Interpolation

class geoanalytics.imputation.Interpolation.Interpolation(dataframe)

Bases: object

About this algorithm

Description:

Interpolation is a missing data imputation technique that estimates NaN values using linear interpolation between known values. It fills values in both forward and backward directions to ensure completeness.

Parameters:

• Dataset (pandas DataFrame) must be provided during object initialization.

• No other parameters are required during instantiation.

Attributes:

• df (pd.DataFrame) – The input data with ‘x’, ‘y’ coordinates and feature columns.

• imputedDF (pd.DataFrame) – DataFrame containing ‘x’, ‘y’, and imputed values.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.imputation import Interpolation

df = pd.read_csv("input.csv")

ip = Interpolation(df)

output = ip.impute()

ip.save('Interpolation.csv')

Credits

This implementation was created by and revised by under the guidance of Professor Rage Uday Kiran.

getMemoryRSS()

Prints the memory usage (RSS) of the process in kilobytes.

getMemoryUSS()

Prints the memory usage (USS) of the process in kilobytes.

getRuntime()

Prints the total runtime of the algorithm.

run()

Performs linear interpolation to fill missing values in the dataset.

The method: - Separates ‘x’ and ‘y’ columns from the rest of the data. - Applies linear interpolation along each column. - Uses both forward and backward filling to handle edge NaNs. - Reconstructs the complete DataFrame with imputed values.

Returns:

Imputed DataFrame with original ‘x’, ‘y’ columns and interpolated features.

save(outputFile='Interpolation.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile – The filename for saving the DataFrame. Defaults to ‘Interpolation.csv’.