uscensus

class dartfx.uscensus.uscensus.UsCensusAggregatedDataset(*, api: UsCensusApi, c_dataset: list[str], c_vintage: int | None = None, c_geographyLink: str, c_variablesLink: str, c_tagsLink: str, c_examplesLink: str, c_groupsLink: str, c_sorts_url: str, c_documentationLink: str, c_isAggregate: bool | None = False, c_isMicrodata: bool | None = False, c_isTimeseries: bool | None = False, c_isCube: bool | None = False, c_isAvailable: bool | None = False, type: str, title: str, accessLevel: str, bureauCode: list[str], description: str, distribution: list[dict], contactPoint: dict, identifier: str, keyword: list[str], license: str, modified: str, programCode: list[str], references: list[str], spatial: str | None = None, temporal: str | None = None, publisher: dict)[source]

Bases: UsCensusDataset

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

class dartfx.uscensus.uscensus.UsCensusApi(api_key: str | None = None, session: CachedSession | None = None, *, user_agent: str = 'dartfx-uscensus/0.0.1')[source]

Bases: BaseModel

Helper to call the U.S. Census data API

api_key: str | None

get_dcat_json() → dict[source]: Returns the data catalog as a JSON object.

get_dcat_xml() → Element[source]: Returns the data catalog as an XML object.

get_request(path, description=None, headers=None, **kwargs)[source]: Call the API using the GET method.

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

post_request(path, description=None, **kwargs)[source]: Call the API using the POST method.

request(method, path, description=None, content_type='application/json', **kwargs)[source]: Call the API.

user_agent: str

exception dartfx.uscensus.uscensus.UsCensusApiError(message, url, status_code=None, response=None)[source]

Bases: Exception

Custom exception for U.S. Census API errors.

class dartfx.uscensus.uscensus.UsCensusCatalog(api: UsCensusApi, load_data: bool = True)[source]

Bases: BaseModel

api: UsCensusApi

property datasets: dict[str, UsCensusDataset]

get_dataset(dataset_id: str) → UsCensusDataset[source]

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

refresh()[source]

search_datasets(is_aggregate: bool | None = None, is_cube: bool | None = None, is_microdata: bool | None = None) → list[UsCensusDataset][source]: Search for datasets.

property stats

class dartfx.uscensus.uscensus.UsCensusDataset(*, api: UsCensusApi, c_dataset: list[str], c_vintage: int | None = None, c_geographyLink: str, c_variablesLink: str, c_tagsLink: str, c_examplesLink: str, c_groupsLink: str, c_sorts_url: str, c_documentationLink: str, c_isAggregate: bool | None = False, c_isMicrodata: bool | None = False, c_isTimeseries: bool | None = False, c_isCube: bool | None = False, c_isAvailable: bool | None = False, type: str, title: str, accessLevel: str, bureauCode: list[str], description: str, distribution: list[dict], contactPoint: dict, identifier: str, keyword: list[str], license: str, modified: str, programCode: list[str], references: list[str], spatial: str | None = None, temporal: str | None = None, publisher: dict)[source]

Bases: BaseModel

class Geography(*, default: list[dict], fips: list[Fips])[source]

Bases: BaseModel

class Fips(*, name: str, geoLevelDisplay: str, referenceDate: str, requires: list[str] | None = None)[source]

Bases: BaseModel

geoLevelDisplay: str

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

referenceDate: str

requires: list[str] | None

default: list[dict]

fips: list[Fips]

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Bases: BaseModel

class Values(*, item: dict[str, str] | None = None, range: list[Range] | None = None)[source]

Bases: BaseModel

class Range(*, min: str, max: str, description: str)[source]

Bases: BaseModel

description: str

max: str

min: str

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

item: dict[str, str] | None

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

range: list[Range] | None

property codelist

concept: str | None

property croissant_data_type

group: str

is_weight: bool | None

label: str

limit: int

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

predicateOnly: bool | None

predicateType: str | None

suggested_weight: str | None

values: Values | None

accessLevel: str

property access_url

api: UsCensusApi

bureauCode: list[str]

c_dataset: list[str]

c_documentationLink: str

c_examplesLink: str

c_geographyLink: str

c_groupsLink: str

c_isAggregate: bool | None

c_isAvailable: bool | None

c_isCube: bool | None

c_isMicrodata: bool | None

c_isTimeseries: bool | None

c_sorts_url: str

c_tagsLink: str

c_variablesLink: str

c_vintage: int | None

contactPoint: dict

description: str

distribution: list[dict]

property geography

get_croissant(include_computed=False) → Metadata[source]

get_dcat_us()[source]

get_ddi_codebook(codebook_version='2.5', include_schema=False) → str[source]

Generate DDI-Codebook XML for this dataset.

This generates an XML string so we do not have a dependency on XML packages and developers can use the one they like….

Returns:: The DDI-Codebook XML
Return type:: str

property id

identifier: str

keyword: list[str]

license: str

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

modified: str

property name: str

programCode: list[str]

publisher: dict

references: list[str]

spatial: str | None

property stats

temporal: str | None

title: str

type: str

property variables

class dartfx.uscensus.uscensus.UsCensusMicrodataDataset(*, api: UsCensusApi, c_dataset: list[str], c_vintage: int | None = None, c_geographyLink: str, c_variablesLink: str, c_tagsLink: str, c_examplesLink: str, c_groupsLink: str, c_sorts_url: str, c_documentationLink: str, c_isAggregate: bool | None = False, c_isMicrodata: bool | None = False, c_isTimeseries: bool | None = False, c_isCube: bool | None = False, c_isAvailable: bool | None = False, type: str, title: str, accessLevel: str, bureauCode: list[str], description: str, distribution: list[dict], contactPoint: dict, identifier: str, keyword: list[str], license: str, modified: str, programCode: list[str], references: list[str], spatial: str | None = None, temporal: str | None = None, publisher: dict)[source]

Bases: UsCensusDataset

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.

class dartfx.uscensus.uscensus.UsCensusTimeSeriesDataset(*, api: UsCensusApi, c_dataset: list[str], c_vintage: int | None = None, c_geographyLink: str, c_variablesLink: str, c_tagsLink: str, c_examplesLink: str, c_groupsLink: str, c_sorts_url: str, c_documentationLink: str, c_isAggregate: bool | None = False, c_isMicrodata: bool | None = False, c_isTimeseries: bool | None = False, c_isCube: bool | None = False, c_isAvailable: bool | None = False, type: str, title: str, accessLevel: str, bureauCode: list[str], description: str, distribution: list[dict], contactPoint: dict, identifier: str, keyword: list[str], license: str, modified: str, programCode: list[str], references: list[str], spatial: str | None = None, temporal: str | None = None, publisher: dict)[source]

Bases: UsCensusDataset

model_config = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self – The BaseModel instance.
context – The context.