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:
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 |
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:
- Using
ElementTree
: We will usexml.etree.ElementTree
to parse the XML file, and while parsing, we will ignore the namespaces by stripping them from the tag names. - 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:
-
Namespace Handling:
- The
namespaces={'stu': ''}
in thefindall
andfind
methods is a way to tellElementTree
that we're ignoring thestu
namespace prefix when querying for the tags. It uses an empty string for the namespace URI, effectively ignoring the namespace. - For nested tags,
find
andfindall
are used with the stripped namespace.
- The
-
Iterating Through
StudentScreening
Tags:- The
root.findall('.//stu:StudentScreening')
searches for all theStudentScreening
tags under thestu:Studentdata
root.
- The
-
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'
.
- For each student, the code checks if the tag exists and then gets the
-
Storing Data:
- The extracted data is stored in a list of dictionaries (
rows
), which is later converted into a pandas DataFrame.
- The extracted data is stored in a list of dictionaries (
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.