={null}
client.training_set("name", "variant").dataframe()
```

These mechanisms offer an intuitive and efficient approach to delve into your data using the familiar Dataframe APIs.