--- title: How to Parse XML in Python published: true description: Explore various ways to parse XML in Python, covering both built-in libraries and external tools. You’ll learn how to save parsed data into formats like CSV and JSON. tags: parsexml, parsexmlpython, pythonparsexml, parsedata cover_image: https://crawlbase.com/blog/how-to-parse-xml-in-python/how-to-parse-xml-in-python-og.jpg canonical_url: https://crawlbase.com/blog/how-to-parse-xml-in-python/ # Use a ratio of 100:42 for best results. # published_at: 2024-10-02 13:01 +0000 --- This blog was originally posted to [Crawlbase Blog](https://crawlbase.com/blog/how-to-parse-xml-in-python/?utm_source=dev.to&utm_medium=referral&utm_campaign=content_distribution) XML (Extensible Markup Language) is a common format for storing and transferring data between different platforms and systems. As a Python developer working on web services, config files, or data transfer, you need to know how to parse XML files. You can use Python libraries to make XML parsing easy and fast. This article will cover various ways to parse XML in Python, both built-in libraries and external tools. You’ll learn how to handle XML files of all sizes, convert XML to dictionaries and save parsed data to CSV and JSON. We’ll also look at parsing invalid or malformed XML with more lenient tools. Let’s dive into the details of how to parse XML in Python. ## What is XML? XML, or Extensible Markup Language, is a data format to store and exchange data between different systems. It’s human-readable and machine-readable, that’s why it’s used in web services, configuration files, and more. ### Why XML? XML is used because it’s a flexible and organized way to represent complex data. Unlike CSV or plain text, XML allows you to create a hierarchy of elements and attributes, so it’s easier to understand and manipulate the data. Here are a few reasons why XML is preferred: - **Platform independence**: XML can be used with any operating system and programming language. - **Scalability**: XML files can contain simple and complex data structures. - **Readability**: Non-developers can read and understand XML. ### What is XML Parsing? XML parsing is the process of reading and processing an XML document to extract data. In Python, parsing XML allows you to browse XML documents, extract data, and change it as needed. This is especially important when working with APIs or other data exchange systems that use XML as their protocol. Python has built-in libraries and third-party tools to parse XML data, whether it’s a small config file or a big data source. In the next sections, we’ll see how. ## Parsing XML Using Python’s Built-in Libraries Python has powerful built-in libraries for working with XML data. These libraries allow you to parse XML files, extract what you need, and manipulate the data as required. Two popular libraries in Python for parsing XML are `xml.etree.ElementTree` and `xml.dom.minidom`. ### Parsing XML with `xml.etree.ElementTree` `xml.etree.ElementTree` is a lightweight library that comes with Python by default. It is an XML parsing library for Python, which was made to easily parse and navigate through XML files. For example, this is how you might use `ElementTree` to parse an XML string: ```python import xml.etree.ElementTree as ET # Example XML data xml_data = """ Wireless Mouse 29.99 Electronics Office Chair 89.99 Furniture """ # Parse the XML data root = ET.fromstring(xml_data) # Access and print data for product in root.findall('product'): name = product.find('name').text price = product.find('price').text category = product.find('category').text print(f"Product Name: {name}, Price: ${price}, Category: {category}") ``` In this example, we start by importing the `ElementTree` module. We can get the root element of an XML by parsing the XML string using `fromstring()` method. We will then use the `findall()`, and `find()` methods to search for a particular tag or extract text from inside tags. ### Parsing XML with xml.dom.minidom `xml.dom.minidom` is another built-in library that uses the Document Object Model (DOM) to parse and manipulate XML. It is more powerful and flexible, but it can be less easy to use than `ElementTree` in some cases. The same XML data can be parsed with minidom as follows: ```python from xml.dom.minidom import parseString # Example XML data xml_data = """ Wireless Mouse 29.99 Electronics Office Chair 89.99 Furniture """ # Parse the XML data dom = parseString(xml_data) # Access and print data products = dom.getElementsByTagName('product') for product in products: name = product.getElementsByTagName('name')[0].childNodes[0].nodeValue price = product.getElementsByTagName('price')[0].childNodes[0].nodeValue category = product.getElementsByTagName('category')[0].childNodes[0].nodeValue print(f"Product Name: {name}, Price: ${price}, Category: {category}") ``` In this example, `parseString()` is used to load the XML into a DOM object. We then use `getElementsByTagName()` to find the `product`, `name`, `price`, and `category` elements, and `childNodes[0].nodeValue` to extract the text. While `minidom` offers detailed control, it’s generally less efficient than `ElementTree` for simple tasks. ## Working with External XML Parsing Libraries You might want to stick with the built-in libraries for simple tasks when you are working with XML data in Python. On the other hand, for more complex requirements or better handling incorrect XML, you