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[Data] Improve appearance of repr(dataset) #59631

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81 changes: 60 additions & 21 deletions doc/source/data/loading-data.rst
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -395,11 +395,17 @@ Ray Data interoperates with libraries like pandas, NumPy, and Arrow.

.. testoutput::

MaterializedDataset(
num_blocks=3,
num_rows=3,
schema={food: string, price: double}
)
shape: (3, 2)
╭────────┬────────╮
│ food ┆ price │
│ --- ┆ --- │
│ string ┆ double │
╞════════╪════════╡
│ spam ┆ 9.34 │
│ ham ┆ 5.37 │
│ eggs ┆ 0.94 │
╰────────┴────────╯
(Showing 3 of 3 rows)

You can also create a :class:`~ray.data.dataset.Dataset` from a list of regular
Python objects. In the schema, the column name defaults to "item".
Expand All @@ -414,7 +420,19 @@ Ray Data interoperates with libraries like pandas, NumPy, and Arrow.

.. testoutput::

MaterializedDataset(num_blocks=5, num_rows=5, schema={item: int64})
shape: (5, 1)
╭───────╮
│ item │
│ --- │
│ int64 │
╞═══════╡
│ 1 │
│ 2 │
│ 3 │
│ 4 │
│ 5 │
╰───────╯
(Showing 5 of 5 rows)

.. tab-item:: NumPy

Expand All @@ -434,11 +452,20 @@ Ray Data interoperates with libraries like pandas, NumPy, and Arrow.

.. testoutput::

MaterializedDataset(
num_blocks=1,
num_rows=3,
schema={data: ArrowTensorTypeV2(shape=(2, 2), dtype=double)}
)
shape: (3, 1)
╭──────────────────────────────────────────╮
│ data │
│ --- │
│ ArrowTensorTypeV2(shape=(2, 2), dtype=d… │
╞══════════════════════════════════════════╡
│ [[1. 1.]
[1. 1.]] │
│ [[1. 1.]
[1. 1.]] │
│ [[1. 1.]
[1. 1.]] │
╰──────────────────────────────────────────╯
(Showing 3 of 3 rows)

.. tab-item:: pandas

Expand All @@ -460,11 +487,17 @@ Ray Data interoperates with libraries like pandas, NumPy, and Arrow.

.. testoutput::

MaterializedDataset(
num_blocks=1,
num_rows=3,
schema={food: object, price: float64}
)
shape: (3, 2)
╭────────┬────────╮
│ food ┆ price │
│ --- ┆ --- │
│ object ┆ double │
╞════════╪════════╡
│ spam ┆ 9.34 │
│ ham ┆ 5.37 │
│ eggs ┆ 0.94 │
╰────────┴────────╯
(Showing 3 of 3 rows)

.. tab-item:: PyArrow

Expand All @@ -485,11 +518,17 @@ Ray Data interoperates with libraries like pandas, NumPy, and Arrow.

.. testoutput::

MaterializedDataset(
num_blocks=1,
num_rows=3,
schema={food: string, price: double}
)
shape: (3, 2)
╭────────┬────────╮
│ food ┆ price │
│ --- ┆ --- │
│ string ┆ double │
╞════════╪════════╡
│ spam ┆ 9.34 │
│ ham ┆ 5.37 │
│ eggs ┆ 0.94 │
╰────────┴────────╯
(Showing 3 of 3 rows)

.. _loading_datasets_from_distributed_df:

Expand Down
33 changes: 20 additions & 13 deletions doc/source/data/quickstart.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ across your cluster for better performance.
def transform_batch(batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]:
vec_a = batch["petal length (cm)"]
vec_b = batch["petal width (cm)"]
batch["petal area (cm^2)"] = vec_a * vec_b
batch["petal area (cm^2)"] = np.round(vec_a * vec_b, 2)
return batch

# Apply the transformation to our dataset
Expand All @@ -74,18 +74,25 @@ across your cluster for better performance.

.. testoutput::

MaterializedDataset(
num_blocks=...,
num_rows=150,
schema={
sepal length (cm): double,
sepal width (cm): double,
petal length (cm): double,
petal width (cm): double,
target: int64,
petal area (cm^2): double
}
)
shape: (150, 6)
╭───────────────────┬──────────────────┬───────────────────┬──────────────────┬────────┬───────────────────╮
│ sepal length (cm) ┆ sepal width (cm) ┆ petal length (cm) ┆ petal width (cm) ┆ target ┆ petal area (cm^2) │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ double ┆ double ┆ double ┆ double ┆ int64 ┆ double │
╞═══════════════════╪══════════════════╪═══════════════════╪══════════════════╪════════╪═══════════════════╡
│ 5.1 ┆ 3.5 ┆ 1.4 ┆ 0.2 ┆ 0 ┆ 0.28 │
│ 4.9 ┆ 3.0 ┆ 1.4 ┆ 0.2 ┆ 0 ┆ 0.28 │
│ 4.7 ┆ 3.2 ┆ 1.3 ┆ 0.2 ┆ 0 ┆ 0.26 │
│ 4.6 ┆ 3.1 ┆ 1.5 ┆ 0.2 ┆ 0 ┆ 0.3 │
│ 5.0 ┆ 3.6 ┆ 1.4 ┆ 0.2 ┆ 0 ┆ 0.28 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 6.7 ┆ 3.0 ┆ 5.2 ┆ 2.3 ┆ 2 ┆ 11.96 │
│ 6.3 ┆ 2.5 ┆ 5.0 ┆ 1.9 ┆ 2 ┆ 9.5 │
│ 6.5 ┆ 3.0 ┆ 5.2 ┆ 2.0 ┆ 2 ┆ 10.4 │
│ 6.2 ┆ 3.4 ┆ 5.4 ┆ 2.3 ┆ 2 ┆ 12.42 │
│ 5.9 ┆ 3.0 ┆ 5.1 ┆ 1.8 ┆ 2 ┆ 9.18 │
╰───────────────────┴──────────────────┴───────────────────┴──────────────────┴────────┴───────────────────╯
(Showing 10 of 150 rows)

To explore more transformation capabilities, read :ref:`Transforming data <transforming_data>`.

Expand Down
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