我正在解析XML文件，子元素之一偶尔包含4000多个字符。这样做时，我想创建第二个元素来存储溢出字符，然后再将其保存到pandas数据框。构建数据框后，将其导出到Excel（我知道该怎么做）。
或者在解析并且它具有4000多个字符时，动态创建一个新的dataframe列来存储数据（我认为这是更好的解决方案，因为数据已导出到Excel以进行报告）
import pandas as pd
import xml.etree.cElementTree as et
from bs4 import BeautifulSoup
def getvalueofnode(node):
if node is None:
return None
else:
soup = BeautifulSoup(node.text) # clean js keywords
text = soup.get_text()
text = text.replace("\n", " ") # remove newline
text = text.replace("\r", " ") # remove newline
text = text.replace(' +', ' ') # remove duplicate spaces
return text
parsedXML = et.parse(filename)
dfcols = ['datarec','casekey','description','narative']
df_xml = pd.DataFrame(columns=dfcols)
for node in parsedXML.getroot():
datarec = node.find('DATA_RECORD')
casekey = node.find('CASE_KEY')
description = node.find('DESCRIPTION')
narative = node.find('CASE_NARRATIVE')
df_xml = df_xml.append(pd.Series([datarec, getvalueofnode(casekey), getvalueofnode(description), getvalueofnode(narative)], index=dfcols), ignore_index=True)
我应该走哪条路？
编辑------复制XML
<?xml version="1.0" ?>
<!DOCTYPE main [
<!ELEMENT main (DATA_RECORD*)>
<!ELEMENT DATA_RECORD (CASE_KEY,DESCRIPTION?,CASE_NARRATIVE?)+>
<!ELEMENT CASE_KEY (#PCDATA)>
<!ELEMENT DESCRIPTION (#PCDATA)>
<!ELEMENT CASE_NARRATIVE (#PCDATA)>
]>
<main>
<DATA_RECORD>
<CASE_KEY>6479351</CASE_KEY>
<DESCRIPTION>Four bill payments</DESCRIPTION>
<CASE_NARRATIVE>
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque accumsan congue risus, tristique imperdiet sapien consectetur nec. Donec ut urna lectus. Duis eget magna et quam aliquet porta non vitae enim. Proin diam ex, ullamcorper in lectus ac, cursus sollicitudin ipsum. Sed lorem urna, congue et condimentum in, rhoncus id nunc. Duis vel mauris pharetra, accumsan neque non, pellentesque leo. Nullam vel nibh vulputate, eleifend turpis condimentum, faucibus mi. Sed mattis dolor non libero scelerisque, in congue ligula ullamcorper. In finibus laoreet erat et venenatis. Aenean tincidunt magna a nisl euismod posuere tristique eget orci. Vestibulum ac turpis vel justo laoreet fermentum rutrum eget est. In hac habitasse platea dictumst. Aenean blandit at leo vel pharetra. Duis vel commodo orci.
Praesent tincidunt mattis suscipit. Nam aliquet purus eu nibh ultrices, ac tristique risus euismod. Sed bibendum tincidunt elit, a finibus arcu bibendum at. Praesent turpis neque, auctor at dui ut, cursus rhoncus tortor. Cras rutrum, lacus et molestie posuere, odio purus porta nisi, vel egestas nulla nibh accumsan erat. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Integer imperdiet, ligula ac iaculis iaculis, augue massa dapibus neque, sit amet iaculis orci nibh quis libero. Phasellus tortor ligula, luctus non mi quis, consequat dapibus risus. Vestibulum nec finibus ex. Duis ipsum nisl, tincidunt in erat rhoncus, pulvinar consequat tortor. Curabitur faucibus interdum metus. Morbi egestas ipsum ac rutrum faucibus. Maecenas non leo sem.
In ultrices, libero ut sagittis blandit, ex dolor pretium nibh, ac bibendum ligula nunc sed quam. In ultricies, arcu aliquam porta pharetra, orci mauris imperdiet lectus, a facilisis purus purus at sem. Nullam ac feugiat nulla. Duis congue lorem sit amet tellus varius ultrices. Curabitur risus mauris, rutrum ut sodales tempor, varius eget lectus. In eget hendrerit ligula, ac mollis mi. Nulla volutpat felis ornare elit facilisis dapibus. Fusce facilisis nisi est, eget gravida lorem aliquam nec. Ut sed purus sit amet mi sodales vestibulum id sit amet purus. Ut in vestibulum purus. Donec eget enim ipsum. Mauris eget neque neque. Pellentesque feugiat faucibus felis, quis tincidunt nisl.
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Aliquam egestas leo mauris, non placerat dolor euismod eu. Proin eget convallis augue. Suspendisse elit ante, ornare at augue sit amet, molestie elementum leo. Duis id leo in odio consequat auctor. Duis commodo elementum velit, porttitor blandit libero luctus commodo. Nulla in libero vel libero varius faucibus a non tellus. Pellentesque dapibus eget lectus id fringilla. Sed vitae nisi nisi. Sed ultricies orci vitae sapien ultrices, nec ornare tortor placerat. Vestibulum et ligula tristique, rhoncus dolor in, semper lorem. Integer non urna nec risus convallis pharetra. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam vitae ullamcorper leo. Suspendisse potenti.
