pickle — Python object serialization — Python 3. 14. 0 documentation The pickle module implements binary protocols for serializing and de-serializing a Python object structure “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,” [1] or “flattening”; however, to avoid
pickle5 · PyPI This package backports all features and APIs added in the pickle module in Python 3 8 3, including the PEP 574 additions It should work with Python 3 5, 3 6 and 3 7 Basic usage is similar to the pickle module, except that the module to be imported is pickle5: import pickle5 as pickle pb = pickle PickleBuffer(b"foo") data = pickle dumps(pb, protocol=5) assert pickle loads(data) == b"foo" Detailed documentation can be found in PEP 574 and the standard pickle documentation
How to Install and Use Pickle for Python 3 - AskPython Pickle is an incredibly useful Python module for serializing and deserializing Python object structures By “serializing”, we mean converting a Python object hierarchy into a byte stream And by “deserializing”, we mean reconstructing the object hierarchy from the byte stream In this guide, we will cover these topics in detail using practical, real-world examples you can try for yourself By the end, you’ll be a Pickle pro! What is Pickle, and Why Use It? Pickle allows you to save
pandas. DataFrame. to_pickle — pandas 2. 3. 3 documentation See also read_pickle Load pickled pandas object (or any object) from file DataFrame to_hdf Write DataFrame to an HDF5 file DataFrame to_sql Write DataFrame to a SQL database DataFrame to_parquet Write a DataFrame to the binary parquet format