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Book Overview & Buying
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Table Of Contents
Python Data Cleaning and Preparation Best Practices
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In this chapter, we’ve explored some powerful techniques, such as grouping, aggregation, and applying custom functions. These methods are essential for summarizing and transforming data, enabling deeper insights into datasets. We’ve learned how to efficiently group data by categorical variables, such as Category and Region, and apply aggregate functions, such as sum, mean, and custom metrics to derive meaningful summaries.
Additionally, we deep-dived into the versatility of apply functions, which allow for row-wise or column-wise custom computations. Best practices, such as optimizing function efficiency, handling missing values, and understanding performance implications, were emphasized to ensure effective data processing. Finally, we discussed the strategic application of filters to refine datasets based on specific criteria, enhancing data analysis precision.
In the next chapter, we will discuss designing and optimizing data write operations to efficiently...