teehr.LocationCrosswalkTable#

class teehr.LocationCrosswalkTable(ev)[source]#

Access methods to location crosswalks table.

Methods

`distinct_values`	Return distinct values for a column.
`field_enum`	Get the location crosswalk fields enum.
`fields`	Return table columns as a list.
`filter`	Apply a filter.
`load_csv`	Import location crosswalks from CSV file format.
`load_parquet`	Import location crosswalks from parquet file format.
`order_by`	Apply an order_by.
`query`	Run a query against the table with filters and order_by.
`to_geopandas`	Return GeoPandas DataFrame.
`to_pandas`	Return Pandas DataFrame for Location Crosswalk.
`to_sdf`	Return PySpark DataFrame.
`validate`	Validate the dataset table against the schema.

field_enum() → LocationCrosswalkFields[source]#: Get the location crosswalk fields enum.

load_csv(in_path: Path | str, pattern: str = '**/*.csv', field_mapping: dict | None = None, **kwargs)[source]#

Import location crosswalks from CSV file format.

Parameters:

in_path (Union[Path, str]) – The input file or directory path. CSV file format.
field_mapping (dict, optional) – A dictionary mapping input fields to output fields. Format: {input_field: output_field}
**kwargs – Additional keyword arguments are passed to pd.read_csv() or pd.read_parquet().

Notes

The TEEHR Location Crosswalk table schema includes fields:

load_parquet(in_path: Path | str, pattern: str = '**/*.parquet', field_mapping: dict | None = None, **kwargs)[source]#

Import location crosswalks from parquet file format.

Parameters:

in_path (Union[Path, str]) – The input file or directory path. Parquet file format.
field_mapping (dict, optional) – A dictionary mapping input fields to output fields. Format: {input_field: output_field}
**kwargs – Additional keyword arguments are passed to pd.read_csv() or pd.read_parquet().

Notes

The TEEHR Location Crosswalk table schema includes fields: