标签归档：xslt

知识问答

熊猫read_xml（）方法测试策略

问题：熊猫read_xml（）方法测试策略

当前，pandas I / O工具没有维护read_xml()方法，而相应的工具to_xml()。但是，read_json证明可以为数据帧导入和read_html标记格式实现树状结构。

如果大熊猫团队会考虑这样一个read_xml为未来大熊猫版本的方法，他们会追求什么实现：使用内置的解析xml.etree.ElementTree其iterfind()或iterparse()功能或第三方模块，lxml其XPath 1.0和XSLT 1.0的方法呢？

以下是我在简单，扁平，以元素为中心的XML输入上针对四种方法类型的测试运行。所有这些都针对root的任何第二级子级进行了通用解析，并且每种方法都应产生完全相同的pandas数据帧。除最后一次调用外pd.Dataframe()，所有其他功能都在词典列表中。XSLT方法将XML转换为CSV，以便StringIO()在中进行转换pd.read_csv()。

问题 （多部分）

  • 性能：您如何解释由于iterparse迭代解析文件而通常建议对较大文件使用的速度较慢的速度？部分原因是由于if逻辑检查吗？

  • 内存：CPU内存是否与I / O调用中的时间相关？XSLT和XPath 1.0在较大的XML文档中往往无法很好地扩展，因为必须在内存中读取整个文件才能进行解析。

