XML is a widely used format for storing structured data, but it’s not ideal for analysis or tabular tools like Excel. Converting XML to CSV allows you to work with the data in a simpler, flat format that’s compatible with spreadsheets and data analysis libraries. By converting XML to CSV, you can easily import data into Excel, perform calculations, or feed it into Python data analysis tools like Pandas.

This approach also helps standardize complex hierarchical data into a format that is easier to read, manipulate, and share across different applications. In this tutorial, we’ll explore how to efficiently transform XML files into CSV using Spire.XLS for Python.

Table of Contents:

• Setting Up Spire.XLS for Python
• Understanding XML Data Structures
• How XML Data Is Extracted and Converted
• Basic Example: Convert XML to CSV with Python
• Advanced Techniques
  • Exporting Only Specific Elements
  • Including XML Attributes in CSV
  • Handling Nested XML Structures
• Troubleshooting and Common Issues
• Conclusion
• FAQs

Setting Up Spire.XLS for Python

Before we can start, we need to install the Spire.XLS library. The package is available on PyPI , so installation is straightforward. Run the following command:

pip install spire.xls

Once installed, you can import the required classes:

from spire.xls import *
from spire.xls.common import *

Spire.XLS provides the Workbook and Worksheet objects for managing Excel-like files. You’ll use them to create new CSV files and populate them with your XML data.

Understanding XML Data Structures

XML files are organized in a tree-like hierarchy, where elements (or nodes) are enclosed in tags. Each element can contain text, attributes, or even other child elements.

For example, consider this Books.xml:

<catalog>
  <book isbn="9780451524935">
    <title>The Catcher in the Rye</title>
    <author>J.D. Salinger</author>
    <genre>Fiction</genre>
    <reviews>
      <review>Excellent coming-of-age story</review>
      <review>A true classic</review>
    </reviews>
  </book>
  <book isbn="9780439023528">
    <title>The Hunger Games</title>
    <author>Suzanne Collins</author>
    <genre>Adventure</genre>
  </book>
</catalog>

• Root node : <catalog> is the top-level container.
• Child nodes : Each <book> is a child of <catalog>.
• Elements : <title>, <author>, and <genre> are elements inside each <book>.
• Attributes : The isbn in <book isbn="..."> is an attribute attached to the book element.
• Nested elements : The <reviews> node contains multiple <review> sub-nodes.

Challenges when converting XML to CSV:

• Hierarchical data – XML allows nesting (e.g., <reviews> inside <book>), while CSV is flat.
• Attributes vs. elements – Data may be stored as an attribute (isbn) or as a tag (title).
• Optional fields – Not all <book> elements may contain the same tags, which can lead to missing values in the CSV.

A robust converter must be able to handle these differences and map hierarchical XML into a flat, tabular CSV format.

How XML Data Is Extracted and Converted

To load and parse an XML file in Python, you can use the built-in xml.etree.ElementTree library. This library lets you navigate the XML tree, retrieve elements, and access attributes. For example:

import xml.etree.ElementTree as ET

# Load the XML file
tree = ET.parse("Books.xml")
root = tree.getroot()

# Iterate through elements
for book in root.findall("book"):
    title = book.findtext("title", "")
    author = book.findtext("author", "")
    isbn = book.attrib.get("isbn", "")

After extracting the XML data, the next step is to map it to a tabular structure . Using Spire.XLS for Python , you can create a workbook, write the extracted values into worksheet cells, and finally export the worksheet as a CSV file for easy analysis.

General Steps to Convert XML to CSV in Python

1. Use xml.etree.ElementTree to load and retrieve data from the XML file.
2. Create a Workbook object using Spire.XLS.
3. Add a worksheet with Workbook.Worksheets.Add() .
4. Write extracted XML data into the worksheet using Worksheet.SetValue() .
5. Save the worksheet to a CSV file using Worksheet.SaveToFile() .

Basic Example: Convert XML to CSV with Python

Let’s start with a basic XML-to-CSV conversion. This example automatically generates headers by inspecting the first <book> element and then exports all child nodes into CSV.

from spire.xls import *
from spire.xls.common import *
import xml.etree.ElementTree as ET

# Create a Workbook object
workbook = Workbook()

# Remove default worksheets
workbook.Worksheets.Clear()

# Add a worksheet and name it
worksheet = workbook.Worksheets.Add("Books")

# Load an XML file
xml_tree = ET.parse("C:\\Users\\Administrator\\Desktop\\Books.xml")

# Get the root element of the XML tree
xml_root = xml_tree.getroot()

# Get the first the "book" element
first_book = xml_root.find("book")

# Extract header information and convert it into a list
header = list(first_book.iter())[1:]  

# Write header to Excel
for col_index, header_node in enumerate(header, start=1):
    header_text = header_node.tag
    worksheet.SetValue(1, col_index, header_text)

