Python解析xml文档实战案例

xml文档

<?xml version="1.0" ?>
<!DOCTYPE PubmedArticleSet PUBLIC "-//NLM//DTD PubMedArticle, 1st January 2019//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/out/pubmed_190101.dtd">
<PubmedArticleSet>
<PubmedArticle>
    <MedlineCitation Status="MEDLINE" Owner="NLM">
        <PMID Version="1">28901317</PMID>
        <DateCompleted>
            <Year>2018</Year>
            <Month>05</Month>
            <Day>10</Day>
        </DateCompleted>
        <DateRevised>
            <Year>2018</Year>
            <Month>12</Month>
            <Day>02</Day>
        </DateRevised>
        <Article PubModel="Print">
            <Journal>
                <ISSN IssnType="Electronic">1998-4138</ISSN>
                <JournalIssue CitedMedium="Internet">
                    <Volume>13</Volume>
                    <Issue>4</Issue>
                    <PubDate>
                        <Year>2017</Year>
                    </PubDate>
                </JournalIssue>
                <Title>Journal of cancer research and therapeutics</Title>
                <ISOAbbreviation>J Cancer Res Ther</ISOAbbreviation>
            </Journal>
            <ArticleTitle><i>k-RAS</i> mutation and resistance to epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with nonsmall cell lung cancer.</ArticleTitle>
            <Pagination>
                <MedlinePgn>699-701</MedlinePgn>
            </Pagination>
            <ELocationID EIdType="doi" ValidYN="Y">10.4103/jcrt.JCRT_468_17</ELocationID>
            <Abstract>
                <AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">The aim of this study was to evaluate the relationship between k-RAS gene mutation and the resistance to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) treatment in patients with nonsmall-cell lung cancer (NSCLC).</AbstractText>
                <AbstractText Label="METHODS" NlmCategory="METHODS">Forty-five pathologies confirmed NSCLC patients who received EGFR-TKI (Gefitinib) treatment were retrospectively included in this study. The mutation of codon 12 and 13, located in exon1 and exon 2 of k-RAS gene were examined by polymerase chain reaction (PCR) and DAN sequencing in tumor samples of the included 45 NSCLC patients. The correlation between Gefitinib treatment response and k-RAS mutation status was analyzed in tumor samples of the 45 NSCLC patients.</AbstractText>
                <AbstractText Label="RESULTS" NlmCategory="RESULTS">Eight tumor samples of the 45 NSCLC patients were found to be mutated in coden 12 or 13, with an mutation rate of 17.8% (8/45); the objective response rate (ORR) was 29.7%(11/37) with 1 cases of complete response (CR) and 10 cases of partial response in k-RAS mutation negative patients. Furthermore, the ORR was 0.0% in k-RAS mutation positive patients with none CR. The ORR between k-RAS mutation and nonmutation patients were significant different (P < 0.05).</AbstractText>
                <AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">k-RAS gene mutation status was associated with the response of Gefitinib treatment in patients with NSCLC.</AbstractText>
            </Abstract>
            <AuthorList CompleteYN="Y">
                <Author ValidYN="Y">
                    <LastName>Zhou</LastName>
                    <ForeName>Bin</ForeName>
                    <Initials>B</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, Province 325200, PR China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Tang</LastName>
                    <ForeName>Congrong</ForeName>
                    <Initials>C</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, Province 325200, PR China.</Affiliation>
                    </AffiliationInfo>
                </Author>
                <Author ValidYN="Y">
                    <LastName>Li</LastName>
                    <ForeName>Jie</ForeName>
                    <Initials>J</Initials>
                    <AffiliationInfo>
                        <Affiliation>Department of Pharmacy, Ruian People's Hospital, Ruian, Zhejiang, Province 325200, PR China.</Affiliation>
                    </AffiliationInfo>
