Convert Pandas DataFrame to XML File Using to_xml

The to_xml method allows you to convert a DataFrame into an XML format.

In this tutorial, we will walk through the steps required to export a Pandas DataFrame to an XML file using the to_xml method.

 

 

Pandas to_xml Syntax

The basic syntax for to_xml methods is as follows:

DataFrame.to_xml(path_or_buffer=None, root_name="data", row_name="row", na_rep="nan", attr_cols=None, elem_cols=None, attrs_prefix='@', elems_prefix='_', header=True, index=True, root_attrs=None, encoding=None, xml_declaration=True, pretty_print=True, processor=None, **kwargs)

Here are some of the commonly used parameters of this method:

• path_or_buffer: Target file or a writable buffer. If not provided, the result is returned as a string.
• root_name: The name for the root element. Default is “data”.
• row_name: The name for the row elements. Default is “row”.
• na_rep: The representation for missing values. Default is “nan”.
• attr_cols: The columns to serialize as attributes in the row element.
• elem_cols: The columns to serialize as child elements.
• attrs_prefix: Prefix to add for each row attribute. Default is ‘@’.
• elems_prefix: Prefix to add for each row child element. Default is ‘_’.
• header: Whether to include the header in the output. Default is True.
• index: Whether to include the index in the output. Default is True.
• root_attrs: A dictionary containing the attributes for the root element.
• encoding: The character encoding for the output file. For example, “utf-8”.
• xml_declaration: Whether to include the XML declaration at the beginning of the file. Default is True.
• pretty_print: Whether to include indentation for nested elements. Default is True.

 

Specifying a File Path or a Buffer

The path_or_buffer parameter in the to_xml method defines where the resulting XML content will be written. You have two main options:

1. Specify a file path where the XML data will be saved.
2. Use a buffer (like StringIO) to catch the XML content for further processing.

Let’s explore both scenarios.

Exporting to a File

import pandas as pd
df = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30],
    'Occupation': ['Engineer', 'Doctor']
})
df.to_xml("sample_data.xml")

If you check your current directory, you’ll find a file named sample_data.xml. It contains:

<?xml version='1.0' encoding='utf-8'?>
<data>
    <row index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </row>
    <row index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </row>
</data>

Writing to a Buffer

from io import BytesIO

buffer = BytesIO()
df.to_xml(buffer)
buffer.seek(0)  # Reset buffer position to the beginning
xml_content = buffer.read().decode('utf-8')
print(xml_content)

Output:

<?xml version='1.0' encoding='utf-8'?>
<data>
    <row index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </row>
    <row index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </row>
</data>

In this scenario, we utilize a StringIO buffer to capture the XML content.

After invoking the to_xml method, you can retrieve the XML content from the buffer and process it as needed.

 

Selecting an XML Parser

The processor parameter of the to_xml method allows you to specify the XML parser to use. Pandas supports the following parsers:

1. lxml
2. etree (Python’s built-in xml.etree.ElementTree)

Using lxml as the Processor

To use lxml, you first need to install it:

!pip install lxml

Once installed, specify it in the to_xml method:

df.to_xml("sample_data_lxml.xml", processor='lxml')

This will generate an XML file using the lxml library. The result would be similar to the default, but lxml might offer additional features compared to the standard library.

Using etree as the Processor

Here’s how to specify it:

df.to_xml("sample_data_etree.xml", processor='etree')

This command writes the XML data using the etree parser.

 

Specifying the Name for the Root Element

In an XML document, the root element is the top-most element that contains all other elements.

The root_name parameter in the to_xml method allows you to specify a custom name for the root element to comply with a given XML schema.

Suppose we want to change the root element name to “Employees”:

df.to_xml("sample_data.xml",root_name="Employees")

Output:

<?xml version='1.0' encoding='utf-8'?>
<Employees>
    <row index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </row>
    <row index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </row>
</Employees>

The root element is now named <Employees>, making the XML structure more semantically meaningful for this data.

 

Remove Index

By default, when converting a DataFrame to XML using the to_xml method, the index of each row is included as an attribute.

To exclude the index from the XML, you can set the index parameter of to_xml to False:

print(df.to_xml(root_name="Company", row_name="Employee", index=False))

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
	<Employee>
		<Name>Alice</Name>
                <Age>25</Age>
		<Position>Engineer</Position>
	</Employee>
	<Employee>
		<Name>Bob</Name>
                <Age>30</Age>
		<Position>Doctor</Position>
	</Employee>
</Company>

The index is now absent from each <Employee> element.

 

Naming Individual Row Elements

Within the root element, each row of the DataFrame gets translated into an individual XML element. By default, this element is named <row>.

