Wave¶

Wave ¶

Wave(year: str, wave_folder: str, country: Country)

A single survey wave within a country.

Typically obtained via bracket notation on a :class:Country::

wave = uga['2019-20']
df = wave.household_roster()

Parameters:

Name	Type	Description	Default
`year`	`str`	Wave label (e.g. `'2019-20'`).	required
`wave_folder`	`str`	Subdirectory name on disk (may differ from year for multi-round surveys).	required
`country`	`Country`	Parent country instance.	required

data_scheme `property` ¶

data_scheme: list[str]

resources `property` ¶

resources: dict[str, Any]

Load the data_info.yml that describes table structure, merges, etc.

formatting_functions `property` ¶

formatting_functions: dict[str, Callable[..., Any]]

column_mapping ¶

column_mapping(request: str, data_info: dict[str, Any] | None = None) -> dict[str, Any]

Retrieve column mappings for a given dataset request and map into a dictionary to be ready for df_data_grabber.

Input: request: str, the request data name in data_scheme (e.g. 'cluster_features', 'household_roster', 'food_acquired', 'interview_date')

Output: final_mapping: dict, {file_name: {'idxvars': idxvar_dic, 'myvars': myvars_dic}}

Example: {'data_file.dta': {'idxvars': {'cluster': ('cluster', )}), 'myvars': {'region': ('region', ), 'urban': ('urban', )}}}

grab_data ¶

grab_data(request: str) -> pd.DataFrame

get data from the data file Input: request: str, the request data name (e.g. 'cluster_features', 'household_roster', 'food_acquired', 'interview_date') Output: df: pd.DataFrame, the data requested