  • 策略：词典列表是Dataframe()呼叫的最佳策略吗？请参阅以下有趣的答案：生成器版本和iterwalk用户定义版本。两个上载列表到数据帧。

输入数据（Stack Overflow当前的年度最大用户，其中包括我们的熊猫朋友）

<?xml version="1.0" encoding="utf-8"?>
<stackoverflow>
  <topusers>
    <user>Gordon Linoff</user>
    <link>http://www.stackoverflow.com//users/1144035/gordon-linoff</link>
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    <tag2>sql-server</tag2>
    <tag3>mysql</tag3>
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    <link>http://www.stackoverflow.com//users/217408/g%c3%bcnter-z%c3%b6chbauer</link>
    <location>Linz, Austria</location>
    <year_rep>5,835</year_rep>
    <total_rep>154,439</total_rep>
    <tag1>angular2</tag1>
    <tag2>typescript</tag2>
    <tag3>javascript</tag3>
  </topusers>
  <topusers>
    <user>jezrael</user>
    <link>http://www.stackoverflow.com//users/2901002/jezrael</link>
    <location>Bratislava, Slovakia</location>
    <year_rep>5,740</year_rep>
    <total_rep>83,237</total_rep>
    <tag1>pandas</tag1>
    <tag2>python</tag2>
    <tag3>dataframe</tag3>
  </topusers>
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    <user>VonC</user>
    <link>http://www.stackoverflow.com//users/6309/vonc</link>
    <location>France</location>
    <year_rep>5,577</year_rep>
    <total_rep>651,397</total_rep>
    <tag1>git</tag1>
    <tag2>github</tag2>
    <tag3>docker</tag3>
  </topusers>
  <topusers>
    <user>Martijn Pieters</user>
    <link>http://www.stackoverflow.com//users/100297/martijn-pieters</link>
    <location>Cambridge, United Kingdom</location>
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    <total_rep>525,176</total_rep>
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    <tag2>jquery</tag2>
    <tag3>java</tag3>
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    <tag3>dataframe</tag3>
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    <user>CommonsWare</user>
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    <tag3>android-intent</tag3>
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    <location>United Kingdom</location>
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    <tag1>javascript</tag1>
    <tag2>html</tag2>
    <tag3>css</tag3>
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    <user>Jon Skeet</user>
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    <location>Reading, United Kingdom</location>
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    <tag2>java</tag2>
    <tag3>.net</tag3>
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    <location>Sunnyvale, CA</location>
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    <tag2>jquery</tag2>
    <tag3>asynchronous</tag3>
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    <user>matt</user>
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    <location></location>
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    <total_rep>220,515</total_rep>
    <tag1>swift</tag1>
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    <tag3>xcode</tag3>
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    <tag1>python</tag1>
    <tag2>pandas</tag2>
    <tag3>r</tag3>
  </topusers>
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    <user>Martin R</user>
    <link>http://www.stackoverflow.com//users/1187415/martin-r</link>
    <location>Germany</location>
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    <total_rep>269,380</total_rep>
    <tag1>swift</tag1>
    <tag2>ios</tag2>
    <tag3>swift3</tag3>
  </topusers>
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    <link>http://www.stackoverflow.com//users/1491895/barmar</link>
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    <total_rep>289,989</total_rep>
    <tag1>javascript</tag1>
    <tag2>php</tag2>
    <tag3>jquery</tag3>
  </topusers>
  <topusers>
    <user>Alexey Mezenin</user>
    <link>http://www.stackoverflow.com//users/1227923/alexey-mezenin</link>
    <location>??????</location>
    <year_rep>4,142</year_rep>
    <total_rep>31,602</total_rep>
    <tag1>laravel</tag1>
    <tag2>php</tag2>
    <tag3>laravel-5.3</tag3>
  </topusers>
  <topusers>
    <user>BalusC</user>
    <link>http://www.stackoverflow.com//users/157882/balusc</link>
    <location>Amsterdam, Netherlands</location>
    <year_rep>4,046</year_rep>
    <total_rep>703,046</total_rep>
    <tag1>java</tag1>
    <tag2>jsf</tag2>
    <tag3>servlets</tag3>
  </topusers>
  <topusers>
    <user>GurV</user>
    <link>http://www.stackoverflow.com//users/6348498/gurv</link>
    <location></location>
    <year_rep>4,016</year_rep>
    <total_rep>7,932</total_rep>
    <tag1>sql</tag1>
    <tag2>mysql</tag2>
    <tag3>sql-server</tag3>
  </topusers>
  <topusers>
    <user>Nina Scholz</user>
    <link>http://www.stackoverflow.com//users/1447675/nina-scholz</link>
    <location>Berlin, Deutschland</location>
    <year_rep>3,950</year_rep>
    <total_rep>61,135</total_rep>
    <tag1>javascript</tag1>
    <tag2>arrays</tag2>
    <tag3>object</tag3>
  </topusers>
  <topusers>
    <user>JB Nizet</user>
    <link>http://www.stackoverflow.com//users/571407/jb-nizet</link>
    <location>Saint-Etienne, France</location>
    <year_rep>3,923</year_rep>
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    <tag1>java</tag1>
    <tag2>hibernate</tag2>
    <tag3>java-8</tag3>
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    <user>Frank van Puffelen</user>
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    <location>San Francisco, CA</location>
    <year_rep>3,920</year_rep>
    <total_rep>86,520</total_rep>
    <tag1>firebase</tag1>
    <tag2>firebase-database</tag2>
    <tag3>android</tag3>
  </topusers>
  <topusers>
    <user>dasblinkenlight</user>
    <link>http://www.stackoverflow.com//users/335858/dasblinkenlight</link>
    <location>United States</location>
    <year_rep>3,886</year_rep>
    <total_rep>475,813</total_rep>
    <tag1>c#</tag1>
    <tag2>java</tag2>
    <tag3>c++</tag3>
  </topusers>
  <topusers>
    <user>Tim Biegeleisen</user>
    <link>http://www.stackoverflow.com//users/1863229/tim-biegeleisen</link>
    <location>Singapore</location>
    <year_rep>3,814</year_rep>
    <total_rep>77,211</total_rep>
    <tag1>sql</tag1>
    <tag2>mysql</tag2>
    <tag3>java</tag3>
  </topusers>
  <topusers>
    <user>Greg Hewgill</user>
    <link>http://www.stackoverflow.com//users/893/greg-hewgill</link>
    <location>Christchurch, New Zealand</location>
    <year_rep>3,796</year_rep>
    <total_rep>529,137</total_rep>
    <tag1>git</tag1>
    <tag2>python</tag2>
    <tag3>git-pull</tag3>
  </topusers>
  <topusers>
    <user>unutbu</user>
    <link>http://www.stackoverflow.com//users/190597/unutbu</link>
    <location></location>
    <year_rep>3,735</year_rep>
    <total_rep>401,595</total_rep>
    <tag1>python</tag1>
    <tag2>pandas</tag2>
    <tag3>numpy</tag3>
  </topusers>
  <topusers>
    <user>Hans Passant</user>
    <link>http://www.stackoverflow.com//users/17034/hans-passant</link>
    <location>Madison, WI</location>
    <year_rep>3,688</year_rep>
    <total_rep>672,118</total_rep>
    <tag1>c#</tag1>
    <tag2>.net</tag2>
    <tag3>winforms</tag3>
  </topusers>
  <topusers>
    <user>Jonathan Leffler</user>
    <link>http://www.stackoverflow.com//users/15168/jonathan-leffler</link>
    <location>California, USA</location>
    <year_rep>3,649</year_rep>
    <total_rep>455,157</total_rep>
    <tag1>c</tag1>
    <tag2>bash</tag2>
    <tag3>unix</tag3>
  </topusers>
  <topusers>
    <user>paxdiablo</user>
    <link>http://www.stackoverflow.com//users/14860/paxdiablo</link>
    <location></location>
    <year_rep>3,636</year_rep>
    <total_rep>507,043</total_rep>
    <tag1>c</tag1>
    <tag2>c++</tag2>
    <tag3>bash</tag3>
  </topusers>
  <topusers>
    <user>Pranav C Balan</user>
    <link>http://www.stackoverflow.com//users/3037257/pranav-c-balan</link>
    <location>Ramanthali, Kannur, Kerala, India</location>
    <year_rep>3,604</year_rep>
    <total_rep>64,476</total_rep>
    <tag1>javascript</tag1>
    <tag2>jquery</tag2>
    <tag3>html</tag3>
  </topusers>
  <topusers>
    <user>Suragch</user>
    <link>http://www.stackoverflow.com//users/3681880/suragch</link>
    <location>Hohhot, China</location>
    <year_rep>3,580</year_rep>
    <total_rep>71,032</total_rep>
    <tag1>swift</tag1>
    <tag2>ios</tag2>
    <tag3>android</tag3>
  </topusers>
</stackoverflow>

