MeanImputation

class geoanalytics.imputation.MeanImputation.MeanImputation(dataframe)

Bases: object

About this algorithm

Description:

MeanImputation fills missing values in a dataset by replacing them with the mean of their respective columns.

Parameters:

• dataframe (pd.DataFrame) – A Pandas DataFrame containing missing values.

• The first two columns must represent spatial/positional attributes, typically ‘x’ and ‘y’.

Attributes:

• df (pd.DataFrame) – Original dataframe with renamed first two columns (‘x’, ‘y’) and copied features.

• imputedDF (pd.DataFrame) – Stores the resulting dataframe after mean imputation.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.imputation import MeanImputation

df = pd.read_csv('data_with_nans.csv')

obj = MeanImputation(df)

imputed_df = obj.impute()

obj.save('MeanImputation.csv')

Credits

The complete program was written by and revised by under the supervision 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 mean imputation on all feature columns (excluding x and y).

Returns:

DataFrame with ‘x’, ‘y’, and imputed features.

Return type:

pd.DataFrame

save(outputFile='MeanImputation.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile (str) – File path to save the output. Defaults to ‘MeanImputation.csv’.