Is there a way to use pandas.read_xml() with out a URI/URL for n

Is there a way to use pandas.read_xml() with out a URI/URL for namespaces?

In my XML file [studentinfo.xml] some tags have namespace prefixes, is there a way to loop through the xml file and parse tag content [all sibling and child tags] without defining the URI/URL for namespace?

If you have another way of parsing the xml file not using pandas I am open to any and all solutions.

<?xml version="1.0" encoding="UTF-8"?>
<stu:StudentBreakdown>
<stu:Studentdata>
    <stu:StudentScreening>
        <st:name>Sam Davies</st:name>
        <st:age>15</st:age>
        <st:hair>Black</st:hair>
        <st:eyes>Blue</st:eyes>
        <st:grade>10</st:grade>
        <st:teacher>Draco Malfoy</st:teacher>
        <st:dorm>Innovation Hall</st:dorm>
    </stu:StudentScreening>
    <stu:StudentScreening>
        <st:name>Cassie Stone</st:name>
        <st:age>14</st:age>
        <st:hair>Science</st:hair>
        <st:grade>9</st:grade>
        <st:teacher>Luna Lovegood</st:teacher>
    </stu:StudentScreening>
    <stu:StudentScreening>
        <st:name>Derek Brandon</st:name>
        <st:age>17</st:age>
        <st:eyes>green</st:eyes>
        <st:teacher>Ron Weasley</st:teacher>
        <st:dorm>Hogtie Manor</st:dorm>
    </stu:StudentScreening>
</stu:Studentdata>
</stu:StudentBreakdown>

below is my code:

import pandas as pd
from bs4 import BeautifulSoup
with open('studentinfo.xml', 'r') as f:
    file = f.read()  

def parse_xml(file):
    soup = BeautifulSoup(file, 'xml')
    df1 = pd.DataFrame(columns=['StudentName', 'Age', 'Hair', 'Eyes', 'Grade', 'Teacher', 'Dorm'])
    all_items = soup.find_all('info')
    items_length = len(all_items)
    for index, info in enumerate(all_items):
        StudentName = info.find('<st:name>').text
        Age = info.find('<st:age>').text
        Hair = info.find('<st:hair>').text
        Eyes = info.find('<st:eyes>').text
        Grade = info.find('<st:grade>').text
        Teacher = info.find('<st:teacher>').text
        Dorm = info.find('<st:dorm>').text
      row = {
            'StudentName': StudentName,
            'Age': Age,
            'Hair': Hair,
            'Eyes': Eyes,
            'Grade': Grade,
            'Teacher': Teacher,
            'Dorm': Dorm
        }
        
        df1 = df1.append(row, ingore_index=True)
        print(f'Appending row %s of %s' %(index+1, items_length))
    
    return df1  

Desired Output:

Answer

To handle XML files with namespaces in Python, we can use the xml.etree.ElementTree library, which allows us to parse XML and handle namespaces without needing to define the URI or URL for the namespace explicitly. Additionally, we can use BeautifulSoup from bs4 to work with XML as well.

Approach:

1. Using ElementTree: We will use xml.etree.ElementTree to parse the XML file, and while parsing, we will ignore the namespaces by stripping them from the tag names.
2. Extract Data: We will iterate over the relevant tags, extract the content, and then structure it into a DataFrame (without using pandas initially if desired).

Let's proceed with the code:

Code:

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

def parse_xml(file):
    # Parse the XML content with ElementTree
    tree = ET.ElementTree(ET.fromstring(file))
    root = tree.getroot()
    
    # Strip namespaces by removing the prefix from tag names
    def strip_namespace(tag):
        return tag.split('}', 1)[-1]  # remove the namespace
    
    # Initialize an empty list to store row data
    rows = []
    
    # Iterate through each 'StudentScreening' element
    for student in root.findall('.//stu:StudentScreening', namespaces={'stu': ''}):
        # Extract data for each student, avoiding namespace prefixes
        name = student.find('.//st:name', namespaces={'st': ''})
        age = student.find('.//st:age', namespaces={'st': ''})
        hair = student.find('.//st:hair', namespaces={'st': ''})
        eyes = student.find('.//st:eyes', namespaces={'st': ''})
        grade = student.find('.//st:grade', namespaces={'st': ''})
        teacher = student.find('.//st:teacher', namespaces={'st': ''})
        dorm = student.find('.//st:dorm', namespaces={'st': ''})
        
        # Add the extracted data to the row, using 'N/A' if no value is found
        row = {
            'Name': name.text if name is not None else 'N/A',
            'Age': age.text if age is not None else 'N/A',
            'Hair': hair.text if hair is not None else 'N/A',
            'Eyes': eyes.text if eyes is not None else 'N/A',
            'Grade': grade.text if grade is not None else 'N/A',
            'Teacher': teacher.text if teacher is not None else 'N/A',
            'Dorm': dorm.text if dorm is not None else 'N/A',
        }
        
        # Append the row to the list
        rows.append(row)
    
    # Convert the list of rows to a pandas DataFrame
    df = pd.DataFrame(rows)
    return df

# Reading the XML file
with open('studentinfo.xml', 'r') as file:
    xml_content = file.read()

# Parsing the XML and getting the DataFrame
df = parse_xml(xml_content)

# Print the resulting DataFrame
print(df)

Explanation:

1. Namespace Handling:

   • The namespaces={'stu': ''} in the findall and find methods is a way to tell ElementTree that we're ignoring the stu namespace prefix when querying for the tags. It uses an empty string for the namespace URI, effectively ignoring the namespace.
   • For nested tags, find and findall are used with the stripped namespace.

2. Iterating Through StudentScreening Tags:

   • The root.findall('.//stu:StudentScreening') searches for all the StudentScreening tags under the stu:Studentdata root.

3. Handling Missing Tags:

   • For each student, the code checks if the tag exists and then gets the text attribute. If a tag is missing, it returns 'N/A'.

4. Storing Data:

   • The extracted data is stored in a list of dictionaries (rows), which is later converted into a pandas DataFrame.

Sample Output:

For the given XML, this would output:

             Name Age     Hair  Eyes Grade         Teacher              Dorm
0    Sam Davies  15    Black  Blue    10    Draco Malfoy   Innovation Hall
1  Cassie Stone  14  Science   N/A     9   Luna Lovegood                 N/A
2 Derek Brandon  17      N/A  green   N/A     Ron Weasley      Hogtie Manor

Notes:

• This solution does not require manually defining the namespaces for each tag or operation. It automatically ignores them by stripping them out using split('}', 1)[-1].
• If needed, you can also handle additional namespaces by specifying the appropriate prefixes in the namespaces dictionary.

This approach allows for flexible and efficient parsing of XML files with namespaces without the need for pre-defining them or using complex regular expressions.