In Python 2.x and 3.0, 3.1 and 3.2, multiprocessing.Pool() objects are not context managers. You cannot use them in a with statement. Only in Python 3.3 and up can you use them as such. From the Python 3 multiprocessing.Pool() documentation: New in version 3.3: Pool objects now support the context management protocol – see Context … Read more
It’s a trivial mistake: pickle.dump(obj,file) takes a file object, not a file name. What I need is something like: with open(“a-file.pickle”,’wb’) as f: pickle.dump({“a dict”:True},f)
It is because you are setting Test.A as a class attribute instead of an instance attribute. Really what is happening is that with the test1.py, the object being read back from the pickle file is the same as test2.py, but its using the class in memory where you had originally assigned x.A. When your data … Read more
The second argument to cPickle.dump() must be a file object. You passed in a string containing a filename instead. You need to use the open() function to open a file object for that filename, then pass the file object to cPickle: with open(outfile, ‘wb’) as pickle_file: cPickle.dump(all, pickle_file) See the Reading and Writing Files section … Read more
For Python 3.2+/2.7+ you can view (__repr__‘s of) pickles from the command-line: $ python -c “import pickle; pickle.dump({‘hello’: ‘world’}, open(‘obj.dat’, ‘wb’))” $ python -mpickle obj.dat {‘hello’: ‘world’} It should be easy to integrate this into the Windows shell.
Pickle is unsafe because it constructs arbitrary Python objects by invoking arbitrary functions. However, this is also gives it the power to serialize almost any Python object, without any boilerplate or even white-/black-listing (in the common case). That’s very desirable for some use cases: Quick & easy serialization, for example for pausing and resuming a … Read more
I know this is a very old question, but I have never explicitly seen a satisfactory solution to this question other than the obvious, and most likely correct, answer to re-structure your code. Unfortunately, it is not always practical to do such a thing, in which case as a very last resort, it is possible … Read more
csv ✅human readable ✅cross platform ⛔slower ⛔more disk space ⛔doesn’t preserve types in some cases pickle ✅fast saving/loading ✅less disk space ⛔non human readable ⛔python only Also take a look at parquet format (to_parquet, read_parquet) ✅fast saving/loading ✅less disk space than pickle ✅supported by many platforms ⛔non human readable
You should use numpy.save and numpy.load.
I needed to achieve the same thing too. I turns out it caused me quite a headache before I finally figured out, thanks to this post, how to actually make it work in a binary format. To insert/update: pdata = cPickle.dumps(data, cPickle.HIGHEST_PROTOCOL) curr.execute(“insert into table (data) values (:data)”, sqlite3.Binary(pdata)) You must specify the second argument … Read more