meta分析中需要調研大量文獻, pubmed數據庫是主要來源, 但是pubmed 導出的文本文件,可讀性非常差,並且考慮到需要對文獻進行篩選和標記,xlsx是個很好的選擇. 下面代碼是把 xml 轉化爲 xlsx
#!/usr/bin/env python3
'''
解析 pubmed 導出的xml文件,並且轉換成xlsx格式
'''
import sys
import xml.etree.ElementTree as ET
import pandas as pd
import numpy as np
def main(file, out):
tree = ET.parse(file)
root = tree.getroot()
PMID = None
ArticleTitle = None
Journal_name = None
Abstract = None
First_author = None
First_author_Affiliation = None
DOI = None
Year = None
df = pd.DataFrame(columns=["PMID", "DOI", "Journal", "Year",
"First_author", "First_author_affiliation", "Title", "Abstract"])
for pubmedArticle in root:
try:
PMID = pubmedArticle.find("MedlineCitation/PMID").text
ArticleTitle = pubmedArticle.find(
"MedlineCitation/Article/ArticleTitle").text
Journal_name = pubmedArticle.find(
"MedlineCitation/Article/Journal/Title").text
Abstract = pubmedArticle.find(
"MedlineCitation/Article/Abstract/AbstractText").text
First_author = pubmedArticle.find(
"MedlineCitation/Article/AuthorList/Author")[1].text
First_author_Affiliation = pubmedArticle.find(
"MedlineCitation/Article/AuthorList/Author[1]/AffiliationInfo/Affiliation").text
DOI = pubmedArticle.find(
"MedlineCitation/Article/ELocationID").text
Year = pubmedArticle.find("MedlineCitation/DateRevised/Year").text
except:
pass
line = {"PMID": PMID, "DOI": DOI, "Journal": Journal_name, "Year": Year, "First_author": First_author,
"First_author_affiliation": First_author_Affiliation, "Title": ArticleTitle, "Abstract": Abstract}
#line = [[str(x) for x in [PMID,ArticleTitle,Journal_name,Abstract,First_author,First_author_Affiliation,DOI,Year]]]
df = df.append(line, ignore_index=True)
df.to_excel(out, index=False)
if __name__ == "__main__":
if len(sys.argv[1:]) < 2:
print("py pubmed_result.xml out.xlsx")
sys.exit(1)
main(sys.argv[1], sys.argv[2])