From Raw to Refined: Python's Touch on Data Cleaning

Abstract

This paper employs Python, particularly the pandas library, as a powerful tool for navigating and transforming data. Beginning with dataset loading and exploratory analysis, it delves into crucial techniques like handling missing values, selecting pertinent columns, and converting categorical variables. The process includes imputation methods, numeric data adjustments, and categorical data transformation through one-hot encoding. This guide culminates in a validated and polished dataset, emphasizing Python's efficacy in elevating data quality and setting the stage for robust analysis, underlining the importance of pristine data in the analytical pipeline.