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Data wrangling is the process of collecting, cleaning, transforming, integrating, and organizing raw data into a structured and reliable format suitable for analysis, visualization, and machine learning. It is one of the most time-consuming phases of the data analysis lifecycle because real-world data is often incomplete, inconsistent, duplicated, incorrectly formatted, or scattered across multiple sources. The primary objective of data wrangling is to improve data quality so that analytical results are accurate, meaningful, and trustworthy. It typically involves tasks such as handling missing values, removing duplicate records, correcting inconsistencies, standardizing formats, validating data, merging datasets, filtering irrelevant information, and transforming variables into appropriate forms. An essential aspect of data wrangling is understanding data semantics. Data semantics refers to the meaning, context, and interpretation of data elements. It ensures that analysts understand what each variable represents, its unit of measurement, permissible values, relationships with other variables, and business significance. Without proper semantic understanding, data may be interpreted incorrectly, leading to inaccurate conclusions. For example, a field named "Sales" may represent gross sales in one dataset and net sales in another. Similarly, a date field may follow different formats such as DD/MM/YYYY or ____ _______ _________ __________ ____ ______ ______ ____ _____.
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