Large XML File Parsing in Python

Question

I have an XML file of size 4 GB. I want to parse it and convert it to a Data Frame to work on it. But because the file size is too large the following code is unable to convert the file to a Pandas Data Frame. The code just keeps loading and does not provide any output. But when I use it for a similar file of smaller size I obtain the correct output.

Can anyone suggest any solution to this. Maybe a code that speeds up the process of conversion from XML to Data Frame or splitting of the XML file into smaller sub sets.

Any suggestion whether I should work with such large XML files on my personal system (2 GB RAM) or I should use Google Colab. If Google Colab, then is there any way to upload such large files quicker to drive and thus to Colab?

Following is the code I had used:

import xml.etree.ElementTree as ET
tree = ET.parse("Badges.xml")
root = tree.getroot()

#Column names for DataFrame
columns = ['row Id',"UserId",'Name','Date','Class','TagBased']

#Creating DataFrame
df = pd.DataFrame(columns = columns)

#Converting XML Tree to a Pandas DataFrame

for node in root: 
    
    row_Id = node.attrib.get("Id")
    UserId = node.attrib.get("UserId")
    Name = node.attrib.get("Name")
    Date = node.attrib.get("Date")
    Class = node.attrib.get("Class")
    TagBased = node.attrib.get("TagBased")
    
    df = df.append(pd.Series([row_Id,UserId,Name,Date,Class,TagBased], index = columns), ignore_index = True)

Following is my XML File:

<badges>
  <row Id="82946" UserId="3718" Name="Teacher" Date="2008-09-15T08:55:03.923" Class="3" TagBased="False" />
  <row Id="82947" UserId="994" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82949" UserId="3893" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82950" UserId="4591" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82951" UserId="5196" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82952" UserId="2635" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />
  <row Id="82953" UserId="1113" Name="Teacher" Date="2008-09-15T08:55:03.957" Class="3" TagBased="False" />

Answer 1

Consider using cElementTree instead of ElementTree

https://effbot.org/zone/celementtree.htm

The cElementTree module is a C implementation of the ElementTree API, optimized for fast parsing and low memory use. On typical documents, cElementTree is 15-20 times faster than the Python version of ElementTree, and uses 2-5 times less memory.

The cElementTree module is designed to replace the ElementTree module from the standard elementtree package. In theory, you should be able to simply change:

from elementtree import ElementTree

to

import cElementTree as ElementTree

Answer 2

Consider iterparse for fast streaming processing that builds tree incrementally. In each iteration build a list of dictionaries that you can then pass into pandas.DataFrame constructor once outside loop. Adjust below to name of repeating nodes of root's children:

from xml.etree.ElementTree import iterparse
#from cElementTree import iterparse
import pandas as pd

file_path = r"/path/to/Input.xml"
dict_list = []

for _, elem in iterparse(file_path, events=("end",)):
    if elem.tag == "row":
        dict_list.append({'rowId': elem.attrib['Id'],
                          'UserId': elem.attrib['UserId'],
                          'Name': elem.attrib['Name'],
                          'Date': elem.attrib['Date'],
                          'Class': elem.attrib['Class'],
                          'TagBased': elem.attrib['TagBased']})

        # dict_list.append(elem.attrib)      # ALTERNATIVELY, PARSE ALL ATTRIBUTES

        elem.clear()

df = pd.DataFrame(dict_list)