ModeImputation

class geoanalytics.imputation.ModeImputation.ModeImputation(dataframe)

Bases: object

About this algorithm

Description:

Mode Imputation replaces missing values in each column of a dataset with the most frequent value (mode) of that column. It is a simple and fast technique for handling missing data, particularly useful when the dataset has categorical features or dominant repeated values.

Parameters:

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

• No parameters are needed for run().

Attributes:

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

• imputedDF (pd.DataFrame) – DataFrame after filling in missing values with mode.

• startTime, endTime (float) – Variables to track execution time.

• memoryUSS, memoryRSS (float) – Memory usage of the imputation process in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.imputation import ModeImputation

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

mode_imputer = ModeImputation(df)

imputed_df = mode_imputer.run()

mode_imputer.getRuntime()
mode_imputer.getMemoryUSS()
mode_imputer.getMemoryRSS()

mode_imputer.save('ModeImputation.csv')

Credits

This implementation was created by Raashika and revised by M.Charan Teja 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 clustering algorithm.

run()

Executes the mode imputation algorithm by filling missing values with column-wise mode.

Returns:

imputedDF (pd.DataFrame) – DataFrame with missing values filled

save(outputFile='ModeImputation.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile – str, filename to save the imputed data (default: ‘ModeImputation.csv’)