                </Author>
            </AuthorList>
            <Language>eng</Language>
            <PublicationTypeList>
                <PublicationType UI="D016428">Journal Article</PublicationType>
            </PublicationTypeList>
        </Article>
        <MedlineJournalInfo>
            <Country>India</Country>
            <MedlineTA>J Cancer Res Ther</MedlineTA>
            <NlmUniqueID>101249598</NlmUniqueID>
            <ISSNLinking>1998-4138</ISSNLinking>
        </MedlineJournalInfo>
        <ChemicalList>
            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="C117307">KRAS protein, human</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D047428">Protein Kinase Inhibitors</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>0</RegistryNumber>
                <NameOfSubstance UI="D011799">Quinazolines</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>EC 2.7.10.1</RegistryNumber>
                <NameOfSubstance UI="C512478">EGFR protein, human</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>EC 2.7.10.1</RegistryNumber>
                <NameOfSubstance UI="D066246">ErbB Receptors</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>EC 3.6.5.2</RegistryNumber>
                <NameOfSubstance UI="D016283">Proto-Oncogene Proteins p21(ras)</NameOfSubstance>
            </Chemical>
            <Chemical>
                <RegistryNumber>S65743JHBS</RegistryNumber>
                <NameOfSubstance UI="D000077156">Gefitinib</NameOfSubstance>
            </Chemical>
        </ChemicalList>
        <CitationSubset>IM</CitationSubset>
        <MeshHeadingList>
            <MeshHeading>
                <DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D002289" MajorTopicYN="N">Carcinoma, Non-Small-Cell Lung</DescriptorName>
                <QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName>
                <QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
                <QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D019008" MajorTopicYN="N">Drug Resistance, Neoplasm</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D066246" MajorTopicYN="N">ErbB Receptors</DescriptorName>
                <QualifierName UI="Q000037" MajorTopicYN="N">antagonists & inhibitors</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D000077156" MajorTopicYN="N">Gefitinib</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D009154" MajorTopicYN="N">Mutation</DescriptorName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D047428" MajorTopicYN="N">Protein Kinase Inhibitors</DescriptorName>
                <QualifierName UI="Q000008" MajorTopicYN="Y">administration & dosage</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D016283" MajorTopicYN="N">Proto-Oncogene Proteins p21(ras)</DescriptorName>
                <QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName>
            </MeshHeading>
            <MeshHeading>
                <DescriptorName UI="D011799" MajorTopicYN="N">Quinazolines</DescriptorName>
                <QualifierName UI="Q000008" MajorTopicYN="Y">administration & dosage</QualifierName>
            </MeshHeading>
        </MeshHeadingList>
    </MedlineCitation>
    <PubmedData>
        <History>
            <PubMedPubDate PubStatus="entrez">
                <Year>2017</Year>
                <Month>9</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="pubmed">
                <Year>2017</Year>
                <Month>9</Month>
                <Day>14</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
            <PubMedPubDate PubStatus="medline">
                <Year>2018</Year>
                <Month>5</Month>
                <Day>11</Day>
                <Hour>6</Hour>
                <Minute>0</Minute>
            </PubMedPubDate>
        </History>
        <PublicationStatus>ppublish</PublicationStatus>
        <ArticleIdList>
            <ArticleId IdType="pubmed">28901317</ArticleId>
            <ArticleId IdType="pii">JCanResTher_2017_13_4_699_214476</ArticleId>
            <ArticleId IdType="doi">10.4103/jcrt.JCRT_468_17</ArticleId>
        </ArticleIdList>
    </PubmedData>
</PubmedArticle>