Python方法

import xml.etree.ElementTree as et
import pandas as pd
from io import StringIO
from lxml import etree as lxet

def read_xml_iterfind():
    tree = et.parse('Input.xml')

    data = []
    inner = {}
    for el in tree.iterfind('./*'):
        for i in el.iterfind('*'):
            inner[i.tag] = i.text
        data.append(inner)
        inner = {}

    df = pd.DataFrame(data)

def read_xml_iterparse():
    data = []
    inner = {}
    i = 1
    for (ev, el) in et.iterparse(path):
        if i <= 2:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        if el.text is not None and len(el.text.strip()) > 0:
            inner[el.tag] = el.text
    i += 1

    df = pd.DataFrame(data)    

def read_xml_lxml_xpath():     
    tree = lxet.parse('Input.xml')

    data = []
    inner = {}
    for el in tree.xpath('/*/*'):
        for i in el:
            inner[i.tag] = i.text
        data.append(inner)
        inner = {}

    df = pd.DataFrame(data)

def read_xml_lxml_xsl():     
    xml = lxet.parse('Input.xml')

    xslstr = '''
    <xsl:transform xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
        <xsl:output version="1.0" encoding="UTF-8" indent="yes"  method="text"/>
        <xsl:strip-space elements="*"/>

        <!-- HEADERS -->
        <xsl:template match = "/*">
            <xsl:for-each select="*[1]/*">
              <xsl:value-of select="local-name()" />
                <xsl:choose>
                   <xsl:when test="position() != last()">
                      <xsl:text>,</xsl:text>
                   </xsl:when>
                   <xsl:otherwise>
                      <xsl:text>&#xa;</xsl:text>
                   </xsl:otherwise>                              
                </xsl:choose>   
            </xsl:for-each>
            <xsl:apply-templates/>
        </xsl:template>

