Argthtjtr

I'm dealing with XML files that are 4GB+ in size and wondering how I can best parse them. Right now I run into memory issues and looking for a way to not load the whole file in memory and go through it in batches perhaps?

Current code is using lxml and iterating over the repeating elements. Namespaces are cleared up front:

from lxml import etree, objectify

import pandas as pd



file = 'some_huge_file.xml'

if file.lower().endswith('.xml'):



    parser = etree.XMLParser(remove_blank_text=True)

    tree = etree.parse(mvnFile, parser)

    root = tree.getroot()



    ####

    for elem in root.getiterator():

        if not hasattr(elem.tag, 'find'): continue  # (1)

        i = elem.tag.find('}')

        if i >= 0:

            elem.tag = elem.tag[i + 1:]

    objectify.deannotate(root, cleanup_namespaces=True)

    ####

    data = [{

        'Element1': tp.findtext('element1'),

        'Element2': tp.findtext('element2'),

        'Element3': tp.findtext('element3'),

    }

        for tp in tree.xpath('//mainelement/subelement')]



     df = pd.DataFrame(data)

print(df)

Furthermore I need to do some splitting of the values of the elements as they are space-separated. However, I only need specific values so I'm wondering if I can do this somehow within the parsing instead of splitting the columns on space afterwards?

xml example:

<mainelement>

    <subelement tc="00:00:00:000" ms="0">

        <element1>0.861668 0.496888 0.000000 0.000000 0.867815</element1>

        <element2>0.043423 0.509801 -0.111990 -0.070212 0.126711</element2>

        <element3>-0.001501 0.008416 0.000098 0.005241 0.005301</element3>

    </subelement>

    <subelement tc="00:00:00:001" ms="1">

        <element1>-0.503814 0.005664 -0.070326 -0.860926 -0.503720</element1>

        <element2>-0.044658 0.046381 0.909291 -0.033390 0.049348</element2>

        <element3>-0.000000 -0.000000 -0.000000 -0.005217 0.007849</element3>

    </subelement>

    <subelement tc="00:00:00:002" ms="2">

        <element1> -0.861173 0.503578 -0.007163 0.056031 0.862101</element1>

        <element2>0.371398 1.325794 -0.030966 0.059466 1.388910</element2>

        <element3>-0.010139 0.001048 0.026847 -0.010139 0.001048</element3>

    </subelement>

    <subelement tc="00:00:00:003" ms="3">

        <element1>0.856813 0.494664 0.003921 0.023356 0.868762</element1>

        <element2>-0.030966 0.059466 1.388910 -0.152636 -0.008650</element2>

        <element3>0.001048 0.026847 -0.010139 0.001048 0.035846</element3>

    </subelement>

</mainelement>

Based on the links you posted in the comments I came up with the following to iterate and split more efficiently that works fine:

from lxml import etree, objectify

import pandas as pd



file = 'some_huge_file.xml'



time = 

data1_element1_x = 

data1_element1_y = 

data1_element2_x = 

data1_element2_y = 

data2_element1_x = 

data2_element1_y = 

data2_element2_x = 

data2_element2_y = 



if file.lower().endswith('.xml'):

    for event, elem in etree.iterparse(file):

        if elem.tag == "subelement":

            time.append(elem.get('tc'))

            for child in elem:

                if child.tag == "element1":

                    split_data = child.text.split(" ")

                    data1_element1_x.append(float(split_data[0]))

                    data1_element1_y.append(float(split_data[1]))

                    data2_element1_x.append(float(split_data[2]))

                    data2_element1_y.append(float(split_data[3]))

                elif child.tag == "element2":

                    split_data = child.text.split(" ")

                    data1_element2_x.append(float(split_data[0]))

                    data1_element2_y.append(float(split_data[1]))

                    data2_element2_x.append(float(split_data[2]))

                    data2_element2_y.append(float(split_data[3]))

             elem.clear()

df = pd.DataFrame({

    'Time':time, 

    'Data1_element1_x': data1_element1_x, 

    'Data1_element1_y': data1_element1_y, 

    'Data1_element2_x': data1_element2_x, 

    'Data1_element2_y': data1_element2_y, 

    'Data2_element1_x': data2_element1_x, 

    'Data2_element1_y': data2_element1_y, 

    'Data2_element2_x': data2_element2_x, 

    'Data2_element2_y': data2_element2_y

})



print(df)