BackwardFill

class geoanalytics.imputation.BackwardFill.BackwardFill(dataframe)

Bases: object

About this algorithm

Description:

Backward Fill imputes missing values using the next valid observation, with forward fill as a fallback for leading NaNs.

Parameters:

• dataframe (pd.DataFrame) – Input dataset containing spatial columns (‘x’, ‘y’) followed by features with potential missing values.

Attributes:

• df (pd.DataFrame) – Cleaned copy of input data with ‘x’, ‘y’ as first two columns.

• imputedDF (pd.DataFrame) – Resulting DataFrame after imputation, preserving spatial columns.

Execution methods

import pandas as pd

from geoanalytics.imputation import BackwardFill

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

imputer = BackwardFill(df)

output = imputer.impute()

imputer.save("BackwardFilled.csv")

Credits

This implementation was created and revised 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()

Applies backward fill followed by forward fill to impute missing values in the feature columns.

Returns:

A DataFrame containing original ‘x’, ‘y’ columns and imputed feature columns.

save(outputFile='BackwardFilled.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile – Filename to save the resulting DataFrame.