data

data

Functions

Name	Description
data	Fetch univariate or bivariate data for a specific source, year, NUTS level, and selected filters.

data

data.data(
    x_source,
    y_source=None,
    year=None,
    level=None,
    x_filters=None,
    y_filters=None,
    limit=2500,
)

Fetch univariate or bivariate data for a specific source, year, NUTS level, and selected filters.

Args: x_source (str): Source name for the x variable. y_source (str, optional): Source name for the y variable. Default is None. year (int): The year for data. level (str): The NUTS level (“0”, “1”, “2”, “3”). x_filters (dict): Filters for the x variable as a dictionary of field-value pairs. y_filters (dict, optional): Filters for the y variable as a dictionary of field-value pairs. Default is None. limit (int): Maximum number of results to return. Default is 2500. This default should be enough for most uses, as it is well above the number of NUTS 3 regions in the EU. The maximum allowed by the API is 10,000.

Returns: pd.DataFrame: A DataFrame containing univariate or bivariate data with the following columns:

- `geo` (str): (NUTS) region code at the requested level.
- `geo_name` (str): Name of the (NUTS) region.
- `geo_source` (str): Source type of the spatial units (e.g., "NUTS").
- `geo_year` (int): Year of the (NUTS) region classification.
- `x_year` (int): The year of the predictor variable (X) (renamed from `predictor_year` or `data_year`).
- `y_year` (int, optional): The year of the outcome variable (Y) (renamed from `outcome_year`).
- `x` (float): The value of the univariate variable.
- `y` (float, optional): The value of the y variable (only included when `y_source` is provided).

Notes: - If y_source is not provided, the returned DataFrame contains only univariate data (x). - If y_source is provided, the returned DataFrame includes both predictor (x) and outcome (y) variables. - Some regions may have missing (NaN) values for x or y, indicating unavailable data.

Example:

# Univariate example
import mapineqpy as mi
mi.data(
    x_source="TGS00010",
    year=2020,
    level="2",
    x_filters={"isced11": "TOTAL", "unit": "PC", "age": "Y_GE15", "freq": "A"}
)

# Bivariate example
import mapineqpy as mi
mi.data(
    x_source="TGS00010",
    y_source="DEMO_R_MLIFEXP",
    year=2020,
    level="2",
    x_filters={"isced11": "TOTAL", "unit": "PC", "age": "Y_GE15", "freq": "A"},
    y_filters={"unit": "YR", "age": "Y_LT1", "freq": "A"}
)