<![CDATA[This study aims to develop a structured data wrangling pipeline for TikTok datasets using the Pandas Python library. The purpose of the research is to transform raw social media data into clean, consistent, and analyzable formats that can support academic inquiry into digital engagement patterns.The methodology consists of five stages: data loading, cleansing, transformation, feature engineering, and validation. Raw TikTok data, including video metadata, user interactions (likes, comments, shares), and hashtags, were processed to remove inconsistencies, handle missing values, and standardize formats. Feature engineering was applied to derive analytical variables such as engagement rate, posting frequency, and hashtag clustering. Validation ensured structural integrity, completeness, and consistency of the dataset, enabling reliable statistical analysis. The results demonstrate that systematic wrangling improves dataset quality, enhances interpretability, and enables advanced analysis of user behavior and content trends. By applying Pandas-based operations, the study provides a reproducible framework that bridges technical rigor with methodological transparency. This research contributes to the academic field of social media analytics by offering a practical pipeline for TikTok data preparation. It highlights the importance of data wrangling not merely as a preparatory step, but as a methodological foundation for evidence-based digital research.]]>
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