# Write other data to Excel by iterating over each book element and each data node within it
row_index = 2
for book in xml_root.iter("book"):
    for col_index, data_node in enumerate(list(book.iter())[1:], start=1):  
        value = data_node.text
        header_text = list(header[col_index - 1].iter())[0].tag
        worksheet.SetValue(row_index, col_index, value)
    row_index += 1

# Save the document to an Excel file
worksheet.SaveToFile("output/XmlToCsv.csv", ",", Encoding.get_UTF8())

# Dispose resources
workbook.Dispose()

This script works well for simple, flat XML structures. It automatically generates headers (title, author, genre, price) and fills each row with corresponding values.

Output:

You might also be interested in: Convert XML to Excel and PDF in Python

Advanced Techniques

The basic script works in many cases, but XML often isn’t so simple. Let’s go through some advanced techniques to handle real-world scenarios.

Exporting Only Specific Elements

Sometimes your XML has more data than you need. Maybe you only want to export title and author, ignoring everything else.

from spire.xls import *
from spire.xls.common import *
import xml.etree.ElementTree as ET

# Create a Workbook object
workbook = Workbook()

# Remove default worksheets
workbook.Worksheets.Clear()

# Add a worksheet and name it
worksheet = workbook.Worksheets.Add("Books")

# Load the XML file
xml_tree = ET.parse(r"C:\Users\Administrator\Desktop\Books.xml")
xml_root = xml_tree.getroot()

# Define the elements you want to export
selected_elements = ["title", "author"]

# Write header
for col_index, tag in enumerate(selected_elements, start=1):
    worksheet.SetValue(1, col_index, tag)

# Write data
row_index = 2
for book in xml_root.iter("book"):
    for col_index, tag in enumerate(selected_elements, start=1):
        # Use findtext to handle missing values safely
        worksheet.SetValue(row_index, col_index, book.findtext(tag, ""))
    row_index += 1

# Save the document to a CSV file
worksheet.SaveToFile("output/XmlToCsv_Selected.csv", ",", Encoding.get_UTF8())

# Dispose resources
workbook.Dispose()

This approach ensures your CSV only contains the columns you care about.

Output:

Including XML Attributes in CSV

What if your XML contains important data stored as attributes, such as isbn? You can easily include them:

from spire.xls import *
from spire.xls.common import *
import xml.etree.ElementTree as ET

# Create a Workbook object
workbook = Workbook()

# Remove default worksheets
workbook.Worksheets.Clear()

# Add a worksheet and name it
worksheet = workbook.Worksheets.Add("Books")

# Load an XML file
xml_tree = ET.parse(r"C:\Users\Administrator\Desktop\Books.xml")

# Get the root element of the XML tree
xml_root = xml_tree.getroot()

# Get the first the "book" element
first_book = xml_root.find("book")

# Extract header information (child nodes)
header = list(first_book.iter())[1:]

# Write header to Excel
worksheet.SetValue(1, 1, "isbn")   # <-- Add ISBN column first
for col_index, header_node in enumerate(header, start=2):  # start at 2 now
    header_text = header_node.tag
    worksheet.SetValue(1, col_index, header_text)

# Write data
row_index = 2
for book in xml_root.iter("book"):
    # Write isbn as text
    isbn_value = book.attrib.get("isbn", "")
    worksheet.Range[row_index, 1].Text = isbn_value

    # Then write other fields
    for col_index, data_node in enumerate(list(book.iter())[1:], start=2):
        value = data_node.text
        worksheet.SetValue(row_index, col_index, value)

    row_index += 1

# Format the whole ISBN column as text to prevent scientific notation
last_row = row_index - 1   
isbn_range = f"A2:A{last_row}"
worksheet.Range[isbn_range].NumberFormat = "@"

# Save the document to an Excel file (CSV format)
worksheet.SaveToFile("output/XmlToCsv_WithAttributes.csv", ",", Encoding.get_UTF8())

# Dispose resources
workbook.Dispose()

Here we explicitly create an ISBN column, extract it from each <book>’s attributes, and format it as text to prevent Excel from displaying it in scientific notation.