</PubmedArticleSet>

  方法一:xml.etree.cElementTre

# -*- coding: utf-8 -*-

"""
@Datetime: 2019/4/25
@Author: Zhang Yafei
"""
import os
import re
import threading
import xml.etree.cElementTree as ET
from concurrent.futures import ThreadPoolExecutor
from itertools import chain

import pandas as pd


def pubmed_xml_parser(path):
    dir_name = path.split('\\')[0]
    print(dir_name)
    etree = ET.parse(path)
    root = etree.getroot()
    data_list = []
    pmid_set = []
    for articles in root.iter('PubmedArticle'):
        pmid = articles.find('MedlineCitation').find('PMID').text
        if pmid in pmid_set:
            continue
        pmid_set.append(pmid)
        Article = articles.find('MedlineCitation').find('Article')
        journal = Article.find('Journal').find('ISOAbbreviation').text
        try:
            authors = Article.find('AuthorList').findall('Author')
            affiliations_info = set()
            for author in authors:
                # author_name = author.find('LastName').text + ' ' + author.find('ForeName').text
                affiliations = [x.find('Affiliation').text for x in author.findall('AffiliationInfo')]
                # author = author_name + ':' + ';'.join(affiliations)
                for affiliation in affiliations:
                    affiliations_info.add(affiliation)
            affiliations_info = ';'.join(affiliations_info)
        except AttributeError:
            affiliations_info = ''
        try:
            date = Article.find('Journal').find('JournalIssue').find('PubDate').find('Year').text
        except AttributeError:
            date = Article.find('Journal').find('JournalIssue').find('PubDate').find('MedlineDate').text
            date = re.search('\d+', date).group(0)
        try:
            mesh_words = []
            for mesh_heading in articles.find('MedlineCitation').find('MeshHeadingList').findall('MeshHeading'):
                if len(list(mesh_heading)) == 1:
                    mesh_words.append(list(mesh_heading)[0].text)
                    continue
                mesh_name = ''
                for mesh in mesh_heading:
                    if mesh.tag == 'DescriptorName':
                        mesh_name = mesh.text
                        continue
                    if mesh_name and mesh.tag == 'QualifierName':
                        mesh_word = mesh_name + '/' + mesh.text
                        mesh_words.append(mesh_word)
            mesh_words = ';'.join(mesh_words)
        except AttributeError:
            print(articles.find('MedlineCitation').find('PMID').text)
            mesh_words = ''
        article_type = '/'.join([x.text for x in Article.find('PublicationTypeList').getchildren()])
        country = articles.find('MedlineCitation').find('MedlineJournalInfo').find('Country').text
        data_list.append(
            {'PMID': pmid, 'journal': journal, 'affiliations_info': affiliations_info, 'pub_year': date,
             'mesh_words': mesh_words,
             'country': country, 'article_type': article_type, 'file_path': path})
        print(pmid + '\t解析完成')
    df = pd.DataFrame(data_list)
    with threading.Lock():
        df.to_csv('{}.csv'.format(dir_name), encoding='utf_8_sig', mode='a', index=False, header=False)


def to_excel(data, path):
    writer = pd.ExcelWriter(path)
    data.to_excel(writer, sheet_name='table', index=False)
    writer.save()


def get_files_path():
    for base_path, folders, files in os.walk('first in class drug'):
        file_list = [os.path.join(base_path, file) for file in files if file.endswith('.xml')]
    for base_path, folders, files in os.walk('follow on drug'):
        file_list.extend([os.path.join(base_path, file) for file in files if file.endswith('.xml')])
    for base_path, folders, files in os.walk('me too drug'):
        file_list.extend([os.path.join(base_path, file) for file in files if file.endswith('.xml')])
    if os.path.exists('first in class drug.csv') or os.path.exists('follow on drug.csv') or os.path.exists(
            'me too drug.csv'):
        if os.path.exists('first in class drug.csv'):
            df = pd.read_csv('first in class drug.csv', encoding='utf-8')
            has_files_list = df.file_path.tolist()
        if os.path.exists('follow on drug.csv'):
            df = pd.read_csv('follow on drug.csv', encoding='utf-8')
            has_files_list = chain(has_files_list, df.file_path.tolist())
        if os.path.exists('me too drug.csv'):
            df = pd.read_csv('me too drug.csv', encoding='utf-8')
            has_files_list = chain(has_files_list, df.file_path.tolist())
        print('共需解析文件:{0}'.format(len(file_list)))
        has_files_list = set(has_files_list)
        file_list = set(file_list) - has_files_list
        print('已解析文件:{0}'.format(len(has_files_list)))
    else:
        df = pd.DataFrame(
            columns=['PMID', 'affiliations_info', 'article_type', 'country', 'file_path', 'journal', 'mesh_words',
                     'pub_year'])
        df.to_csv('follow on drug.csv', encoding='utf_8_sig', index=False)
        df.to_csv('first in class drug.csv', encoding='utf_8_sig', index=False)
        df.to_csv('me too drug.csv', encoding='utf_8_sig', index=False)
        print('共需解析文件:{0}'.format(len(file_list)))
        print('已解析文件:0')
    return file_list


if __name__ == '__main__':
    files_list = get_files_path()
    if not files_list:
        print('全部解析完成')
    else:
        with ThreadPoolExecutor(max_workers=os.cpu_count()) as pool:
            pool.map(pubmed_xml_parser, files_list)

  方法二:lxml+xpath

# -*- coding: utf-8 -*-

"""
@Datetime: 2019/4/26
@Author: Zhang Yafei
"""
import os
import re
import threading
from concurrent.futures import ThreadPoolExecutor

