這篇文章帶給大家的內容是關於python3中setdefault的用法介紹(程式碼) ,有一定的參考價值,有需要的朋友可以參考一下,希望對你有所幫助。
當字典d[k]找不到正確的鍵時,Python會拋出異常,有沒有一種優雅的方法來避免這種情況呢?答案是肯定的.
#index0.py 從索引中取得單字出現的頻率資訊,並寫入清單 --沒有使用dict.setdefault
#!/usr/bin/env python # coding=utf-8 import sys, re WORD_RE = re.compile(r'\w+') index = {} with open(sys.argv[1], encoding='utf-8') as fp: for line_no, line in enumerate(fp, 1): for match in WORD_RE.finditer(line): word = match.group() column_no = match.start()+1 location = (line_no, column_no) occurrences = index.get(word, []) occurrences.append(location) index[word] = occurrences for word in sorted(index, key=str.upper): print(word, index[word])
The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!
a [(19, 48), (20, 53)] Although [(11, 1), (16, 1), (18, 1)] ambiguity [(14, 16)] and [(15, 23)] are [(21, 12)] aren [(10, 15)] at [(16, 38)] bad [(19, 50)] be [(15, 14), (16, 27), (20, 50)] beats [(11, 23)] Beautiful [(3, 1)] better [(3, 14), (4, 13), (5, 11), (6, 12), (7, 9), (8, 11), (17, 8), (18, 25)] break [(10, 40)] by [(1, 20)] cases [(10, 9)] ...
#!/usr/bin/env python # coding=utf-8 import sys, re WORD_RE = re.compile(r'\w+') index = {} with open(sys.argv[1], encoding='utf-8') as fp: for line_no, line in enumerate(fp, 1): for match in WORD_RE.finditer(line): word = match.group() column_no = match.start()+1 location = (line_no, column_no) index.setdefault(word, []).append(location) for word in sorted(index, key=str.upper): print(word, index[word])
my_dict.setdefault(key, []).append(new_value)
if key not in my_dict: my_dict[key] = [] my_dict[key].append(new_value)
二者效果相同,只是setdefault只需一次就完成整個操作,而後者需要進行兩次查詢
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