使用 lxml 和 xpath 加速 xml 解析过程

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
   <document DateTime="2017-06-23T04:27:08.592Z">
       <PeakInfo No="1" mz="505.2315648572003965"
                 Intensity="4531.0000000000000000"
                 Rel_Intensity="3.2737729673489735"
                 Resolution="1879.5638812957554364"
                 SNR="14.0278637770897561"
                 Area="1348.1007591467391649"
                 Rel_Area="2.3371194184605959"
                 Index="238.9999999999976694"/>
       <PeakInfo No="2" mz="522.1330917856538463"
                 Intensity="3382.0000000000000000"
                 Rel_Intensity="2.4435886505350317"
                 Resolution="3502.9921209527169594"
                 SNR="10.4705882352940982"
                 Area="881.4468100654634100"
                 Rel_Area="1.5281101521284057"
                 Index="925.0000000000000000"/>
   </document>

以上是我最近使用的一个 xml 文件的一部分。每个文件包含 400 多个 PeakInfo，我确实制作了一个 python 脚本来解析每个文件：

from lxml import etree
import pandas as pd
import tkinter.filedialog
import os
import pandas.io.formats.excel

full_path = tkinter.filedialog.askdirectory(initialdir='.')
newfolder = full_path+'\\xls files'
os.chdir(full_path)
os.makedirs(newfolder)

data = {}
for files in os.listdir(full_path):
        if os.path.isfile(os.path.join(full_path, files)):
            plist = pd.DataFrame()
            filename = os.path.basename(files).rpartition('.')[0]

            if len(filename) == 2:
                filename = filename[:1]+'0'+filename[1:]

            xmlp = etree.parse(files)
            for p in xmlp.xpath('//PeakInfo'):
                data['Exp. m/z'] = p.attrib['mz']
                data['Intensity'] = p.attrib['Intensity']
                plist = plist.append(data, ignore_index=True)
                plist['Exp. m/z'] = plist['Exp. m/z'].astype(float)
                plist['Exp. m/z'] = plist['Exp. m/z'].map('{:.4f}'.format)
                plist['Intensity'] = plist['Intensity'].astype(float)
                plist['Intensity'] = plist['Intensity'].map('{:.0f}'.format)
                pandas.io.formats.excel.header_style = None
                plist.to_excel(os.path.join(newfolder, filename+'.xls'),index=False)

如果只有两个字符（即 A1 到 A01），此代码更改文件名，然后拉出 mz 和 Intensity 并保存为 xls 文件。问题是解析每个文件花费的时间太长。是否有任何提示可以显着加快进程？