XML Conversion for Data Scientists Made Easy

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Standing for Extensible Markup Language, XML is one of the most commonly used data formats for data storage and exchange. It’s a text based language introduced as an alternative to the classic HTML, which has reached its actual limits regarding data.

XML allows defining and storing data in a way that allows sharing information. Information exchange is supported between different systems, including databases or websites. For these reasons, it’s widely used in numerous industries under different formats, from medical data to financial transactions.

With all these, XML isn’t perfect. Despite its perfect integration of data in a sharing manner, it’s suitable for data science and analytics only if well structured and hierarchical. This isn’t always the case, so it needs to be transposed into a relational database.

Without this step, data analysis can be a time consuming and challenging task. Fortunately, there are ways to get around this minus.

XML is independent, so it can be used on all kinds of systems. It supports unicode as well, so it can transmit data written in more languages. The best thing about it is the fact that data stored and moved can be changed without altering the presentation.

XML allows using Schema or DTD for validation and boosts an impressive data sharing capability between businesses and industries because of its independent profile. XML data can be easily shared and requires no conversion whatsoever when moved between systems.

Based on all these benefits, XML is a highly flexible option, but it hasn’t been planned thoroughly, so it has a few flaws. The syntax is redundant, especially when compared to different data sharing formats. From this point of view, storage and sharing can be tricky when the volume is high.

XML files are large, and it can’t support arrays. It’s also less readable when compared to other similar formats.

And on top of all these, its hierarchical structure and parsing complexity turn XML into a challenge for many data scientists.

Converting XML for data science purposes

There are a few good reasons to convert data into XML, but there are disadvantages, too. Despite its flexibility, XML isn’t too easy to read. Sure, specialists from the same field or company can understand each other, but that’s pretty much it.

Proper data analytics over more platforms could do with a solid conversion. Fortunately, there are plenty of formats out there, with CSV and JSON dominating the field.

Once converted, XML data is easier to access, read and use. Depending on the conversion format, it will be compatible with various data analysis software, meaning assessing it is a quicker and more effective job.

The larger the data, the more difficult it is to analyze it without a thorough conversion.

Optimal tools and libraries for XML conversion

The DOM is one of the most versatile tools to use in XML conversion, as it features operations to modify data arranged in a hierarchy. Building such an abstract representation could be a time-consuming task, though.

SAX is a library developed by the Java community that does the same thing but also addresses some of the shortcomings associated with the DOM. While not as popular in Python, StAX is similar and offers more control over the conversion.

In terms of XML parsers in Python’s standard library, it’s worth mentioning:

  • xml.dom.minidom
  • xml.sax
  • xml.dom.pulldom
  • xml.etree.ElementTree

Third party XML parser libraries to consider include:

  • untangle
  • xmltodict
  • lxml
  • BeautifulSoup

Oracle and SQL Server also feature native solutions for XML data conversion.

Step by step instructions for XML conversion

The XML conversion is an actual necessity for data science today, so there are more ways to conduct it. Whether you’re after the CSV, JSON or other format, the principles are the same.

  • Data extraction: Prior to the conversion, data is extracted. Users will need to code based on the columns or fields they’re interested in.
  • Data transformation: There are more ways to transform or convert data. Using XSLT is one of the standard methods, as it can transform XML into numerous file types.
  • Data loading: Converted data is then loaded into a more readable format, whether tabular or relational. It depends on the type of data requested.

Automated XML conversion for greater benefits

Back in the day, XML conversion had a manual approach. These days, anyone can do it. The technical approach requires extra knowledge and experience.

However, there are easier ways to do it over all kinds of websites. Scientists only have to upload the XML file for the data to be converted.

The new file can be downloaded for easier data analysis.

You just need to choose a good converter that could process any volume of data very efficiently. We tried a couple of these as well and found the Flexter XML Converter from Sonra very efficient and reliable for what we actually desire.

The technical approach offers an in-depth analysis, but it has its drawbacks as well. Data loss is one of the most common issues, and when misinterpreted, results in data science and analysis can be misleading.

Hierarchical data isn’t always interpreted in the same manner. A wrongful interpretation can affect the final outcome of the conversion. So, one will also require very good skills to deal with the data. Last, but not least, based on the size and type of XML file, encoding errors may also occur.

The best way to overcome such issues is to double check the result, but that’s a vicious circle because that’s why you actually convert data, so you can analyze it in a more effective manner.

There are times when data is so overwhelming that an automated approach is the more effective option. Not only does it remove the potential for human errors, but it’s also less time consuming.

To do it manually, though, one would need to pick the right type of conversion method for the type of data or XML file they have.


Bottom line, XML is a superior option these days, but it does have a few flaws. While it’s easy to share and move, such data is difficult to interpret without a proper conversion, especially when it comes to automatic extractions.

While the manual approach is a more technical solution that provides more control, there are countless automatic alternatives that can save time and prevent errors.

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