See Section 2.2.5 for details on compression capabilities included in the Python standard library. This appendix is intended to provide readers who are unfamiliar with data compression a basic background on its techniques and theory. The final section of this appendix provides a practical example accompanied by some demonstration code of a Huffman-inspired custom encoding.
Data compression is widely used in a variety of programming contexts. All popular operating systems and programming languages have numerous tools and libraries for dealing with data compression of various sorts. The right choice of compression tools and libraries for a particular application depends on the characteristics of the data and application in question: streaming versus file; expected patterns and regularities in the data; relative importance of CPU usage, memory usage, channel demands, and storage requirements; and other factors.
Just what is data compression, anyway? The short answer is that data compression removes redundancy from data; in information-theoretic terms, compression increases the entropy of the compressed text. But those statements are essentially just true by definition. Redundancy can come in a lot of different forms. Repeated bit sequences (11111111) are one type. Repeated byte sequences are another (XXXXXXXX). But more often redundancies tend to come on a larger scale, either regularities of the data set taken as a whole, or sequences of varying lengths that are relatively common. Basically, what data compression aims at is finding algorithmic transformations of data representations that will produce more compact representations given "typical" data sets. If this description seems a bit complex to unpack, read on to find some more practical illustrations.