以下是小编帮大家整理的python比较2个xml内容的方法，本文共3篇，仅供参考，大家一起来看看吧。
篇1：python比较2个xml内容的方法
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这篇文章主要介绍了python比较2个xml内容的方法,涉及Python操作XML文件的相关技巧,需要的朋友可以参考下
本文实例讲述了python比较2个xml内容的方法，分享给大家供大家参考。具体分析如下：
from xml.etree import ElementTree K=True main_pid = 10000 loop_depth = 0 def compare_xml(left, right, key_info=‘.‘): global loop_depth loop_depth += 1 if loop_depth == 1: print if left.tag != right.tag: print_diff(main_pid, key_info, ‘difftag‘, left.tag, right.tag) return if left.text != right.text: print_diff(main_pid, key_info, ‘difftext‘, left.text, right.text) return leftitems = dict(left.items) rightitems = dict(right.items()) for k,v in leftitems.items(): if k not in rightitems: s = ‘%s/%s‘ % (key_info, left.tag) print_diff(main_pid, s, ‘lostattr‘, k, “”) for k,v in rightitems.items(): if k not in leftitems: s = ‘%s/%s‘ % (key_info, right.tag) print_diff(main_pid, s, ‘extraattr‘, “”, k) leftnodes = left.getchildren() rightnodes = right.getchildren() leftlen = len(leftnodes) rightlen = len(rightnodes) if leftlen != rightlen: s = ‘%s/%s‘ % (key_info, right.tag) print_diff(main_pid, s, ‘difflen‘, leftlen, rightlen) return l = leftlen 调用： if __name__ == ‘__main__‘: s1 = ‘‘‘‘‘<?xml version=“1.0” encoding=“UTF-8”?>\ 希望本文所述对大家的Python程序设计有所帮助， 作者：上大王 字体：[增加 减小] 类型：转载 这篇文章主要介绍了python提取内容关键词的方法,适用于英文关键词的提取,非常具有实用价值,需要的朋友可以参考下 本文实例讲述了python提取内容关键词的方法，分享给大家供大家参考。具体分析如下： 一个非常高效的提取内容关键词的python代码，这段代码只能用于英文文章内容，中文因为要分词，这段代码就无能为力了，不过要加上分词功能，效果和英文是一样的。 代码如下: # coding=UTF-8 import nltk from nltk.corpus import brown # This is a fast and simple noun phrase extractor (based on NLTK) # Feel free to use it, just keep a link back to this post # thetokenizer//05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/ # Create by Shlomi Babluki # May, 2013 # This is our fast Part of Speech tagger ############################################################################# brown_train = brown.tagged_sents(categories=‘news‘) regexp_tagger = nltk.RegexpTagger( [(r‘^-?[0-9]+(.[0-9]+)?$‘, ‘CD‘), (r‘(-|:|;)$‘, ‘:‘), (r‘\‘*$‘, ‘MD‘), (r‘(The|the|A|a|An|an)$‘, ‘AT‘), (r‘.*able$‘, ‘JJ‘), (r‘^[A-Z].*$‘, ‘NNP‘), (r‘.*ness$‘, ‘NN‘), (r‘.*ly$‘, ‘RB‘), (r‘.*s$‘, ‘NNS‘), (r‘.*ing$‘, ‘VBG‘), (r‘.*ed$‘, ‘VBD‘), (r‘.*‘, ‘NN‘) ]) unigram_tagger = nltk.UnigramTagger(brown_train, backoff=regexp_tagger) bigram_tagger = nltk.BigramTagger(brown_train, backoff=unigram_tagger) ############################################################################# # This is our semi-CFG; Extend it according to your own needs ############################################################################# cfg = {} cfg[“NNP+NNP”] = “NNP” cfg[“NN+NN”] = “NNI” cfg[“NNI+NN”] = “NNI” cfg[“JJ+JJ”] = “JJ” cfg[“JJ+NN”] = “NNI” ############################################################################# class NPExtractor(object): def __init__(self, sentence): self.sentence = sentence # Split the sentence into singlw words/tokens def tokenize_sentence(self, sentence): tokens = nltk.word_tokenize(sentence) return tokens # Normalize brown corpus‘ tags (“NN”, “NN-PL”, “NNS” >“NN”) def normalize_tags(self, tagged): n_tagged = [] for t in tagged: if t[1] == “NP-TL” or t[1] == “NP”: n_tagged.append((t[0], “NNP”)) continue if t[1].endswith(“-TL”): n_tagged.append((t[0], t[1][:-3])) continue if t[1].endswith(“S”): n_tagged.append((t[0], t[1][:-1])) continue n_tagged.append((t[0], t[1])) return n_tagged # Extract the main topics from the sentence def extract(self): tokens = self.tokenize_sentence(self.sentence) tags = self.normalize_tags(bigram_tagger.tag(tokens)) merge = True while merge: merge = False for x in range(0, len(tags) - 1): t1 = tags[x] t2 = tags[x + 1] key = “%s+%s” % (t1[1], t2[1]) value = cfg.get(key, ‘‘) if value: merge = True tags.pop(x) tags.pop(x) match = “%s %s” % (t1[0], t2[0]) pos = value tags.insert(x, (match, pos)) break matches = [] for t in tags: if t[1] == “NNP” or t[1] == “NNI”: #if t[1] == “NNP” or t[1] == “NNI” or t[1] == “NN”: matches.append(t[0]) return matches # Main method, just run “python np_extractor.py” def main(): sentence = “Swayy is a beautiful new dashboard for discovering and curating online content.” np_extractor = NPExtractor(sentence) result = np_extractor.extract() print “This sentence is about: %s” % “, ”.join(result) if __name__ == ‘__main__‘: main() 希望本文所述对大家的Python程序设计有所帮助， python访问sqlserver示例 Python open读写文件实现脚本 python 控制语句 python计算程序开始到程序结束的运行时间 Python去掉字符串中空格的方法 python函数缺省值与引用学习笔记分享 python原始套接字编程示例分享 Python里隐藏的“禅” 删除目录下相同文件的python代码(逐级优化 python抓取京东价格分析京东商品价格走势 Python入门教程 超详细1小时学会 python 中文乱码问题深入分析 比较详细Python正则表达式操作指 Python字符串的encode与decode研 Python open读写文件实现脚本 Python enumerate遍历数组示例应 Python 深入理解yield Python+Django在windows下的开发 python 文件和路径操作函数小结 python 字符串split的用法分享
篇2：python提取内容关键词的方法
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