might choose another library to have more features and flexibility. In this part, we will discuss two famous external XML parsing libraries: lxml and BeautifulSoup. ### **Parsing XML with lxml** You can also use [lxml](https://pypi.org/project/lxml/) which is a very nice library when you are working with XML and HTML documents. It has good support for XPath and XSLT hence could be a powerful XML processor. To get started with lxml, you'll need to install it. You can do this via pip: ```bash pip install lxml ``` Here's an example of how to use `lxml` to parse XML data: ```python from lxml import etree # Example XML data xml_data = """ Wireless Mouse 29.99 Electronics Office Chair 89.99 Furniture """ # Parse the XML data root = etree.fromstring(xml_data) # Access and print data for product in root.xpath('//product'): name = product.find('name').text price = product.find('price').text category = product.find('category').text print(f"Product Name: {name}, Price: ${price}, Category: {category}") ``` In this example, we use `lxml`'s `etree` module to parse XML. With the `xpath` method, you can write powerful queries so that you can extract precisely from this messy XML structure. ### How to Handle Malformed XML with BeautifulSoup [BeautifulSoup](https://pypi.org/project/beautifulsoup4/) is often used for parsing HTML, but it can also handle malformed XML gracefully. This makes it a good choice for dealing with XML documents that may not be well-formed. To use `BeautifulSoup` for XML parsing, install the library along with a parser like `lxml`: ```bash pip install beautifulsoup4 lxml ``` Here’s an example of using BeautifulSoup to parse XML: ```python from bs4 import BeautifulSoup # Example XML data with a malformed element xml_data = """ Wireless Mouse 29.99 Electronics Office Chair 89.99 Furniture """ # Parse the XML data soup = BeautifulSoup(xml_data, 'lxml-xml') # Access and print data for product in soup.find_all('product'): name = product.find('name').get_text() price = product.find('price').get_text() category = product.find('category').get_text() print(f"Product Name: {name}, Price: ${price}, Category: {category}") ``` In this case, `BeautifulSoup` helps parse incomplete or broken XML documents. It is especially useful when you run into XML that deviates from the common practices of writing XML. In the next part, we will look at how to transform XML data into Python dictionaries for better manipulation. ## How to Convert XML to Dictionary in Python Working with XML data may be difficult, if the requirement is to edit or extract some elements of it. A common workaround is to convert the XML into a Python dictionary. A dictionary is used for storing and retrieving data as key-value pairs making us deal with data more easily. Let’s explore two popular libraries for converting XML into a Python dictionary: `xmltodict` and `untangle`. ### Using xmltodict [xmltodict](https://pypi.org/project/xmltodict/) is a simple library that can convert XML data to a dictionary in a few lines of code. It simplifies and speeds up the processing of XML data. To get started, you’ll need to install the library using pip: ```bash pip install xmltodict ``` Here’s an example of how to use `xmltodict` to convert XML into a dictionary: ```python import xmltodict # Example XML data xml_data = """ Notebook 5.99 100 Pencil 0.99 500 """ # Convert XML to a dictionary data_dict = xmltodict.parse(xml_data) # Access and print data for item in data_dict['store']['item']: name = item['name'] price = item['price'] quantity = item['quantity'] print(f"Item: {name}, Price: ${price}, Quantity: {quantity}") ``` In this example, `xmltodict.parse()` converts the XML data into a Python dictionary, allowing you to work with it as if it were a standard dictionary. This makes it much easier to retrieve and manipulate data from XML. ### Using `untangle` Another great library for parsing XML into Python objects is [untangle](https://pypi.org/project/untangle/). Unlike `xmltodict`, which converts XML into a dictionary, untangle turns the XML into Python objects that you can easily access through attributes. First, install the library using pip: ```bash pip install untangle ``` Here’s an example of how to use `untangle`: ```python import untangle # Example XML data xml_data = """ Notebook 5.99 100 Pencil 0.99 500 """ # Parse XML into Python objects data = untangle.parse(xml_data) # Access and print data for item in data.store.item: name = item.name.cdata price = item.price.cdata quantity = item.quantity.cdata print(f"Item: {name}, Price: ${price}, Quantity: {quantity}") ``` In this example, `untangle` converts the XML structure into Python objects. Each XML tag becomes an attribute of the object, and you can easily access the content using `cdata` (character data). Next, we’ll look at how to save the parsed XML data into different formats like CSV or JSON for further use. ## How to Save Parsed XML Data After parsing XML data, it is generally saved in more familiar forms such as CSV or JSON. This allows you to save, exchange, and analyze data in a more user-friendly manner for the majority of apps. In this part, we'll look at two ways to save parsed XML data: exporting it to CSV with pandas and saving it as JSON. ### Exporting to CSV with `pandas` CSV (Comma-Separated Values) files are commonly used to store tabular data. Python's `pandas` package makes it simple to save parsed XML data to a CSV file.To get started, make sure