Pandas DataFrame read_xml() Method

The Python Pandas library provides theread_xml() method toread data from an XML document and convert it into a Pandas DataFrame object. This method is a powerful tool for handling structured XML data in tabular form, enabling users to process and analyze XML data effectively.

XML (Extensible Markup Language) is a popular data exchange format, often used for hierarchical and structured data. With theread_xml() method, you can extract XML data into Pandas for further manipulation and analysis. This method also provides various options for handling complex XML structures and attributes.

Syntax

The syntax of theread_xml() method is as follows −

pandas.read_xml(path_or_buffer, *, xpath='./*', namespaces=None, elems_only=False, attrs_only=False, names=None, dtype=None, converters=None, parse_dates=None, encoding='utf-8', parser='lxml', stylesheet=None, iterparse=None, compression='infer', storage_options=None, dtype_backend=<no_default>

Parameters

The Python Pandasread_xml() method accepts the following parameters −

• path_or_buffer: The file path, URL, or file-like object containing the XML data.

• xpath: A string that specifies the XPath to parse specific nodes in the XML. Default is"./*".

• namespaces: A dictionary to map prefixes to namespace URIs for XPath.

• elems_only: IfTrue, parses only child elements.

• attrs_only: If set toTrue, parses only attributes at the specified XPath.

• names: Determines the column names for parsing the XML data into DataFrame.

• dtype: Specifies the data type for columns in resultant DataFrame.

• converters: It takes a dictionary of functions for converting values in certain columns.

• parser: The XML parser to use. Options include'lxml' (default) and'etree'.

• stylesheet: Path to an XSLT stylesheet to transform the XML before parsing.

• dtype_backend: Specifies the data backend fornullable types.

• encoding: The encoding of the input file. Default is'utf-8'.

• compression: Indicates if the XML file is compressed. Options include'infer','gzip','bz2','zip', etc.

• storage_options: Additional options for remote storage connections.

Return Value

Theread_xml() method returns a Pandas DataFrame containing the parsed data from the XML document.

Example: Reading a Simple XML File

Here is a basic example that demonstrates how to use theread_xml() method to read an XML file and convert it to a DataFrame.

import pandas as pd# Create an XML Document first# Sample DataFramedf = pd.DataFrame({'name': ['Tanmay', 'Manisha'],'company': ['TutorialsPoint', 'TutorialsPoint'],'phone': ['(011) 123-4567', '(011) 789-4567']})# Save DataFrame to XML df.to_xml("simple_data.xml")# Read XML data from a filedf = pd.read_xml('simple_data.xml')print("DataFrame:")print(df)

The output of the above code will be −

Example: Reading an XML String

Instead of reading XML data from a local file, the following example demonstrates reading a string that representing the XML data into the Pandas DataFrame.

import pandas as pdfrom io import StringIO# Create an XML stringxml_string ="""<?xml version='1.0' encoding='utf-8'?><data>  <row>    <index>0</index>    <Name>Kiran</Name>    <Age>25</Age>    <City>New Delhi</City>  </row>  <row>    <index>1</index>    <Name>Priya</Name>    <Age>30</Age>    <City>Hyderabad</City>  </row>  <row>    <index>2</index>    <Name>Naveen</Name>    <Age>35</Age>    <City>Chennai</City>  </row></data>"""# Read XML string df = pd.read_xml(StringIO(xml_string))print("DataFrame from XML string:")print(df)

Output of the above code is as follows −

DataFrame from XML string:

Example: Reading XML Data with Custom XPath

This example demonstrates how to use a custom XPath query to extract specific elements from an XML document. The following example reads only the "title" element from the XML data using thexpath parameter.

import pandas as pdfrom io import StringIO# Create an XML Stringxml = """<?xml version="1.0" encoding="UTF-8"?><bookstore>  <book category="cooking">    <title lang="en">Everyday Italian</title>    <author>Giada De Laurentiis</author>    <year>2005</year>    <price>30.00</price>  </book>  <book category="children">    <title lang="en">Harry Potter</title>    <author>J K. Rowling</author>    <year>2005</year>    <price>29.99</price>  </book>  <book category="web">    <title lang="en">Learning XML</title>    <author>Erik T. Ray</author>    <year>2003</year>    <price>39.95</price>  </book></bookstore>"""# Read XML data with a custom XPathdf = pd.read_xml(StringIO(xml), xpath=".//title")# Diaplay the Output DataFrameprint("Output DataFrame with custom XPath:")print(df)

Following is an output of the above code −

Output DataFrame with custom XPath:

Example: Reading Compressed XML Files

Thecompression parameter allows reading compressed XML files. The following example shows how to read a compressed XML file using thecompression parameter.

import pandas as pd# Create an XML Document first# Sample DataFramedf = pd.DataFrame({'name': ['Tanmay', 'Manisha'],'company': ['TutorialsPoint', 'TutorialsPoint'],'phone': ['(011) 123-4567', '(011) 789-4567']})# Save DataFrame to compressed XML df.to_xml("compressed_data.xml.gz", compression='gzip')# Read a compressed XML filedf = pd.read_xml('compressed_data.xml.gz', compression='gzip')print("DataFrame from compressed XML:")print(df)

Output of the above code is as follows −

DataFrame from compressed XML:

Example: Parsing XML with Custom Date Parsing

This example uses thepandas.read_xml() method to handlenullable types anddate parsing using thedtype_backend andparse_dates parameters respectively.

import pandas as pdfrom io import StringIO# XML string with timestampsxml_content = '''<data>    <record>        <id>1</id>        <value>3.14</value>        <flag>True</flag>        <label>X</label>        <timestamp>2025-01-01 12:00:00</timestamp>    </record>    <record>        <id>2</id>        <value>6.28</value>        <flag>False</flag>        <label>Y</label>        <timestamp>2025-01-02 12:00:00</timestamp>    </record></data>'''# Parsing the XML datadf = pd.read_xml(StringIO(xml_content),                  dtype_backend="numpy_nullable",                  parse_dates=["timestamp"])# Diaplay the Output DataFrameprint("Output DataFrame:")print(df)

Following is an output of the above code −

Output DataFrame: