From XML to Pandas dataframes

In this example, each student is represented by a <student> element, which has a name attribute containing the name of a specific student. Each of these elements has then sub-elements defined by the <email>, <grade> and <age> tags; between these tags the actual data content referring to the given student is present. Let’s say this data is saved in an XML file called “students.xml”.

So we want to find a way to convert XML-structured data to a more functional table.

Now we can iterate through each node of the tree, which means we will get each student element and grab its name attribute and all of its sub-elements to build our dataframe.

In order to get the name attribute, we use the attrib.get() function, while the text content of each element can be retrieved using the find() function of nodes.
Each iteration will return a set of data that can be thought as an observation in a pandas DataFrame; we can build this procedure as follows:

The downside to this approach is that you need to know the structure of the XML file in advance, and you have to hard-code column names accordingly.
We can try to convert this code to a more useful and versatile function, without having to hard-code any values:

An interesting piece of code is the dict comprehension in:

where a dictionary in the form column name: node value is created and added to the list of rows. These will finally be used to create the complete dataframe.

This is a more efficient implementation of the XML parsing function, although we still need to know the basic structure of the input XML document we want to parse.

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