In this chapter, we learned about retrieving, processing, and storing data in different formats. We have looked at reading and writing data from various file formats and sources, such as CSV, Excel, JSON, HDF5, HTML, pickle, table, and Parquet files. We also learned how to read and write from various relational and NoSQL databases, such as SQLite3, MySQL, MongoDB, Cassandra, and Redis.
The next chapter, Chapter 7, Cleaning Messy Data, is about the important topic of data preprocessing and feature engineering with Python. The chapter starts with exploratory data analysis, and leads to filtering, handling missing values, and outliers. After cleaning, the focus will be on data transformation, such as encoding, scaling, and splitting.
