How to convert an XML file to nice pandas dataframe?

Let’s assume that I have an XML like this:

<author type="XXX" language="EN" gender="xx" feature="xx" web="foobar.com">
    <documents count="N">
        <document KEY="e95a9a6c790ecb95e46cf15bee517651" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="bc360cfbafc39970587547215162f0db" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="19e71144c50a8b9160b3f0955e906fce" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
        <document KEY="21d4af9021a174f61b884606c74d9e42" web="www.foo_bar_exmaple.com"><![CDATA[A large text with lots of strings and punctuations symbols [...]
]]>
        </document>
    </documents>
</author>

I would like to read this XML file and convert it to a pandas DataFrame:

key                                         type     language    feature            web                         data
e95324a9a6c790ecb95e46cf15bE232ee517651      XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
bc360cfbafc39970587547215162f0db             XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
19e71144c50a8b9160b3cvdf2324f0955e906fce     XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]
21d4af9021a174f61b8erf284606c74d9e42         XXX        EN          xx      www.foo_bar_exmaple.com     A large text with lots of strings and punctuations symbols [...]

This is what I already tried, but I am getting some errors and probably there is a more efficient way of doing this task:

from lxml import objectify
import pandas as pd

path = 'file_path'
xml = objectify.parse(open(path))
root = xml.getroot()
root.getchildren()[0].getchildren()
df = pd.DataFrame(columns=('key','type', 'language', 'feature', 'web', 'data'))

for i in range(0,len(xml)):
    obj = root.getchildren()[i].getchildren()
    row = dict(zip(['key','type', 'language', 'feature', 'web', 'data'], [obj[0].text, obj[1].text]))
    row_s = pd.Series(row)
    row_s.name = i
    df = df.append(row_s)

Could anybody provide me a better aproach for this problem?

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

You can easily use xml (from the Python standard library) to convert to a pandas.DataFrame. Here’s what I would do (when reading from a file replace xml_data with the name of your file or file object):

import pandas as pd
import xml.etree.ElementTree as ET
import io

def iter_docs(author):
    author_attr = author.attrib
    for doc in author.iter('document'):
        doc_dict = author_attr.copy()
        doc_dict.update(doc.attrib)
        doc_dict['data'] = doc.text
        yield doc_dict

xml_data = io.StringIO(u'''YOUR XML STRING HERE''')

etree = ET.parse(xml_data) #create an ElementTree object 
doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

If there are multiple authors in your original document or the root of your XML is not an author, then I would add the following generator:

def iter_author(etree):
    for author in etree.iter('author'):
        for row in iter_docs(author):
            yield row

and change doc_df = pd.DataFrame(list(iter_docs(etree.getroot()))) to doc_df = pd.DataFrame(list(iter_author(etree)))

Have a look at the ElementTree tutorial provided in the xml library documentation.

Method 2

As of v1.3, you can simply use:

pandas.read_xml(path_or_file)

Method 3

Here is another way of converting a xml to pandas data frame. For example i have parsing xml from a string but this logic holds good from reading file as well.

import pandas as pd
import xml.etree.ElementTree as ET

xml_str = '<?xml version="1.0" encoding="utf-8"?>n<response>n <head>n  <code>n   200n  </code>n </head>n <body>n  <data id="0" name="All Categories" t="2018052600" tg="1" type="category"/>n  <data id="13" name="RealEstate.com.au [H]" t="2018052600" tg="1" type="publication"/>n </body>n</response>'

etree = ET.fromstring(xml_str)
dfcols = ['id', 'name']
df = pd.DataFrame(columns=dfcols)

for i in etree.iter(tag='data'):
    df = df.append(
        pd.Series([i.get('id'), i.get('name')], index=dfcols),
        ignore_index=True)

df.head()

Method 4

You can also convert by creating a dictionary of elements and then directly converting to a data frame:

import xml.etree.ElementTree as ET
import pandas as pd

# Contents of test.xml
# <?xml version="1.0" encoding="utf-8"?> <tags>   <row Id="1" TagName="bayesian" Count="4699" ExcerptPostId="20258" WikiPostId="20257" />   <row Id="2" TagName="prior" Count="598" ExcerptPostId="62158" WikiPostId="62157" />   <row Id="3" TagName="elicitation" Count="10" />   <row Id="5" TagName="open-source" Count="16" /> </tags>

root = ET.parse('test.xml').getroot()

tags = {"tags":[]}
for elem in root:
    tag = {}
    tag["Id"] = elem.attrib['Id']
    tag["TagName"] = elem.attrib['TagName']
    tag["Count"] = elem.attrib['Count']
    tags["tags"]. append(tag)

df_users = pd.DataFrame(tags["tags"])
df_users.head()

Method 5

Chiming in to recommend the use of the xmltodict library. It handled your xml text pretty well and I’ve used it for ingesting an xml file with almost a million records.

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0