Against the backdrop of the big data era

structured data

boasting standardized formatting

serves as a core carrier for data analysis

curriculum experiments and diverse data applications. Raw structured data generally suffers from quality defects including missing values

outliers and redundant records

which severely undermine the accuracy of data mining and the effectiveness of project practice. To address the drawbacks of traditional data cleaning such as low efficiency

poor universality and high labor costs

this paper explores core technologies and implementation paths for structured big data cleaning based on Python. Combined with data preprocessing theories and various practical scenarios

it summarizes the application advantages of Python tool libraries

standardizes standardized processing workflows for missing values

outliers and duplicate data

and optimizes cleaning schemes adapted to structured data scenarios. The research can provide effective technical methods and practical references for curriculum design

graduation projects and data analysis programs.