pickle

Preferred (or most common) file extension for a Python pickle

由 徘徊边缘 提交于 2019-12-08 15:38:28
问题 This is a question from a student that I didn't have a good answer for. At times, I've seen .pickle , .pck , .pcl , and .db for files that contain Python pickles but am unsure what is the most common or best practice. I know that the latter three extensions are also used for other things. The related question is what MIME type is preferred for sending pickles between systems using a REST API? 回答1: Python 2 From the Python 2 docs: output = open('data.pkl', 'wb') I would choose .pkl as the

multiprocessing.Process subclass works on Linux but not Windows

由 浪尽此生 提交于 2019-12-08 14:19:33
I'm trying to get python-gasp working on Windows, but when I do import gasp; gasp.begin_graphics() I get the following traceback: File "C:\Python26\lib\site-packages\gasp\backend.py", line 142, in create_screen screen.updater.start() File "C:\Python26\lib\multiprocessing\process.py", line 104, in start self._popen = Popen(self) File "C:\Python26\lib\multiprocessing\forking.py", line 239, in __init__ dump(process_obj, to_child, HIGHEST_PROTOCOL) File "C:\Python26\lib\multiprocessing\forking.py", line 162, in dump ForkingPickler(file, protocol).dump(obj) File "C:\Python26\lib\pickle.py", line

EOFError Ran out of input Python

由 ε祈祈猫儿з 提交于 2019-12-08 11:37:27
问题 I am trying to use pickle to create a save file for my game, but When I try to submit my dictionary, and then take that information back, but it isn't working. import pickle data = {'health':100, 'gold':1560, 'name': 'mariano'} with open('s.txt','wb') as f: pickle.dump(data, f, protocol = 2) with open('s.txt','rb') as f: data = pickle.load(f) then when I run that code it gives me this error EOFError: Ran out of input 回答1: I figured out what went wrong, or at least how I fixed the situation.

How to load/view structure of pickled object in Ipython console ? (Windows 7, Spyder, Ipython console)

由 人盡茶涼 提交于 2019-12-08 11:33:36
问题 I am learning the program written by other programmer. So I would like to view the structure of the pickled item. Since I need to know the structure of pickled data, I am trying to load pickle in Ipython using Spyder... e.g.: import pickle data1 = {'a': [1, 2.0, 3, 4+6j], 'b': ('string', u'Unicode string'), 'c': None} selfref_list = [1, 2, 3] #selfref_list.append(selfref_list) output = open('data.pkl', 'wb') # Pickle dictionary using protocol 0. pickle.dump(data1, output) # Pickle the list

How can i fix “invalid argument: invalid 'expiry'” in Selenium when adding cookies to a chromedriver?

由 萝らか妹 提交于 2019-12-08 11:27:48
问题 “invalid argument: invalid 'expiry'” I'm trying to add cookies to a browser, but getting the following error: How to fix “invalid argument: invalid 'expiry'” in Selenium when adding cookies to a chromedriver? pickle.dump( driver.get_cookies() , open("cookies.pkl","wb")) driver.get ( URL ) sleep ( 2 ) cookies = pickle.load(open("cookies.pkl", "rb")) for cookie in cookies: driver.add_cookie(cookie) sleep(2) driver.get ( URL ) print(driver.get_cookies()) pickle.dump( driver.get_cookies() , open(

How can I pickle a python object into a csv file?

由 百般思念 提交于 2019-12-08 08:17:08
问题 I am trying to pickle a python object into a csv file. I want to write the pickle of an object as the third column in my file. I want to use pickle to avoid writing serialization for my complex objects. Code to write to csv : with open(self.file_path, 'a') as csv_file: wr = csv.writer(csv_file, delimiter='|') row = ['klines', symbol] row.extend(pickle.dumps(object)) wr.writerow(row) Code to read csv : with open(self.simulation_file_name, 'r') as csv_file: line = csv_file.readline() while line

__new__ not calling __init__ when correct class

由 血红的双手。 提交于 2019-12-08 07:54:40
问题 I have a class Vertex() With the following methods: def __new__(cls, pos_x, pos_y, size_x, size_y, text): instance = super(Vertex, cls).__new__(cls) #print 'variables {0}, {1}, {2}, {3}, {4}'.format(pos_x, pos_y, size_x, size_y, text) #print instance.__class__.__name__ return instance def __init__(self, pos_x=None, pos_y=None, size_x=None, size_y=None, text=None): print 'init' super(Vertex, self).__init__() In the method of another class I have the call: self.vertices[touch.uid] = Vertex(pos

Pickling a trained classifier yields different results from the results obtained directly from a newly but identically trained classifier

由 喜夏-厌秋 提交于 2019-12-08 07:29:04
问题 I'm trying to pickle a trained SVM classifier from the Scikit-learn library so that I don't have to train it over and over again. But when I pass the test data to the classifier loaded from the pickle, I get unusually high values for accuracy, f measure, etc. If the test data is passed directly to the classifier which is not pickled, it gives much lower values. I don't understand why pickling and unpickling the classifier object is changing the way it behaves. Can someone please help me out

multiprocessing.Process subclass works on Linux but not Windows

由 谁说胖子不能爱 提交于 2019-12-08 06:38:15
问题 I'm trying to get python-gasp working on Windows, but when I do import gasp; gasp.begin_graphics() I get the following traceback: File "C:\Python26\lib\site-packages\gasp\backend.py", line 142, in create_screen screen.updater.start() File "C:\Python26\lib\multiprocessing\process.py", line 104, in start self._popen = Popen(self) File "C:\Python26\lib\multiprocessing\forking.py", line 239, in __init__ dump(process_obj, to_child, HIGHEST_PROTOCOL) File "C:\Python26\lib\multiprocessing\forking.py

python pickle - dumping a very huge list

由 ☆樱花仙子☆ 提交于 2019-12-08 03:23:50
问题 I have two directories, each of which contains about 50,000 images, which are mostly 240x180 sizes. I want to pickle their pixel infos as training, validation, and test sets, but this apparently turns out to be very, very large, and eventually cause the computer to either free or run out of disk spaces. When the computer froze, the pkl file in the middle of being generated was 28GB. I'm not sure if this is supposed to be this large. Am I doing something wrong? Or is there a more efficient way
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