Can someone give me an example of two partitioned ...
# ask-community
Can someone give me an example of two partitioned by day software defined assets say foo and bar, where bar on day d depends on foo on day d-1 (or all the foos in a date range).
dagster bot responded by community 1
Hey! Here is an example from my code Partition mapping:
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class NDaysPartitionMapping(PartitionMapping):
    def __init__(self, days: int, offset: int = 0):
        self.days = days
        self.offset = offset

    def get_downstream_partitions_for_partition_range(
        upstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def: PartitionsDefinition,
        upstream_partitions_def: PartitionsDefinition,
    ) -> PartitionKeyRange:
        assert isinstance(upstream_partitions_def, DailyPartitionsDefinition)

        return upstream_partition_key_range

    def get_upstream_partitions_for_partition_range(
        downstream_partition_key_range: PartitionKeyRange,
        downstream_partitions_def: PartitionsDefinition,  # pylint: disable=unused-argument
        upstream_partitions_def: PartitionsDefinition,  # pylint: disable=unused-argument
    ) -> PartitionKeyRange:
        mapped_range = PartitionKeyRange(
                datetime.strptime(downstream_partition_key_range.start, "%Y-%m-%d")
                - timedelta(days=self.days)
                - timedelta(days=self.offset)
                datetime.strptime(downstream_partition_key_range.end, "%Y-%m-%d") - timedelta(days=self.offset)
        return mapped_range
Asset using it:
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    partition_mappings={"ranking_model": NDaysPartitionMapping(days=0, offset=2)},
    ins={"candidates_with_features": AssetIn(metadata={"columns": ALL_COLUMNS})},
def scored_candidates(ranking_model: CatBoost, candidates_with_features: pl.DataFrame) -> pl.DataFrame:
Support partition mappings inside your IO Manager (the most important part) :
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def load_input(self, context: InputContext) -> Union[pl.DataFrame, List[pl.DataFrame]]:
        # In this load_input function, we vary the behavior based on the type of the downstream input
        input_type = context.dagster_type.typing_type
        path = self.get_path(context)

        columns = context.metadata.get("columns")
        if columns is not None:
            context.log.debug(f"{self.__class__} received metadata value columns={columns}")
        allow_missing_partitions = context.metadata.get("allow_missing_partitions", False)

        if input_type == pl.DataFrame:
            context.log.debug(f"Loading DataFrame from {path}")
            df = load_parquet_v2(path, columns=columns)
            context.add_input_metadata({"path": MetadataValue.path(path)})
            return df
        elif input_type == List[pl.DataFrame]:
            # load multiple partitions
            if not context.has_asset_partitions:
                raise TypeError(f"Detected {input_type} input type but the asset is not partitioned")
                <|>("partitioning info")
                # <|>(context.asset_partitions_def.get_partition_keys())
                range_start, range_end = context.asset_partition_key_range

            # <|>(context.upstream_output.asset_info.partitions_def)

            base_dir = os.path.dirname(path)

            partitions = pl.date_range(
                datetime.strptime(range_start, "%Y-%m-%d").date(),
                datetime.strptime(range_end, "%Y-%m-%d").date(),

            context.log.debug(f"Loading {len(partitions)} partitions")

            dfs: List[pl.DataFrame] = []
            for partition in partitions:
                path_with_partition = os.path.join(base_dir, f"{partition}.pq")
                context.log.debug(f"Loading DataFrame partition from {path_with_partition}")
                    df = load_parquet_v2(path_with_partition, columns=columns)
                except FileNotFoundError as e:
                    if not allow_missing_partitions:
                        raise e
                    context.log.debug(f"Couldn't load partition {path_with_partition} and skipped it")
            return dfs
            return check.failed(
                f"Inputs of type {context.dagster_type} not supported. Please specify a valid type "
                "for this input either in the op signature or on the corresponding In."
Sorry, the example is dirty because I don't have the time to clean it up for you now, but I guess you get the idea.
I think you forgot to share the second asset, but, i get the idea.