        <!-- DATA ROWS (COMMA-SEPARATED) -->
        <xsl:template match="/*/*" priority="2">    
            <xsl:for-each select="*">
              <xsl:if test="position() = 1">
                   <xsl:text>&quot;</xsl:text>
              </xsl:if>
              <xsl:value-of select="." />
                <xsl:choose>
                   <xsl:when test="position() != last()">
                      <xsl:text>&quot;,&quot;</xsl:text>
                   </xsl:when>
                   <xsl:otherwise>
                      <xsl:text>&quot;&#xa;</xsl:text>
                   </xsl:otherwise>                              
                </xsl:choose>
            </xsl:for-each>
        </xsl:template>

    </xsl:transform>
    '''
    xsl = lxet.fromstring(xslstr)

    transform = lxet.XSLT(xsl)
    newdom = transform(xml)

    df = pd.read_csv(StringIO(str(newdom)))

时序 （当前的XML和XML的子级是25倍（即900条StackOverflow用户记录）

# SHORTER FILE
python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_iterfind()'
100 loops, best of 3: 3.87 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_iterparse()'
100 loops, best of 3: 5.5 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_lxml_xpath()'
100 loops, best of 3: 3.86 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_lxml_xsl()'
100 loops, best of 3: 5.68 msec per loop

# LARGER FILE
python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_iterfind()'
100 loops, best of 3: 36 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_iterparse()'
100 loops, best of 3: 78.9 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_lxml_xpath()'
100 loops, best of 3: 32.7 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_lxml_xsl()'
100 loops, best of 3: 51.4 msec per loop
点击查看英文原文

Currently, pandas I/O tools does not maintain a read_xml() method and the counterpart to_xml(). However, read_json proves tree-like structures can be implemented for dataframe import and read_html for markup formats.

If the pandas team does consider such a read_xml method for a future pandas version, what implementation would they pursue: parsing with built-in xml.etree.ElementTree with its iterfind() or iterparse() functions or the third-party module, lxml with its XPath 1.0 and XSLT 1.0 methods?

Below are my test runs for four method types on a simple, flat, element-centric XML input. All are set up for generalized parsing for any second level children of root and each method should yield exact same pandas dataframe. All but the last calls pd.Dataframe() on list of dictionaries. The XSLT method transforms XML to CSV for casted StringIO() in pd.read_csv().

Question (multi-part)

  • PERFORMANCE: How do you explain the slower iterparse often recommended for larger files as file is iteratively parsed? Is it partly due to the if logic checks?

  • MEMORY: Do CPU memory correlate with timings in I/O calls? XSLT and XPath 1.0 tend not to scale well with larger XML documents as entire file must be read in memory to be parsed.

  • STRATEGY: Is list of dictionaries an optimal strategy for Dataframe() call? See these interesting answers: generator version and a iterwalk user-defined version. Both upcast lists to dataframe.

Input Data (Stack Overflow’s current top users by year of which our pandas friends are included)

