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Data wrangling is the process of cleaning, transforming, and mapping raw data into a more suitable format for analysis. It involves identifying and correcting errors in the data, handling missing or incomplete data, and converting data into a standardized format. Data wrangling is an important step in the data analysis process as it ensures that the data is accurate, consistent, and complete. It also helps to make the data more accessible and easier to work with, which can save time and improve the accuracy of the analysis. Data wrangling can be a time-consuming and complex process, but it is essential for ensuring that the data is of high quality and can be used effectively for analysis.
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