Data wrangling involves transforming raw data into usable formats from "summary" of Data Science For Dummies by Lillian Pierson
Data wrangling is a crucial step in the data science process that involves taking raw data and converting it into a format that is more easily usable for analysis. Raw data, straight from its source, often comes in a messy and unstructured form, making it difficult to work with. This raw data may contain missing values, errors, inconsistencies, or other issues that need to be addressed before any meaningful analysis can take place. During the data wrangling process, data scientists clean and preprocess the raw data to make it suitable for analysis. This can involve a variety of tasks, such as removing duplicates, handling missing values, correcting errors, standardizing formats, and transforming data into a more organized structure. By cleaning and preprocessing the data, data scientists can ensure that the data is accurate, complete, and ready for analysis. One of the key goals of data wrangling i...
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