learning/prompts/documentation-lookup/05_basic_ptransforms.md

Prompt:
What is a PTransform in Apache Beam?

Response:
A [`PTransform`](https://beam.apache.org/documentation/programming-guide/#transforms) (or transform) represents a data processing operation, or a step, in a Beam pipeline. A transform is applied to zero or more `PCollection` objects and produces zero or more `PCollection` objects.

Transforms have the following key characteristics:
1. Versatility: Able to execute a diverse range of operations on `PCollection` objects.
2. Composability: Can be combined to form elaborate data processing pipelines.
3. Parallel execution: Designed for distributed processing, allowing simultaneous execution across multiple workers.
4. Scalability: Able to handle extensive data and suitable for both batch and streaming data.

The Beam SDKs contain different transforms that you can apply to your pipeline’s `PCollection` objects. The following list includes common transform types:
 - [Source transforms](https://beam.apache.org/documentation/programming-guide/#pipeline-io) such as `TextIO.Read` and `Create`. A source transform conceptually has no input.
 - [Processing and conversion operations](https://beam.apache.org/documentation/programming-guide/#core-beam-transforms) such as `ParDo`, `GroupByKey`, `CoGroupByKey`, `Combine`, and `Count`.
 - [Outputting transforms](https://beam.apache.org/documentation/programming-guide/#pipeline-io) such as `TextIO.Write`.
 - User-defined, application-specific [composite transforms](https://beam.apache.org/documentation/programming-guide/#composite-transforms).

Transform processing logic is provided in the form of a function object, colloquially referred to as “user code.” This code is applied to each element of the input `PCollection` (or more than one `PCollection`). The `PCollection` objects can be linked together to create complex data processing sequences.
User code for transforms must satisfy the [requirements of the Beam model](https://beam.apache.org/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms).

The following example shows how to apply custom user code to a `PCollection` using the `ParDo` transform:

```python
import apache_beam as beam

def SomeUserCode(element):
    # Do something with element
    return element

with beam.Pipeline() as pipeline:
    input_collection = pipeline | beam.Create([...])
    output_collection = input_collection | beam.ParDo(SomeUserCode())
```