which is faster for load: pickle or hdf5 in python Given a 1 5 Gb list of pandas dataframes, which format is fastest for loading compressed data: pickle (via cPickle), hdf5, or something else in Python? I only care about fastest speed to load the d
python - How to use append with pickle? - Stack Overflow Pickle streams are entirely self-contained, and so unpickling will unpickle one object at a time Therefore, to unpickle multiple streams, you should repeatedly unpickle the file until you get an EOFError:
python - How to read pickle file? - Stack Overflow If you simply do pickle load you should be reading the first object serialized into the file (not the last one as you've written) After unserializing the first object, the file-pointer is at the beggining of the next object - if you simply call pickle load again, it will read that next object - do that until the end of the file
How to pip install pickle under Python 3. 9 in Windows? For Generalized Summary, for almost all Python versions, you never need to worry for installing 'pickle' as it comes already installed with the python interpreter Hence, simple import works: import pickle In case this doesn't work, refer to Pickle Install Problems on Stack Overflow Another suggested way is to run: pip install pickle-mixin
File extension naming: . p vs . pkl vs . pickle - Stack Overflow The extension makes no difference because "The Pickle Protocol" runs every time That is to say whenever pickle dumps or pickle loads is run the objects are serialized un-serialized according to the pickle protocol (The pickle protocol is a serialization format) The pickle protocol is python specific(and there are several versions)