Use Python to convert xlsx files to xml files. 1) Use the openpyxl library to read the xlsx file, 2) Use the xml.etree.ElementTree library to create and write the xml file, 3) Iterate through the data in the xlsx file and fill it into the xml structure, 4) Handle the encoding, data type and missing value problems that may be encountered.

introduction

Converting xlsx files to xml format is not uncommon for data processing and system integration. Whether you need to import Excel data into a system, or you need to perform data analysis and conversion, this article will provide you with a detailed guide. After reading this article, you will learn how to convert xlsx files into xml files using the Python programming language through the openpyxl and xml.etree.ElementTree libraries, and learn about the problems and solutions you may encounter in this process.

Review of basic knowledge

Before we start the conversion, let's quickly review the relevant basics. xlsx is a file format for Microsoft Excel 2007 and later, and it is based on the open XML standard. xml is a markup language used to store and transmit data, and is widely used in data exchange and configuration files.

We will use Python's openpyxl library to read xlsx files, which allows us to easily process data in Excel files. The xml.etree.ElementTree library will help us create and manipulate xml files.

Core concept or function analysis

Definition and function of xlsx to xml conversion

The conversion of xlsx to xml is essentially a conversion in data format. We programmatically convert data in Excel tables into XML documents. Such transformations facilitate cross-platform sharing and processing of data, especially when data needs to be exchanged between different systems.

Let's look at a simple example, suppose we have an xlsx file with student information that we want to convert to xml format:

 import openpyxl
import xml.etree.ElementTree as ET

# Read xlsx file wb = openpyxl.load_workbook('students.xlsx')
sheet = wb.active

# Create xml root element root = ET.Element('students')

# traverse each row in the Excel table for row in sheet.iter_rows(min_row=2, values_only=True):
    student = ET.SubElement(root, 'student')
    ET.SubElement(student, 'name').text = row[0]
    ET.SubElement(student, 'age').text = str(row[1])
    ET.SubElement(student, 'grade').text = str(row[2])

# Write xml to file tree = ET.ElementTree(root)
tree.write('students.xml', encoding='utf-8', xml_declaration=True)

How it works

The conversion process mainly includes several steps: reading the xlsx file, creating the xml structure, filling the data and writing the xml file. The openpyxl library helps us read data in the xlsx file line by line, while the xml.etree.ElementTree library allows us to create a tree structure of xml and fill the data into the corresponding elements.

In actual operation, it should be noted that the structure of the xlsx file may be complex and changeable, and may contain multiple worksheets, merged cells, etc., which all need to be processed accordingly in the code.

Example of usage

Basic usage

The above code example already shows the basic conversion process, but let's take a closer look at the role of each step:

 import openpyxl
import xml.etree.ElementTree as ET

# Load xlsx file wb = openpyxl.load_workbook('students.xlsx')
sheet = wb.active # Get the activity sheet # Create xml root element root = ET.Element('students')

# Iterate through each row in the Excel table, starting from the second row (assuming the first row title)
for row in sheet.iter_rows(min_row=2, values_only=True):
    student = ET.SubElement(root, 'student') # Create a child element for each student ET.SubElement(student, 'name').text = row[0] # Set student name ET.SubElement(student, 'age').text = str(row[1]) # Set student age ET.SubElement(student, 'grade').text = str(row[2]) # Set student grade# Write xml to file tree = ET.ElementTree(root)
tree.write('students.xml', encoding='utf-8', xml_declaration=True)

Advanced Usage

In practical applications, xlsx files may contain more complex data structures, such as multi-layer nested data. At this time, we need to perform more complex processing in the code:

 import openpyxl
import xml.etree.ElementTree as ET

wb = openpyxl.load_workbook('complex_data.xlsx')
sheet = wb.active

root = ET.Element('data')

# Assuming that Excel tables have different data types, we need to create different xml structures based on different types for row in sheet.iter_rows(min_row=2, values_only=True):
    if row[0] == 'Person':
        person = ET.SubElement(root, 'person')
        ET.SubElement(person, 'name').text = row[1]
        ET.SubElement(person, 'age').text = str(row[2])
    elif row[0] == 'Address':
        address = ET.SubElement(root, 'address')
        ET.SubElement(address, 'street').text = row[1]
        ET.SubElement(address, 'city').text = row[2]

tree = ET.ElementTree(root)
tree.write('complex_data.xml', encoding='utf-8', xml_declaration=True)

Common Errors and Debugging Tips

During the conversion process, you may encounter the following problems:

• File encoding problem : xlsx files may contain non-UTF-8 encoded data, and you need to specify the correct encoding when reading and writing.
• Data type conversion : Data types in Excel may need to be processed when converted to xml, such as date types that need to be converted to strings.
• Missing data : If some cells in the Excel file are empty, this situation needs to be handled in xml to avoid generating an invalid xml structure.

Solutions to these problems include:

• Use data_only=True parameter of openpyxl library to read the calculated value of the formula.
• When writing to xml, use try-except block to handle possible exceptions.
• For null values, you can choose to ignore or fill the default values.

Performance optimization and best practices

Performance optimization and best practices are very important when performing xlsx to xml conversion:

• Batch processing : If the file is large, you can consider reading and processing data in batches to reduce memory usage.
• Data verification : During the conversion process, data verification logic can be added to ensure the integrity and consistency of the data.
• Code readability : Use clear variable naming and annotation to improve the readability and maintainability of the code.

In practical applications, the code can be optimized by comparing the performance differences of different methods. For example, using the pandas library to read xlsx files may be faster than openpyxl , but it needs to be selected according to the situation.

When writing code, it is very important to keep the code concise and structured, which not only improves the execution efficiency of the code, but also makes subsequent maintenance and extension easier.

Through the study of this article, you should have mastered the basic methods and techniques of how to convert xlsx files into xml files. Hope this knowledge comes in handy in your actual work.

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