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Python for Data Compression: Key Features Readability and Simplicity Clear syntax, making code easy to write and understand. Reduced development time compared to other languages. Extensive Libraries `zlib`, `gzip`, `bz2`, for common compression algorithms. `lzma` for advanced LZMA compression. `zipfile` for managing compressed archives. Cross-Platform Compatibility Runs smoothly on various operating systems (Windows, macOS, Linux). Facilitates easy deployment of data compression solutions. Community Support and Resources Abundant online tutorials, documentation, and community forums. Easier troubleshooting and faster problem-solving. Integration with other tools Seamlessly integrates with other data science tools (NumPy, Pandas). Simplifies data preprocessing and post-processing steps. **Google Search Description:** Learn key Python features for data compression. Explore its readability, extensive libraries (zlib, gzip, etc.), cross-platform compatibility ...

Python Dictionaries: A Data Compression Perspective What is a Python Dictionary? A dictionary is a fundamental data structure in Python. It stores data in key-value pairs. Keys must be immutable (e.g., strings, numbers, tuples). Values can be of any data type. Dictionaries are unordered (before Python 3.7) and mutable. Dictionaries and Data Compression Dictionaries facilitate efficient data representation. They can be used to build symbol tables for encoding/decoding in compression algorithms. Representing frequent data with shorter keys saves space. Key-value pairs can map original data to compressed representations. Example: Huffman Coding with Dictionaries Create a dictionary mapping characters to their Huffman codes. { 'A': '00', 'B': '01', 'C': '10', 'D': '11'} Use this dictionary to encode a string. Decode using the same dictionary, reversing the mapping. Example: Run-Length Encoding (R...