python-collections

Is collections.defaultdict thread-safe?

☆樱花仙子☆ 提交于 2019-11-27 23:20:28
问题 I have not worked with threading in Python at all and asking this question as a complete stranger. I am wondering if defaultdict is thread-safe. Let me explain it: I have d = defaultdict(list) which creates a list for missing keys by default. Let's say I have multiple threads started doing this at the same time: d['key'].append('value') At the end, I'm supposed to end up with ['value', 'value'] . However, if the defaultdict is not thread-safe, if the thread 1 yields to thread 2 after checking

pandas.DataFrame.from_dict not preserving order using OrderedDict

六月ゝ 毕业季﹏ 提交于 2019-11-27 23:05:49
问题 I want to import OData XML datafeeds from the Dutch Bureau of Statistics (CBS) into our database. Using lxml and pandas I thought this should be straigtforward. By using OrderDict I want to preserve the order of the columns for readability, but somehow I can't get it right. from collections import OrderedDict from lxml import etree import requests import pandas as pd # CBS URLs base_url = 'http://opendata.cbs.nl/ODataFeed/odata' datasets = ['/37296ned', '/82245NED'] feed = requests.get(base

Python collections.Counter: most_common complexity

被刻印的时光 ゝ 提交于 2019-11-27 08:15:57
What is the complexity of the function most_common provided by the collections.Counter object in Python? More specifically, is Counter keeping some kind of sorted list while it's counting, allowing it to perform the most_common operation faster than O(n) when n is the number of (unique) items added to the counter? For you information, I am processing some large amount of text data trying to find the n-th most frequent tokens. I checked the official documentation and the TimeComplexity article on the CPython wiki but I couldn't find the answer. JuniorCompressor From the source code of