pandas is installed: ```bash pip install pandas ``` Here’s an example of how to convert XML data into a CSV file using pandas: ```python import xml.etree.ElementTree as ET import pandas as pd # Example XML data xml_data = """ Notebook 5.99 100 Pencil 0.99 500 """ # Parse XML root = ET.fromstring(xml_data) # Extract data and create a list of dictionaries data = [] for item in root.findall('item'): name = item.find('name').text price = item.find('price').text quantity = item.find('quantity').text data.append({'Name': name, 'Price': price, 'Quantity': quantity}) # Convert list of dictionaries to a pandas DataFrame df = pd.DataFrame(data) # Save DataFrame to a CSV file df.to_csv('store_items.csv', index=False) print("Data has been saved to store_items.csv") ``` In this example, we use `xml.etree.ElementTree` to parse the XML data, and then we extract relevant information (like `name`, `price`, and `quantity`) into a list of dictionaries. `pandas` is then used to create a DataFrame and save the data to a CSV file. ### Saving Data to JSON JSON (JavaScript Object Notation) is a lightweight data format used in web applications and APIs. Python has a built-in module called json that can convert parsed XML to JSON. Here’s how to convert XML to JSON and save to a file: ```python import xmltodict import json # Example XML data xml_data = """ Notebook 5.99 100 Pencil 0.99 500 """ # Convert XML to a dictionary using xmltodict data_dict = xmltodict.parse(xml_data) # Convert dictionary to JSON and save to a file with open('store_items.json', 'w') as json_file: json.dump(data_dict, json_file, indent=4) print("Data has been saved to store_items.json") ``` In this example, we use `xmltodict` to convert the XML to a dictionary and then the json module to convert that dictionary to JSON. The JSON is saved to a file called `store_items.json`. Next, we will cover how to handle large XML files. ## Handling Large XML Files Loading the entire file into memory can be slow and inefficient when dealing with large XML files. To address this, it’s better to use memory-friendly strategies that allow for processing the XML in smaller chunks. One effective way is to parse the XML file incrementally, reducing memory usage and speeding up processing time for large datasets. ### Stream Parsing with `iterparse` Stream parsing is an efficient technique for handling large XML files by processing them in chunks, instead of reading the whole file at once. Python’s `iterparse` method allows you to process XML data as it is being parsed, making it ideal for XML files that are too large to fit into memory. Here’s how `iterparse` works: 1. **Parse events:** With `iterparse`, you can define events like 'start' or 'end' to trigger actions when an XML element starts or ends. This gives you control over how and when each part of the XML is processed. 2. **Memory management:** After processing each element, you can clear it from memory to minimize memory usage, which is crucial when handling large XML files. Example: ```python import xml.etree.ElementTree as ET # Stream parse the XML file for event, element in ET.iterparse('large_file.xml', events=('end',)): if element.tag == 'product': # Extract product data name = element.find('name').text category = element.find('category').text price = element.find('price').text print(f"Product: {name}, Category: {category}, Price: {price}") # Clear the processed element from memory element.clear() ``` This example processes each `` element individually and then destroys the object to keep memory usage down. This method is very helpful when dealing with XML files with thousands or millions of elements. ## Final Thoughts Python has multiple flexible tools for dealing with XML, e.g. the built-in xml.etree.ElementTree to more advanced external third-party packages, like lxml and BeautifulSoup. Python can take care of anything you throw at it, such as simple parsing, converting XML to dictionaries, or handling large and malformed files. With the right tool, you can parse XML quickly and format it into either CSV or JSON.Using the methods discussed in the blog, you can easily handle XML parsing in Python. For more tutorials like these, follow our [blog](https://crawlbase.com/blog/). If you have any questions or feedback, our [support team](https://crawlbase.com/dashboard/support) is here to help you. ## Frequently Asked Questions (FAQs) ### Q. Is Python good for parsing XML? Yes, Python is excellent for parsing XML. It has built-in libraries like xml.etree.ElementTree and xml.dom.minidom make XML parsing easy and efficient. Third-party libraries like lxml and BeautifulSoup, on the other hand, have more advanced features are designed specifically to deal with complex or malformed XML data. ### Q. What is the best Python library for XML parsing? Which is the best library depends on one's needs. ElementTree is often enough for simple stuff. lxml or BeautifulSoup plugins are more suitable if you need to handle poorly-formed XML, boost processing speed, etc. ### Q. How can I convert XML to a dictionary in Python? Use libraries like [xmltodict](https://pypi.org/project/xmltodict/) or [untangle](https://pypi.org/project/untangle/) to Convert XML to Python dictionary easily. The libraries provide an easy way to convert XML data into Python dictionaries, allowing you to interact with and use your data easily.

pythonparsexml

How to Parse XML in Python

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