How to create Pandas DataFrame from nested XML?

Last Updated : 28 Apr, 2021

In this article, we will learn how to create Pandas DataFrame from nested XML. We will use the xml.etree.ElementTree module, which is a built-in module in Python for parsing or reading information from the XML file. The ElementTree represents the XML document as a tree and the Element represents only a single node of the tree.

Functions Used:

Here, we will use some functions to process out code which is stated below:

ElementTree.parse( XML_file) : To read data from an XML file
root.iter('root_name'): To iterate through the branches of the root node
ElementTree.fromstring(XML_file) : To read data when XML code which is passed as a string inside triple quotes in the python code
prstree.findall('store'): To find all the elements of the parsed XML ElementTree
node.attribute.get(attribte_name ): To get the attribute
node.find(attribte_name): To retrieve the text content of the mentioned attribute_name
pandas.DataFrame() : To convert the XML data to a DataFrame
list.append(): To append the items to a list

Approach

Parse or read the XML file using ElementTree.parse( ) function and get the root element.
Iterate through the root node to get the child nodes attributes 'SL NO' (here) and extract the text values of each attribute (here foodItem, price, quantity, and discount).
Get the respective food items with specifications as a unit appended to a list(here all_items() list).
Convert the list into a DataFrame using pandas.DataFrame() function and mention the column names within quotes separated by commas.
Print the DataFrame and it's done.

Input Nested XML Data

XML

<?xml version="1.0" encoding="UTF-8"?>

       <Food>

           <Info>
           <Msg>Food Store items.</Msg>
           </Info>

           <store slNo="1">
               <foodItem>meat</foodItem>
               <price>200</price>
               <quantity>1kg</quantity>
               <discount>7%</discount>
           </store>

           <store slNo="2">
               <foodItem>fish</foodItem>
               <price>150</price>
               <quantity>1kg</quantity>
               <discount>5%</discount>
           </store>

           <store slNo="3">
               <foodItem>egg</foodItem>
               <price>100</price>
               <quantity>50 pieces</quantity>
               <discount>5%</discount>
           </store>

           <store slNo="4">
               <foodItem>milk</foodItem>
               <price>50</price>
               <quantity>1 litre</quantity>
               <discount>3%</discount>
           </store>

       </Food>

Example 1:

In this code below we have parsed the XML file. Give the complete path where you have saved the XML file within quotes. So here we need to use ElementTree.parse() function to read the data from the XML file and then the getroot() function to get the root. Then follow the steps given.

Python3

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

# give the path where you saved the xml file
# inside the quotes
xmldata = "C: \\ProgramData\\Microsoft\\
    Windows\\Start Menu\\Programs\\
    Anaconda3(64-bit)\\xmltopandas.xml"
prstree = ETree.parse(xmldata)
root = prstree.getroot()

# print(root)
store_items = []
all_items = []

for storeno in root.iter('store'):
  
    store_Nr = storeno.attrib.get('slNo')
    itemsF = storeno.find('foodItem').text
    price = storeno.find('price').text
    quan = storeno.find('quantity').text
    dis = storeno.find('discount').text

    store_items = [store_Nr, itemsF, price, quan, dis]
    all_items.append(store_items)

xmlToDf = pd.DataFrame(all_items, columns=[
  'SL No', 'ITEM_NUMBER', 'PRICE', 'QUANTITY', 'DISCOUNT'])

print(xmlToDf.to_string(index=False))

Output:

Note: The XML file should be saved in the same directory or folder where your Python code saved.

Example 2:

We can also pass the XML content as a string inside triple quotes. In that case, we need to use the fromstring() function to read the string. Get the root using the 'tag' object and follow the same steps to convert it to a DataFrame as mentioned above.