Python Data Cleaning and Preparation Best Practices

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Data Grouping, Aggregation, Filtering, and Applying Functions

Data grouping and aggregation are fundamental techniques in data cleaning and preprocessing, serving several critical purposes. Firstly, they enable the summarization of large datasets, transforming extensive raw data into concise, meaningful summaries that facilitate analysis and insight derivation. Additionally, aggregation helps manage missing or noisy data by smoothing out inconsistencies and filling gaps with combined data points. These techniques also contribute to reducing data volume, enhancing processing efficiency, and creating valuable features for further analysis or machine learning models.

The main components of data grouping and aggregation include group keys, which ...