DatasetMetadata

Class representing metadata about a dataset including schema information. Instances of DatasetMetadata are created by Squirrels when describing datasets (and as the base for DatasetResult); you should not construct this class directly. If DatasetMetadata is needed for type annotation, it can be imported from the squirrels.types or squirrels module.

Methods

to_json()

Returns the dataset metadata as a JSON-serializable dictionary.

def to_json(self) -> dict:

dict

A dictionary containing dataset metadata with the following structure:

Show return structure

object

Schema information for the dataset.

Hide schema properties

array

Array of field objects, where each field has the following properties:

Hide field properties

string

The name of the field/column.

string

The data type of the field (e.g. “string”, “integer”, “float”, “boolean”, “date”, “datetime”).

list[string]

default:"[]"

The condition(s) of when the field is included. Only used for documentation purposes.

string

Human-readable description of the field.

string

Category of the field (e.g. “dimension”, “measure”, “misc”).

Examples

Here are some common usage patterns for the DatasetMetadata class. It is assumed that the code is running in an async context (e.g. inside an async function or a Jupyter Notebook cell).

Get dataset metadata

from typing import TYPE_CHECKING
from squirrels import SquirrelsProject

if TYPE_CHECKING:
    from squirrels.types import DatasetMetadata

sqrl = SquirrelsProject()

# Get metadata for a dataset
metadata: "DatasetMetadata" = sqrl.dataset_metadata("sales_data")

# Access schema information as JSON
schema_json = metadata.to_json()
print(schema_json)
# Output: {'schema': {'fields': [{'name': 'date', 'type': 'date', ...}, ...]}}

arguments

auth

connections

parameters

parameter_options

data_sources

dashboards

types

Methods

to_json()

Examples

Get dataset metadata

​Methods

​to_json()

​Examples

​Get dataset metadata

Methods

to_json()

Examples

Get dataset metadata