The row_name parameter allows you to customize the name of these individual row elements.

Change Row Element Name

Suppose we want each row to represent an individual “Employee”. To achieve this:

df.to_xml("sample_data.xml", root_name="Company", row_name="Employee")

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
    <Employee index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </Employee>
</Company>

Each data entry is now encapsulated within an <Employee> element.

 

Specifying a Default XML Namespace

XML namespaces are used to distinguish XML elements and attributes that may have the same name but belong to different XML vocabularies.

The namespaces parameter allows you to assign a default namespace, as well as additional namespaces, to your XML output.

Let’s assign a default namespace to our XML:

namespace = "http://www.example.com/employees"
df.to_xml("sample_data.xml", root_name="Company", row_name="Employee", namespaces={"": namespace})

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company xmlns="http://www.example.com/employees">
    <Employee index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </Employee>
</Company>

The xmlns attribute in the Company root element specifies the default namespace for all the elements.

Assign Multiple Namespaces

You can also define multiple namespaces:

namespaces = {
    "": "http://www.example.com/employees",
    "role": "http://www.example.com/employee_roles"
}
df.to_xml("sample_data.xml", root_name="Company", row_name="Employee", namespaces=namespaces)

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company xmlns="http://www.example.com/employees" xmlns:role="http://www.example.com/employee_roles">
    <Employee index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Age>30</Age>
        <Occupation>Doctor</Occupation>
    </Employee>
</Company>

In this output, the default namespace is still http://www.example.com/employees, but we’ve also added a new namespace for “role” which could be used for elements or attributes specific to employee roles.

 

Columns to Write as Attributes

Sometimes, instead of representing every column in your DataFrame as a separate element, you want some of them to be presented as attributes of another element.

The attr_cols parameter in the to_xml method lets you define which columns should be rendered as attributes.

Suppose we want the “Age” column to be an attribute of the “Employee” element:

df.to_xml("sample_data.xml", root_name="Company", row_name="Employee", attr_cols=['Age'])

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
  <Employee index="0" Age="25"/>
  <Employee index="1" Age="30"/>
</Company>

The “Age” column is now represented as an attribute for each “Employee” element rather than a separate child element.

You can represent the omitted elements by using elem_cols as we’ll see next.

 

Specify Columns to Write as Elements

The to_xml method offers the elem_cols parameter to specify which columns should be rendered as XML elements.

Let’s say we only want the “Occupation” column to be represented as an element:

df.to_xml("sample_data.xml", root_name="Company", row_name="Employee", elem_cols=['Occupation'])

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
  <Employee>
    <index>0</index>
    <Occupation>Engineer</Occupation>
  </Employee>
  <Employee>
    <index>1</index>
    <Occupation>Doctor</Occupation>
  </Employee>
</Company>

By using the elem_cols parameter, only the “Occupation” column is represented as an element in the XML output, while other columns are omitted.

To represent other omitted columns, you can include them in the elem_cols parameter.

 

Replacing NaNs with a Specified Value

When exporting data to XML, your data can have missing or empty elements.

If you want to replace missing elements with a specific value. The to_xml method offers the na_rep parameter to address this concern.

First, let’s create a sample DataFrame with NaN values:

import pandas as pd
import numpy as np
data = {
    'Name': ['Alice', 'Bob'],
    'Age': [25, np.nan],
    'Occupation': ['Engineer', 'Doctor']
}
df = pd.DataFrame(data)
print(df.to_xml(root_name="Company", row_name="Employee"))

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
    <Employee index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Age/>
        <Occupation>Doctor</Occupation>
    </Employee>
</Company>

Notice that for Bob, the <Age> element is empty because the value is NaN in the DataFrame.

Now, let’s replace NaN values with the string “Unknown”:

print(df.to_xml(root_name="Company", row_name="Employee", na_rep="Unknown"))

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
    <Employee index="0">
        <Name>Alice</Name>
        <Age>25</Age>
        <Occupation>Engineer</Occupation>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Age>Unknown</Age>
        <Occupation>Doctor</Occupation>
    </Employee>
</Company>

With the na_rep parameter, the NaN value in Bob’s “Age” column is replaced with “Unknown”.

 

Excluding the XML Declaration

The declaration <?xml version='1.0' encoding='utf-8'?> is included at the top of most XML documents.

By default, when you use to_xml, it includes the XML declaration.

If you wish to exclude the XML declaration, set the xml_declaration parameter to False:

print(df.to_xml(root_name="Company", row_name="Employee", xml_declaration=False))

Output:

<Company>
    <Employee index="0">
        <Name>Alice</Name>
        <Position>Engineer</Position>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Position>Doctor</Position>
    </Employee>
</Company>

The choice of including or excluding the XML declaration often depends on the target application or system that will process the XML.

