best way to preserve numpy arrays on disk
I am looking for a fast way to preserve large numpy arrays. I want to save them to the disk in a binary format, then read them back into memory relatively fastly. cPickle is not fast enough, unfortunately.
pickle
Unpickling a python 2 object with python 3
I’m wondering if there is a way to load an object that was pickled in Python 2.4, with Python 3.4.
Unable to load files using pickle and multiple modules
I’m trying to create a user system, which uses a setting and Gui module, and when the GUI module requests for the file to load up using pickle, I keep getting an attribute error. this is from the settings module:
What can multiprocessing and dill do together?
I would like to use the multiprocessing library in Python. Sadly multiprocessing uses pickle which doesn’t support functions with closures, lambdas, or functions in __main__. All three of these are important to me
Python serialization – Why pickle?
I understood that Python pickling is a way to ‘store’ a Python Object in a way that does respect Object programming – different from an output written in txt file or DB.
Pickle or json?
I need to save to disk a little dict object whose keys are of the type str and values are ints and then recover it. Something like this:
Why do I get “Pickle – EOFError: Ran out of input” reading an empty file?
I am getting an interesting error while trying to use Unpickler.load(), here is the source code:
Hitting Maximum Recursion Depth Using Pickle / cPickle
The background: I’m building a trie to represent a dictionary, using a minimal construction algorithm. The input list is 4.3M utf-8 strings, sorted lexicographically. The resulting graph is acyclic and has a maximum depth of 638 nodes. The first line of my script sets the recursion limit to 1100 via sys.setrecursionlimit().
Python: Can’t pickle type X, attribute lookup failed
I am trying to pickle a namedtuple:
Can’t pickle defaultdict
I have a defaultdict that looks like this: