environmentaltools.spatial.fillna

environmentaltools.spatial.fillna(data, var_='z', method='nearest')[source]

Fill missing values in spatial data using interpolation from valid data.

Replaces NaN values in specified variable using spatial interpolation based on non-missing neighboring values. Useful for gap-filling in spatial datasets.

Parameters:

data (pd.DataFrame) – Spatial data containing columns ‘x’, ‘y’, and the variable to fill. Missing values should be NaN.
var (str, optional) – Name of the variable column to fill. Default is ‘z’.
method (str, optional) – Interpolation method: ‘nearest’, ‘linear’, or ‘cubic’. Default is ‘nearest’. ‘nearest’ is robust and works well for filling gaps. See scipy.interpolate.griddata for details.

Returns:

Input data with NaN values filled in the ‘z’ column using spatial interpolation from valid data points.

Return type:

pd.DataFrame

Notes

The function uses scipy.interpolate.griddata to perform the spatial interpolation. Only locations with NaN values are updated; existing valid data is preserved.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> data = pd.DataFrame({
...     'x': [0, 1, 2, 0, 1, 2],
...     'y': [0, 0, 0, 1, 1, 1],
...     'z': [10, np.nan, 30, 40, 50, np.nan]
... })
>>> filled = fillna(data, var_='z', method='nearest')