 

Disable Pretty Printing

The to_xml method in Pandas has a parameter named pretty_print that controls indentation to the XML output for better readability.

By default, the to_xml method outputs XML with indentation.

To disable indentation, set the pretty_print parameter to False:

import pandas as pd
data = {
    'Name': ['Alice', 'Bob'],
    'Position': ['Engineer', 'Doctor']
}
df = pd.DataFrame(data)
print(df.to_xml(root_name="Company", row_name="Employee", pretty_print=False))

Output:

<?xml version='1.0' encoding='utf-8'?><Company><Employee index="0"><Name>Alice</Name><Position>Engineer</Position></Employee><Employee index="1"><Name>Bob</Name><Position>Doctor</Position></Employee></Company>

 

Applying an XSLT Stylesheet

XSLT is particularly useful when you want to change the structure of an XML document or convert it into a different format.

You can use the stylesheet parameter of the to_xml method to apply an XSLT stylesheet to the XML output.

First, let’s generate a simple XML representation of our DataFrame:

import pandas as pd
data = {
    'Name': ['Alice', 'Bob'],
    'Position': ['Engineer', 'Doctor']
}
df = pd.DataFrame(data)
print(df.to_xml(root_name="Company", row_name="Employee"))

Output:

<?xml version='1.0' encoding='utf-8'?>
<Company>
    <Employee index="0">
        <Name>Alice</Name>
        <Position>Engineer</Position>
    </Employee>
    <Employee index="1">
        <Name>Bob</Name>
        <Position>Doctor</Position>
    </Employee>
</Company>

Suppose we have the following XSLT stylesheet that transforms our XML into HTML:

<?xml version="1.0" encoding="UTF-8"?>
<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
<xsl:template match="/">
  <html>
  <body>
    <h2>Company Employees</h2>
    <table border="1">
      <tr>
        <th>Name</th>
        <th>Position</th>
      </tr>
      <xsl:for-each select="Company/Employee">
      <tr>
        <td><xsl:value-of select="Name"/></td>
        <td><xsl:value-of select="Position"/></td>
      </tr>
      </xsl:for-each>
    </table>
  </body>
  </html>
</xsl:template>
</xsl:stylesheet>

This XSLT transforms the XML data into an HTML table.

Now, let’s apply this stylesheet:

xslt = '''...'''  # The XSLT content shown above

print(df.to_xml(root_name="Company", row_name="Employee", stylesheet=xslt))

Output:

<html>
  <body>
    <h2>Company Employees</h2>
    <table border="1">
      <tr>
        <th>Name</th>
        <th>Position</th>
      </tr>
      <tr>
        <td>Alice</td>
        <td>Engineer</td>
      </tr>
      <tr>
        <td>Bob</td>
        <td>Doctor</td>
      </tr>
    </table>
  </body>
</html>

Note: Don’t leave any spaces at the beginning of the XSLT stylesheet to avoid errors.

 

Defining the Compression Type

The to_xml method in Pandas offers a compression parameter that enables you to specify the type of compression when exporting the XML data.

To save the XML data with compression, you can specify the compression parameter:

# Saving the XML with gzip compression
df.to_xml("output.xml.gz", root_name="Company", row_name="Employee", compression='gzip')

With this code, the XML content will be compressed using the gzip format and saved as output.xml.gz.

Pandas to_xml supports multiple compression types, including:

• ‘gzip’
• ‘bz2’
• ‘zip’
• ‘xz’

Reading Compressed XML Data

It’s worth noting that if you have saved your XML in a compressed format and want to read it back into a DataFrame, you can easily do so using Pandas read_xml function:

compressed_df = pd.read_xml("output.xml.gz", compression='gzip')
print(compressed_df)

Output:

      Name   Position
0    Alice  Engineer
1      Bob    Doctor
2  Charlie    Artist

 

Storage Connection Options

The to_xml method in Pandas includes the storage_options parameter, allowing you to pass a dictionary of extra options for storage connections such as cloud storage providers or distributed file systems.

For instance, if you’re working with Amazon S3, you might need to provide specific credentials:

s3_options = {
    'key': 'YOUR_ACCESS_KEY',
    'secret': 'YOUR_SECRET_KEY',
    'use_ssl': False  # Example option to not use SSL (default is True)
}
df.to_xml("s3://my_bucket/output.xml", root_name="Company", row_name="Employee", storage_options=s3_options)

Please note: Always be cautious when hardcoding credentials in your code. It’s safer to use environment variables or other methods to keep secrets out of source code.

 

Resource

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_xml.html