from lxml import etree
import pandas as pd


def pubmed_xpath_parse(path):
    tree = etree.parse(path)
    # 如果xml数据中出现了关于dtd的声明(如下面的例子),那样的话,必须在使用lxml解析xml的时候,进行相应的声明。
    # parser = etree.XMLParser(load_dtd=True)  # 首先根据dtd得到一个parser(注意dtd文件要放在和xml文件相同的目录)
    # tree = etree.parse('1.xml', parser=parser)  # 用上面得到的parser将xml解析为树结构
    data_list = []
    pmid_set = []
    for articles in tree.xpath('//PubmedArticle'):
        # pmid = articles.xpath('MedlineCitation/PMID')[0].xpath('string()')
        pmid = articles.xpath('MedlineCitation/PMID/text()')[0]
        if pmid in pmid_set:
            continue
        pmid_set.append(pmid)
        Article = articles.xpath('MedlineCitation/Article')[0]
        journal = Article.xpath('Journal/ISOAbbreviation/text()')[0]
        try:
            authors = Article.xpath('AuthorList/Author')
            affiliations_info = set()
            for author in authors:
                # author_name = author.find('LastName').text + ' ' + author.find('ForeName').text
                affiliations = [x.xpath('Affiliation/text()')[0] for x in author.xpath('AffiliationInfo')]
                # author = author_name + ':' + ';'.join(affiliations)
                for affiliation in affiliations:
                    affiliations_info.add(affiliation)
            affiliations_info = ';'.join(affiliations_info)
        except AttributeError:
            affiliations_info = ''
        try:
            date = Article.xpath('Journal/JournalIssue/PubDate/Year/text()')[0]
        except IndexError:
            date = Article.xpath('Journal/JournalIssue/PubDate/MedlineDate/text()')[0]
            date = re.search('\d+', date).group(0)
        try:
            mesh_words = []
            for mesh_heading in articles.xpath('MedlineCitation/MeshHeadingList/MeshHeading'):
                if len(mesh_heading.xpath('child::*')) == 1:
                    mesh_words.append((mesh_heading.xpath('child::*'))[0].text)
                    continue
                mesh_name = ''
                for mesh in mesh_heading.xpath('child::*'):
                    if mesh.tag == 'DescriptorName':
                        mesh_name = mesh.xpath('string()')
                        continue
                    if mesh_name and mesh.tag == 'QualifierName':
                        mesh_word = mesh_name + '/' + mesh.xpath('string()')
                        mesh_words.append(mesh_word)
            mesh_words = ';'.join(mesh_words)
        except AttributeError:
            mesh_words = ''
        article_type = '/'.join([x.xpath('./text()')[0] for x in Article.xpath('PublicationTypeList/PublicationType')])
        country = articles.xpath('MedlineCitation/MedlineJournalInfo/Country/text()')[0]
        data_list.append(
            {'PMID': pmid, 'journal': journal, 'affiliations_info': affiliations_info, 'pub_year': date,
             'mesh_words': mesh_words,
             'country': country, 'article_type': article_type, 'file_path': path})
        print(pmid + '\t解析完成')
        df = pd.DataFrame(data_list)
        with threading.Lock():
            df.to_csv('pubmed.csv', encoding='utf_8_sig', mode='a', index=False, header=False)


def to_excel(data, path):
    writer = pd.ExcelWriter(path)
    data.to_excel(writer, sheet_name='table', index=False)
    writer.save()


def get_files_path():
    for base_path, folders, files in os.walk('first in class drug'):
        file_list = [os.path.join(base_path, file) for file in files if file.endswith('.xml')]
    for base_path, folders, files in os.walk('follow on drug'):
        file_list.extend([os.path.join(base_path, file) for file in files if file.endswith('.xml')])
    for base_path, folders, files in os.walk('me too drug'):
        file_list.extend([os.path.join(base_path, file) for file in files if file.endswith('.xml')])
    if os.path.exists('pubmed.csv'):
        df = pd.read_csv('pubmed.csv', encoding='utf-8')
        has_files_list = df.file_path
        print('共需解析文件:{0}'.format(len(file_list)))
        file_list = set(file_list) - set(has_files_list)
        print('已解析文件:{0}'.format(len(set(has_files_list))))
    else:
        df = pd.DataFrame(columns=['PMID','affiliations_info','article_type','country','file_path','journal','mesh_words','pub_year'])
        df.to_csv('pubmed.csv', encoding='utf_8_sig', index=False)
        print('共需解析文件:{0}'.format(len(file_list)))
        print('已解析文件:0')
    return file_list


if __name__ == '__main__':
    files_list = get_files_path()
    if not files_list:
        print('全部解析完成')
    else:
        pool = ThreadPoolExecutor(max_workers=os.cpu_count())
        pool.map(pubmed_xpath_parse, files_list)

  

 

 

 

 

  

posted @ 2019-04-26 21:22  DreamBoy_张亚飞  阅读(742)  评论(0编辑  收藏  举报