Pickle
Pickle is Python’s standard-library facility for serializing Python objects to byte streams and reconstructing them from those streams.
Python How-To
Pickling is Python's serialization process for converting objects into a byte stream that can be stored and later reconstructed.
- How it is used
- Preserves Python objects between program runs, especially when regenerating processed data would take time.
- How deep it goes
- Works through file and bytes operations, restoration checks, custom-class requirements, compatibility limits, and security risks without examining serialization internals.
- Examples
- Uses saved task tuples and dictionaries, a restored custom-class instance, a pickled function, and a malicious-object demonstration.
- What you can do after
- Apply it via Python’s pickle module to save and restore trusted objects, while recognizing unsupported objects and unsafe inputs.
The Quick Python Book, Fourth Edition
pickle is a Python standard-library serialization facility for converting Python objects to and from a byte stream.
- How it is used
- Persists Python data structures and program state in binary files for later restoration.
- How deep it goes
- Explains byte-stream conversion, ordered dump/load calls, supported structures, and serialization limits without implementation internals.
- Examples
- Demonstrates serializing separate scalar values, restoring them, saving a dictionary, and maintaining a disk-backed calculation cache.
- What you can do after
- Apply pickle.dump and pickle.load to save and restore Python values through binary files.
Read how The Quick Python Book, Fourth Edition explains pickle →
Fast Python: High performance techniques for large datasets
- How it is used
- Carries Python data across process boundaries and in synchronous TCP requests.
- How deep it goes
- Shows serialization and reconstruction, then details multiprocessing limits including lambda failures and transfer overhead.
- Examples
- Uses a TCP client payload and a process-pool lambda failure to illustrate serialization in practice.
- What you can do after
- Apply pickle to move data through sockets or process pools, and recognize cases requiring separate handling.
Read how Fast Python: High performance techniques for large datasets explains pickle →
Related concepts
- byte streamproduces · A byte stream is an ordered flow of bytes representing binary data for storage, processing, transmission, or incremental reading without inherent message semantics.
- multiprocessingused by · Multiprocessing is a programming technique that executes work in multiple operating-system processes, enabling concurrent or parallel execution across available processor resources.
- JSONcontrasts with · JSON is a standardized, language-independent text format for representing structured data using objects, arrays, strings, numbers, Boolean values, and null.
- lambdacontrasts with · A lambda is a language construct that defines an unnamed function or callable, often as a value.
- cachingused by · Caching is the practice of storing reusable data or computation results so later access can avoid repeating retrieval or computation.
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