<?xml version="1.0" encoding="utf-8"?>
<stackoverflow>
  <topusers>
    <user>Gordon Linoff</user>
    <link>http://www.stackoverflow.com//users/1144035/gordon-linoff</link>
    <location>New York, United States</location>
    <year_rep>5,985</year_rep>
    <total_rep>499,408</total_rep>
    <tag1>sql</tag1>
    <tag2>sql-server</tag2>
    <tag3>mysql</tag3>
  </topusers>
  <topusers>
    <user>Günter Zöchbauer</user>
    <link>http://www.stackoverflow.com//users/217408/g%c3%bcnter-z%c3%b6chbauer</link>
    <location>Linz, Austria</location>
    <year_rep>5,835</year_rep>
    <total_rep>154,439</total_rep>
    <tag1>angular2</tag1>
    <tag2>typescript</tag2>
    <tag3>javascript</tag3>
  </topusers>
  <topusers>
    <user>jezrael</user>
    <link>http://www.stackoverflow.com//users/2901002/jezrael</link>
    <location>Bratislava, Slovakia</location>
    <year_rep>5,740</year_rep>
    <total_rep>83,237</total_rep>
    <tag1>pandas</tag1>
    <tag2>python</tag2>
    <tag3>dataframe</tag3>
  </topusers>
  <topusers>
    <user>VonC</user>
    <link>http://www.stackoverflow.com//users/6309/vonc</link>
    <location>France</location>
    <year_rep>5,577</year_rep>
    <total_rep>651,397</total_rep>
    <tag1>git</tag1>
    <tag2>github</tag2>
    <tag3>docker</tag3>
  </topusers>
  <topusers>
    <user>Martijn Pieters</user>
    <link>http://www.stackoverflow.com//users/100297/martijn-pieters</link>
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    <tag3>.net</tag3>
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  <topusers>
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    <tag2>jquery</tag2>
    <tag3>asynchronous</tag3>
  </topusers>
  <topusers>
    <user>matt</user>
    <link>http://www.stackoverflow.com//users/341994/matt</link>
    <location></location>
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    <tag3>xcode</tag3>
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    <tag1>python</tag1>
    <tag2>pandas</tag2>
    <tag3>r</tag3>
  </topusers>
  <topusers>
    <user>Martin R</user>
    <link>http://www.stackoverflow.com//users/1187415/martin-r</link>
    <location>Germany</location>
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    <total_rep>269,380</total_rep>
    <tag1>swift</tag1>
    <tag2>ios</tag2>
    <tag3>swift3</tag3>
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    <location>Arlington, MA</location>
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    <tag1>javascript</tag1>
    <tag2>php</tag2>
    <tag3>jquery</tag3>
  </topusers>
  <topusers>
    <user>Alexey Mezenin</user>
    <link>http://www.stackoverflow.com//users/1227923/alexey-mezenin</link>
    <location>??????</location>
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    <total_rep>31,602</total_rep>
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    <tag2>php</tag2>
    <tag3>laravel-5.3</tag3>
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  <topusers>
    <user>BalusC</user>
    <link>http://www.stackoverflow.com//users/157882/balusc</link>
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Python Methods

import xml.etree.ElementTree as et
import pandas as pd
from io import StringIO
from lxml import etree as lxet

def read_xml_iterfind():
    tree = et.parse('Input.xml')

    data = []
    inner = {}
    for el in tree.iterfind('./*'):
        for i in el.iterfind('*'):
            inner[i.tag] = i.text
        data.append(inner)
        inner = {}

    df = pd.DataFrame(data)

def read_xml_iterparse():
    data = []
    inner = {}
    i = 1
    for (ev, el) in et.iterparse(path):
        if i <= 2:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        if el.text is not None and len(el.text.strip()) > 0:
            inner[el.tag] = el.text
    i += 1

    df = pd.DataFrame(data)    

def read_xml_lxml_xpath():     
    tree = lxet.parse('Input.xml')

    data = []
    inner = {}
    for el in tree.xpath('/*/*'):
        for i in el:
            inner[i.tag] = i.text
        data.append(inner)
        inner = {}

    df = pd.DataFrame(data)

def read_xml_lxml_xsl():     
    xml = lxet.parse('Input.xml')

    xslstr = '''
    <xsl:transform xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0">
        <xsl:output version="1.0" encoding="UTF-8" indent="yes"  method="text"/>
        <xsl:strip-space elements="*"/>

        <!-- HEADERS -->
        <xsl:template match = "/*">
            <xsl:for-each select="*[1]/*">
              <xsl:value-of select="local-name()" />
                <xsl:choose>
                   <xsl:when test="position() != last()">
                      <xsl:text>,</xsl:text>
                   </xsl:when>
                   <xsl:otherwise>
                      <xsl:text>&#xa;</xsl:text>
                   </xsl:otherwise>                              
                </xsl:choose>   
            </xsl:for-each>
            <xsl:apply-templates/>
        </xsl:template>

        <!-- DATA ROWS (COMMA-SEPARATED) -->
        <xsl:template match="/*/*" priority="2">    
            <xsl:for-each select="*">
              <xsl:if test="position() = 1">
                   <xsl:text>&quot;</xsl:text>
              </xsl:if>
              <xsl:value-of select="." />
                <xsl:choose>
                   <xsl:when test="position() != last()">
                      <xsl:text>&quot;,&quot;</xsl:text>
                   </xsl:when>
                   <xsl:otherwise>
                      <xsl:text>&quot;&#xa;</xsl:text>
                   </xsl:otherwise>                              
                </xsl:choose>
            </xsl:for-each>
        </xsl:template>