Sed congue, mi rutrum placerat bibendum, erat tortor finibus lorem, eget varius velit lacus ut mauris. Nullam congue placerat mollis. Duis et fringilla nunc, id dictum enim. Morbi non gravida nisi. In nec nunc ante. In vitae odio accumsan, imperdiet lectus a, egestas sapien. In sit amet elit pharetra, scelerisque turpis a, tincidunt nisl. Curabitur tempus eu risus et vulputate. Fusce iaculis diam quis nibh viverra, pulvinar fringilla massa fermentum. Proin elementum in felis sed rutrum. Etiam eget elit vitae turpis ultrices auctor lobortis a erat. Duis fermentum tristique consectetur. Fusce quis est tincidunt, ultricies erat a, pharetra est.
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</CASE_NARRATIVE>
</DATA_RECORD>
<DATA_RECORD>
<CASE_KEY>6479356</CASE_KEY>
<DESCRIPTION>Financial Crime Concern</CASE_NARRATIVE>
</DATA_RECORD>
<DATA_RECORD>
<CASE_KEY>6480409</CASE_KEY>
<DESCRIPTION>Financial Crime Concern :M&S customer was cold called by someone about an investment opportunity, the caller gave customer different options and she chose 3 to invest in. She was unaware of the scam until she was contacted by the police. There is a seperate scion case re the police notification</DESCRIPTION>
<CASE_NARRATIVE><p&# Lorum Ipsum</CASE_NARRATIVE>
</DATA_RECORD>
<DATA_RECORD>
<CASE_KEY>6480519</CASE_KEY>
<DESCRIPTION>Financial Crime </DESCRIPTION>
<CASE_NARRATIVE>fraudster had set up two new payments and created </CASE_NARRATIVE>
</DATA_RECORD>
<DATA_RECORD>
<CASE_KEY>6480521</CASE_KEY>
<DESCRIPTION>Triage Europe</DESCRIPTION>
<CASE_NARRATIVE>Mr. Ockwell is a HB</CASE_NARRATIVE>
</DATA_RECORD>
</main>
而不是BeautifulSoup考虑使用lxmlXSLT和XPath来运行兄弟姐妹：
XSLT可以使用和函数为添加的OVERFLOW元素转换原始XML 。substring()string-length()
XPath可以解析新的，经过转换的树，以便通过循环或列表/字典理解将值映射到熊猫数据帧。
XSLT （另存为.xslt文件，一个特殊的.xml文件）
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
<xsl:output indent="yes" method="xml"/>
<xsl:strip-space elements="*"/>
<!-- IDENTITY TRANSFORM -->
<xsl:template match="@*|node()">
<xsl:copy>
<xsl:apply-templates select="@*|node()"/>
</xsl:copy>
</xsl:template>
<xsl:template match="DATA_RECORD">
<xsl:copy>
<xsl:apply-templates select="CASE_KEY|DESCRIPTION"/>
<CASE_NARRATIVE>
<xsl:value-of select="substring(normalize-space(CASE_NARRATIVE), 1, 4000)"/>
</CASE_NARRATIVE>
<OVERFLOW>
<xsl:value-of select="substring(normalize-space(CASE_NARRATIVE), 4001,
string-length(normalize-space(CASE_NARRATIVE)))"/>
</OVERFLOW>
</xsl:copy>
</xsl:template>
</xsl:stylesheet>
Python （包括简短列表理解版本和长循环版本）
import lxml.etree as et
import pandas as pd
# LOAD XML AND XSL FILES
xml = 'Input.xml'
xsl = 'XSLT_Script.xsl'
# TRANSFORM SOURCE
transform = et.XSLT(xsl)
result = transform(xml)
# SHORT VERSION
data = [{el.tag: el.text for el in dr.xpath("*")} for dr in result.xpath("//DATA_RECORD")]
# LONG VERSION
data = []
for dr in result.xpath("//DATA_RECORD"):
inner = {}
for el in dr.xpath("*"):
inner[el.tag] = el.text
data.append(inner)
df = pd.DataFrame(data)
输出量
print(df)
# CASE_KEY CASE_NARRATIVE DESCRIPTION OVERFLOW
# 0 6479351 Lorem ipsum dolor sit amet, consectetur adipis... Four bill payments s velit lacus ut mauris. Nullam congue placera...
# 1 6479356 None Financial Crime Concern None
# 2 6480409 <p Lorum Ipsum Financial Crime Concern :M&S customer was cold... None
# 3 6480519 fraudster had set up two new payments and created Financial Crime None
# 4 6480521 Mr. Ockwell is a HB Triage Europe None
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