Pandas read_xml() method test strategies

Question

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>
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    <tag3>docker</tag3>
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    <tag1>python</tag1>
    <tag2>selenium</tag2>
    <tag3>protractor</tag3>
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    <tag3>dataframe</tag3>
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    <user>CommonsWare</user>
    <link>http://www.stackoverflow.com//users/115145/commonsware</link>
    <location>Who Wants to Know?</location>
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    <tag1>android</tag1>
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    <tag3>android-intent</tag3>
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    <user>Quentin</user>
    <link>http://www.stackoverflow.com//users/19068/quentin</link>
    <location>United Kingdom</location>
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    <tag1>javascript</tag1>
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    <tag3>css</tag3>
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    <tag3>.net</tag3>
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    <tag3>asynchronous</tag3>
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    <location></location>
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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>
    <year_rep>4,195</year_rep>
    <total_rep>269,380</total_rep>
    <tag1>swift</tag1>
    <tag2>ios</tag2>
    <tag3>swift3</tag3>
  </topusers>
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    <user>Barmar</user>
    <link>http://www.stackoverflow.com//users/1491895/barmar</link>
    <location>Arlington, MA</location>
    <year_rep>4,179</year_rep>
    <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>
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    <user>GurV</user>
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    <location></location>
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    <tag1>sql</tag1>
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    <location>Saint-Etienne, France</location>
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    <tag1>java</tag1>
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    <tag1>firebase</tag1>
    <tag2>firebase-database</tag2>
    <tag3>android</tag3>
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    <user>dasblinkenlight</user>
    <link>http://www.stackoverflow.com//users/335858/dasblinkenlight</link>
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    <tag1>c#</tag1>
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    <user>Tim Biegeleisen</user>
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    <location>Singapore</location>
    <year_rep>3,814</year_rep>
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    <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>
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    <tag1>git</tag1>
    <tag2>python</tag2>
    <tag3>git-pull</tag3>
  </topusers>
  <topusers>
    <user>unutbu</user>
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    <location></location>
    <year_rep>3,735</year_rep>
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    <tag1>python</tag1>
    <tag2>pandas</tag2>
    <tag3>numpy</tag3>
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  <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>
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    <user>Jonathan Leffler</user>
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    <location>California, USA</location>
    <year_rep>3,649</year_rep>
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    <tag1>c</tag1>
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    <location></location>
    <year_rep>3,636</year_rep>
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    <tag1>c</tag1>
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    <tag3>bash</tag3>
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    <user>Pranav C Balan</user>
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    <tag1>javascript</tag1>
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</stackoverflow>

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

Lickspittle

As far as I know this is also discussed on pandas github. Maybe open an issue there? – Inalienable

Answer 1

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.

Eudo

Thank you for your answer. However, your reply appears to be general for Python and not the specific XML methods proposed for Pandas. Maybe a specific coding example can illustrate better like the JIT idea or Cython using above reproducible example? – Lickspittle

Maybe I didn't understand your questions? If something applies to all Python code, then it applies to your Python code. If you are looking for code examples, that was not clear from your question. – Eudo