    </xsl:transform>
    '''
    xsl = lxet.fromstring(xslstr)

    transform = lxet.XSLT(xsl)
    newdom = transform(xml)

    df = pd.read_csv(StringIO(str(newdom)))

Timings (with current XML and XML with 25 times the children (i.e., 900 StackOverflow user records)

# SHORTER FILE
python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_iterfind()'
100 loops, best of 3: 3.87 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_iterparse()'
100 loops, best of 3: 5.5 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_lxml_xpath()'
100 loops, best of 3: 3.86 msec per loop

python -mtimeit -s'import readxml_test_runs as test' 'test.read_xml_lxml_xsl()'
100 loops, best of 3: 5.68 msec per loop

# LARGER FILE
python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_iterfind()'
100 loops, best of 3: 36 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_iterparse()'
100 loops, best of 3: 78.9 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_lxml_xpath()'
100 loops, best of 3: 32.7 msec per loop

python -mtimeit -n'100' -s'import readxml_test_runs as test' 'test.read_xml_lxml_xsl()'
100 loops, best of 3: 51.4 msec per loop

回答 0

性能：如何解释由于迭代解析文件而通常建议对较大文件使用的较慢iterparse？部分原因是由于if逻辑检查？

我认为更多的python代码会使它变慢，因为每次都会评估python代码。您是否尝试过像pypy这样的JIT编译器？

如果仅删除i并使用first_tag，它似乎会快很多，所以是的，部分原因在于if逻辑检查：

def read_xml_iterparse2(path):
    data = []
    inner = {}
    first_tag = None
    for (ev, el) in et.iterparse(path):
        if not first_tag:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        if el.text is not None and len(el.text.strip()) > 0:
            inner[el.tag] = el.text

    df = pd.DataFrame(data)    

%timeit read_xml_iterparse(path)
# 10 loops, best of 5: 33 ms per loop
%timeit read_xml_iterparse2(path)
# 10 loops, best of 5: 23 ms per loop

我不确定我是否了解上次if检查的目的，但也不确定为什么您会丢失仅空白元素。持续删除最后一个可以if节省一点时间：

def read_xml_iterparse3(path):
    data = []
    inner = {}
    first_tag = None
    for (ev, el) in et.iterparse(path):
        if not first_tag:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        inner[el.tag] = el.text

    df = pd.DataFrame(data)    

%timeit read_xml_iterparse(path)
# 10 loops, best of 5: 34.4 ms per loop
%timeit read_xml_iterparse2(path)
# 10 loops, best of 5: 24.5 ms per loop
%timeit read_xml_iterparse3(path)
# 10 loops, best of 5: 20.9 ms per loop

现在，无论是否进行了这些性能改进，您的iterparse版本似乎都会产生一个更大的数据框。这似乎是一个有效的快速版本：

def read_xml_iterparse5(path):
    data = []
    inner = {}
    for (ev, el) in et.iterparse(path):
        # /ending parents trigger a new row, and in our case .text is \n followed by spaces.  it would be more reliable to pass 'topusers' to our read_xml_iterparse5 as the .tag to check
        if el.text and el.text[0] == '\n':
            # ignore /stackoverflow
            if inner:
                data.append(inner)
                inner = {}
        else:
            inner[el.tag] = el.text

    return pd.DataFrame(data)    

print(read_xml_iterfind(path).shape)
# (900, 8)
print(read_xml_iterparse(path).shape)
# (7050, 8)
print(read_xml_lxml_xpath(path).shape)
# (900, 8)
print(read_xml_lxml_xsl(path).shape)
# (900, 8)
print(read_xml_iterparse5(path).shape)
# (900, 8)
%timeit read_xml_iterparse5(path)
# 10 loops, best of 5: 20.6 ms per loop

