Datasets
The Dataset classes define the data model behind Open Targets Gentropy. Every class inherits from the Dataset
class and contains a dataframe with a predefined schema that can be found in the respective classes.
gentropy.dataset.dataset.Dataset
dataclass
¶
Bases: ABC
Open Targets Gentropy Dataset.
Dataset
is a wrapper around a Spark DataFrame with a predefined schema. Schemas for each child dataset are described in the schemas
module.
Source code in src/gentropy/dataset/dataset.py
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|
df: DataFrame
property
writable
¶
Dataframe included in the Dataset.
Returns:
Name | Type | Description |
---|---|---|
DataFrame |
DataFrame
|
Dataframe included in the Dataset |
schema: StructType
property
¶
Dataframe expected schema.
Returns:
Name | Type | Description |
---|---|---|
StructType |
StructType
|
Dataframe expected schema |
coalesce(num_partitions: int, **kwargs: Any) -> Self
¶
Coalesce the DataFrame included in the Dataset.
Coalescing is efficient for decreasing the number of partitions because it avoids a full shuffle of the data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
num_partitions |
int
|
Number of partitions to coalesce to |
required |
**kwargs |
Any
|
Arguments to pass to the coalesce method |
{}
|
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Coalesced Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
filter(condition: Column) -> Self
¶
Creates a new instance of a Dataset with the DataFrame filtered by the condition.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
condition |
Column
|
Condition to filter the DataFrame |
required |
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Filtered Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
from_parquet(session: Session, path: str | list[str], **kwargs: bool | float | int | str | None) -> Self
classmethod
¶
Reads parquet into a Dataset with a given schema.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
session |
Session
|
Spark session |
required |
path |
str | list[str]
|
Path to the parquet dataset |
required |
**kwargs |
bool | float | int | str | None
|
Additional arguments to pass to spark.read.parquet |
{}
|
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Dataset with the parquet file contents |
Raises:
Type | Description |
---|---|
ValueError
|
Parquet file is empty |
Source code in src/gentropy/dataset/dataset.py
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|
get_schema() -> StructType
abstractmethod
classmethod
¶
Abstract method to get the schema. Must be implemented by child classes.
Returns:
Name | Type | Description |
---|---|---|
StructType |
StructType
|
Schema for the Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
persist() -> Self
¶
Persist in memory the DataFrame included in the Dataset.
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Persisted Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
repartition(num_partitions: int, **kwargs: Any) -> Self
¶
Repartition the DataFrame included in the Dataset.
Repartitioning creates new partitions with data that is distributed evenly.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
num_partitions |
int
|
Number of partitions to repartition to |
required |
**kwargs |
Any
|
Arguments to pass to the repartition method |
{}
|
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Repartitioned Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
unpersist() -> Self
¶
Remove the persisted DataFrame from memory.
Returns:
Name | Type | Description |
---|---|---|
Self |
Self
|
Unpersisted Dataset |
Source code in src/gentropy/dataset/dataset.py
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|
validate_schema() -> None
¶
Validate DataFrame schema against expected class schema.
Raises:
Type | Description |
---|---|
ValueError
|
DataFrame schema is not valid |
Source code in src/gentropy/dataset/dataset.py
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|