<!-- TODO: check for variant accuracy, remove mention of flytesnacks figure out "UnionTypes" -->

# Dataclass

When you've multiple values that you want to send across Union.ai entities, you can use a `dataclass`.

To begin, import the necessary dependencies:

```python
import os
import tempfile
from dataclasses import dataclass

import pandas as pd
import union
from flytekit.types.structured import StructuredDataset
```

Build your custom image with ImageSpec:
```python
image_spec = union.ImageSpec(
    registry="ghcr.io/flyteorg",
    packages=["pandas", "pyarrow"],
)
```

## Python types
We define a `dataclass` with `int`, `str` and `dict` as the data types.

```python
@dataclass
class Datum:
    x: int
    y: str
    z: dict[int, str]
```

You can send a `dataclass` between different tasks written in various languages, and input it through the Union.ai UI as raw JSON.

> [!NOTE]
> All variables in a data class should be **annotated with their type**. Failure to do will result in an error.

Once declared, a dataclass can be returned as an output or accepted as an input.

```python
@union.task(container_image=image_spec)
def stringify(s: int) -> Datum:
    """
    A dataclass return will be treated as a single complex JSON return.
    """
    return Datum(x=s, y=str(s), z={s: str(s)})

@union.task(container_image=image_spec)
def add(x: Datum, y: Datum) -> Datum:
    x.z.update(y.z)
    return Datum(x=x.x + y.x, y=x.y + y.y, z=x.z)
```

## Union.ai types
We also define a data class that accepts `StructuredDataset`, `FlyteFile` and `FlyteDirectory`.

```python
@dataclass
class UnionTypes:
    dataframe: StructuredDataset
    file: union.FlyteFile
    directory: union.FlyteDirectory

@union.task(container_image=image_spec)
def upload_data() -> UnionTypes:
    df = pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]})

    temp_dir = tempfile.mkdtemp(prefix="union-")
    df.to_parquet(temp_dir + "/df.parquet")

    file_path = tempfile.NamedTemporaryFile(delete=False)
    file_path.write(b"Hello, World!")

    fs = UnionTypes(
        dataframe=StructuredDataset(dataframe=df),
        file=union.FlyteFile(file_path.name),
        directory=union.FlyteDirectory(temp_dir),
    )
    return fs

@union.task(container_image=image_spec)
def download_data(res: UnionTypes):
    assert pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]}).equals(res.dataframe.open(pd.DataFrame).all())
    f = open(res.file, "r")
    assert f.read() == "Hello, World!"
    assert os.listdir(res.directory) == ["df.parquet"]
```

A data class supports the usage of data associated with Python types, data classes,
FlyteFile, FlyteDirectory and structured dataset.

We define a workflow that calls the tasks created above.

```python
@union.workflow
def dataclass_wf(x: int, y: int) -> (Datum, FlyteTypes):
    o1 = add(x=stringify(s=x), y=stringify(s=y))
    o2 = upload_data()
    download_data(res=o2)
    return o1, o2
```

To trigger the above task that accepts a dataclass as an input with `union run`, you can provide a JSON file as an input:

```shell
$ union run dataclass.py add --x dataclass_input.json --y dataclass_input.json
```

Here is another example of triggering a task that accepts a dataclass as an input with `union run`, you can provide a JSON file as an input:

```shell
$ union run \
  https://raw.githubusercontent.com/flyteorg/flytesnacks/69dbe4840031a85d79d9ded25f80397c6834752d/examples/data_types_and_io/data_types_and_io/dataclass.py \
  add --x dataclass_input.json --y dataclass_input.json
```

---
**Source**: https://github.com/unionai/unionai-docs/blob/main/content/user-guide/data-input-output/dataclass.md
**HTML**: https://www.union.ai/docs/v1/union/user-guide/data-input-output/dataclass/