内存：CPU内存是否与I / O调用中的时间相关？XSLT和XPath 1.0在较大的XML文档中往往无法很好地扩展，因为必须在内存中读取整个文件才能进行解析。

我不能完全确定“ I / O调用”是什么意思，但是如果您的文档足够小以适合缓存，那么一切都会更快，因为它不会从缓存中逐出其他项目。

策略：词典列表是否是Dataframe（）调用的最佳策略？请参阅以下有趣的答案：生成器版本和iterwalk用户定义的版本。两个上载列表到数据帧。

列表使用的内存较少，因此根据您拥有的列数，它可能会产生明显的不同。当然，这然后要求您的XML标记具有一致的顺序，看起来确实如此。该DataFrame()调用也将需要做的工作更少，因为它不必在每一行的dict中查找键，以弄清楚哪一列是什么值。

点击查看英文原文

PERFORMANCE: How do you explain the slower iterparse often recommended for larger files as file is iteratively parsed? Is it partly due to the if logic checks?

I would assume that more python code would make it slower, as the python code is evaluated every time. Have you tried a JIT compiler like pypy?

If I remove i and use first_tag only, it seems to be quite a bit faster, so yes it is partly due to the if logic checks:

def read_xml_iterparse2(path):
    data = []
    inner = {}
    first_tag = None
    for (ev, el) in et.iterparse(path):
        if not first_tag:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        if el.text is not None and len(el.text.strip()) > 0:
            inner[el.tag] = el.text

    df = pd.DataFrame(data)    

%timeit read_xml_iterparse(path)
# 10 loops, best of 5: 33 ms per loop
%timeit read_xml_iterparse2(path)
# 10 loops, best of 5: 23 ms per loop

I wasn’t sure I understood the purpose of the last if check, but I’m also not sure why you would want to lose whitespace-only elements. Removing the last if consistently shaves off a little bit of time:

def read_xml_iterparse3(path):
    data = []
    inner = {}
    first_tag = None
    for (ev, el) in et.iterparse(path):
        if not first_tag:
           first_tag = el.tag

        if el.tag == first_tag and len(inner) != 0:
            data.append(inner)            
            inner = {}

        inner[el.tag] = el.text

    df = pd.DataFrame(data)    

%timeit read_xml_iterparse(path)
# 10 loops, best of 5: 34.4 ms per loop
%timeit read_xml_iterparse2(path)
# 10 loops, best of 5: 24.5 ms per loop
%timeit read_xml_iterparse3(path)
# 10 loops, best of 5: 20.9 ms per loop

Now, with or without those performance improvements, your iterparse version seems to produce an extra-large dataframe. Here seems to be a working, fast version:

def read_xml_iterparse5(path):
    data = []
    inner = {}
    for (ev, el) in et.iterparse(path):
        # /ending parents trigger a new row, and in our case .text is \n followed by spaces.  it would be more reliable to pass 'topusers' to our read_xml_iterparse5 as the .tag to check
        if el.text and el.text[0] == '\n':
            # ignore /stackoverflow
            if inner:
                data.append(inner)
                inner = {}
        else:
            inner[el.tag] = el.text

    return pd.DataFrame(data)    

print(read_xml_iterfind(path).shape)
# (900, 8)
print(read_xml_iterparse(path).shape)
# (7050, 8)
print(read_xml_lxml_xpath(path).shape)
# (900, 8)
print(read_xml_lxml_xsl(path).shape)
# (900, 8)
print(read_xml_iterparse5(path).shape)
# (900, 8)
%timeit read_xml_iterparse5(path)
# 10 loops, best of 5: 20.6 ms per loop

MEMORY: Do CPU memory correlate with timings in I/O calls? XSLT and XPath 1.0 tend not to scale well with larger XML documents as entire file must be read in memory to be parsed.

I’m not totally sure what you mean by “I/O calls” but if your document is small enough to fit in cache, then everything will be much faster as it won’t evict many other items from the cache.

STRATEGY: Is list of dictionaries an optimal strategy for Dataframe() call? See these interesting answers: generator version and a iterwalk user-defined version. Both upcast lists to dataframe.

The lists use less memory, so depending on how many columns you have, it could make a noticeable difference. Of course, this then requires your XML tags to be in a consistent order, which they do appear to be. The DataFrame() call would also need to do less work, as it doesn’t have to lookup keys in the dict on every row, to figure out what column if for what value.