How to convert an XML file to nice pandas dataframe?

Question

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?

score 59 · Accepted Answer · edited Apr 21 '21 at 07:52

59

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.

edited Apr 21 '21 at 07:52

iacob

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answered Feb 01 '15 at 20:08

JaminSore

3,758
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2

Question was about how to load a xml **file**, so ideally your answer should have addressed that, rather than loading a xml string... – birgersp Aug 29 '17 at 12:13
1

@gromit190 Good point. I've updated my answer for reading from a file. – JaminSore Aug 30 '17 at 04:08
2

Tiny nitpick: `xml_data = io.StringIO('''` needs to be replaced with `xml_data = io.StringIO(u'''` because the parameter for [`StringIO`](https://docs.python.org/2.7/library/io.html#io.StringIO) needs to be unicode. Otherwise you get "TypeError: initial_value must be unicode or None, not str". – Cristian Ciupitu Jul 24 '18 at 12:12
@CristianCiupitu I see the question is tagged python-2.7 ---`u` prefix has been added. – JaminSore Jul 25 '18 at 02:59

iacob · Answer 2 · 2021-11-13T13:57:55.227

29

As of v1.3, you can simply use:

pandas.read_xml(path_or_file)

edited Nov 13 '21 at 13:57

answered Mar 25 '21 at 08:24

iacob

20,084
6
92
119

8

Actually, from this specific post, OP needs to adjust XPath to look one level deeper from root: `pandas.read_xml(path_or_file, xpath="/Author/document")` – Parfait May 19 '21 at 16:39

score 16 · Answer 3 · edited May 21 '19 at 17:01

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   200\n  </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()

score 4 · Answer 4 · edited Mar 25 '21 at 08:28

4

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.

edited Mar 25 '21 at 08:28

iacob

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answered Aug 29 '20 at 02:32

janoulle

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score 3 · Answer 5 · answered Apr 23 '19 at 03:41

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()

How to convert an XML file to nice pandas dataframe?

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