def handle_output(self, context, obj: pd.DataFrame):
"""This saves the dataframe as a CSV."""
fpath = self._get_fs_path(context.asset_key)
os.makedirs(os.path.dirname(fpath), exist_ok=True)
obj.to_csv(fpath)
with open(fpath + ".version", "w") as f:
f.write(context.version if context.version else "None")
yield MetadataEntry.int(obj.shape[0], "Rows")
yield MetadataEntry.path(fpath, "Path")
yield MetadataEntry.md(obj.head(5).to_markdown(), "Sample")
yield MetadataEntry.text(context.version, "Resolved version")
yield MetadataEntry.table_schema(
self.get_schema(context.dagster_type),
"Schema",
)
def materialize(_):
yield AssetMaterialization(
asset_key="all_types",
description="a materialization with all metadata types",
metadata_entries=[
MetadataEntry.text("text is cool", "text"),
MetadataEntry.url("https://bigty.pe/neato", "url"),
MetadataEntry.fspath("/tmp/awesome", "path"),
MetadataEntry.json({"is_dope": True}, "json"),
MetadataEntry.python_artifact(MetadataEntry, "python class"),
MetadataEntry.python_artifact(file_relative_path,
"python function"),
MetadataEntry.float(1.2, "float"),
MetadataEntry.int(1, "int"),
MetadataEntry.float(float("nan"), "float NaN"),
MetadataEntry.int(LONG_INT, "long int"),
MetadataEntry.pipeline_run("fake_run_id", "pipeline run"),
MetadataEntry.asset(AssetKey("my_asset"), "my asset"),
MetadataEntry.table(
label="table",
records=[
TableRecord(foo=1, bar=2),
TableRecord(foo=3, bar=4),
],
),
MetadataEntry.table_schema(
label="table_schema",
schema=TableSchema(
columns=[
TableColumn(
name="foo",
type="integer",
constraints=TableColumnConstraints(
unique=True),
),
TableColumn(name="bar", type="string"),
],
constraints=TableConstraints(other=["some constraint"
], ),
),
),
],
)
yield Output(None)