Output:

Handling Nested XML Structures

Nested nodes are common. Suppose your XML looks like this:

<catalog>
  <book>
    <title>1984</title>
    <author>George Orwell</author>
    <reviews>
      <review>Excellent read!</review>
      <review>Still relevant today.</review>
    </reviews>
  </book>
</catalog>

Here’s how to collapse multiple <review> entries into a single CSV column:

from spire.xls import *
from spire.xls.common import *
import xml.etree.ElementTree as ET

# Create a Workbook object
workbook = Workbook()

# Remove default worksheets
workbook.Worksheets.Clear()

# Add a worksheet and name it
worksheet = workbook.Worksheets.Add("Books")

# Load an XML file
xml_tree = ET.parse(r"C:\Users\Administrator\Desktop\Nested.xml")
xml_root = xml_tree.getroot()

# Get the first <book> element
first_book = xml_root.find("book")

# Collect headers (auto-detected)
header = []
for child in first_book:
    if child.tag == "reviews":  
        header.append("reviews")   # collapse nested <review> into one column
    else:
        header.append(child.tag)

# Write header row
for col_index, header_text in enumerate(header, start=1):
    worksheet.SetValue(1, col_index, header_text)

# Write data rows
row_index = 2
for book in xml_root.iter("book"):
    col_index = 1
    for child in book:
        if child.tag == "reviews":
            # Join all <review> texts into a single cell
            reviews = [r.text for r in child.findall("review") if r.text]
            worksheet.SetValue(row_index, col_index, "; ".join(reviews))
        else:
            worksheet.SetValue(row_index, col_index, child.text if child.text else "")
        col_index += 1
    row_index += 1

# Save to CSV
worksheet.SaveToFile("output/XmlToCsv_WithReviews.csv", ",", Encoding.get_UTF8())

# Dispose resources
workbook.Dispose()

Output:

Spire.XLS not only supports importing data from standard XML files into Excel or CSV, but also allows converting OpenXML (Microsoft's XML-based file format) to Excel. If you're interested, check out this tutorial: How to Convert Excel to OpenXML and OpenXML to Excel in Python.

Troubleshooting and Common Issues

Even with a well-structured script, you may encounter some common issues when converting XML to CSV:

1. Scientific notation in Excel
   • Problem: Long numeric strings like ISBNs may appear as 9.78045E+12 instead of the full number.
   • Solution: Format the column as text before saving, for example:

   worksheet.Range["A2:A{last_row}"].NumberFormat = "@"
   

2. Missing values causing errors

   • Problem: Some <book> elements may lack optional fields (e.g., <genre>). Attempting to access .text directly can cause errors.
   • Solution: Use findtext(tag, "") to safely provide a default empty string.

3. Incomplete or unexpected headers

   • Problem: If you generate headers from only the first <book>, you might miss fields that appear later in the XML.
   • Solution: Scan multiple elements (or the entire dataset) to build a complete header list before writing data.

4. Encoding issues

   • Problem: Special characters (such as accents or symbols) may not display correctly in the CSV.
   • Solution: Always save with UTF-8 encoding:

   worksheet.SaveToFile("output.csv",",", Encoding.get_UTF8())
   

Conclusion

Converting XML to CSV in Python doesn’t have to be painful. With Spire.XLS for Python, you can automate much of the process, including header generation, handling attributes, and flattening nested nodes. Whether you’re exporting only a few fields, working with complex hierarchies, or cleaning up messy XML, Spire.XLS gives you the flexibility to handle it.

By integrating these approaches into your workflow, you can turn structured XML datasets into clean, analysis-ready CSV files in just a few lines of code.

FAQs

Q1: Can I export directly to Excel (.xlsx) instead of CSV?

Yes. Simply use workbook.SaveToFile("output.xlsx", ExcelVersion.Version2016).

Q2: How do I handle very large XML files?

Use Python’s iterparse() from xml.etree.ElementTree to stream large files instead of loading them fully into memory.

Q3: What if some <book> elements contain additional tags?

You can enhance the header-building step to scan all <book> nodes and collect unique tags dynamically.

Q4: Can I customize the CSV delimiter (e.g., use ; instead of ,)?

Yes. When calling SaveToFile(), replace the delimiter argument:

worksheet.SaveToFile("output.csv", ";", Encoding.get_UTF8())

Q5: How do I export nested XML structures (e.g., multiple <review> nodes)?

Flatten them into a single cell by joining values. For example:

reviews = [r.text for r in book.find("reviews").findall("review")]
worksheet.SetValue(row_index, col_index, "; ".join(reviews))

Get a Free License

To fully experience the capabilities of Spire.XLS for Python without any evaluation limitations, you can request a free 30-day trial license.

When working with spreadsheets, readability is just as important as the data itself. A well-formatted Excel file makes it easier to analyze, present, and share information. Instead of manually adjusting styles in Excel, you can use Python for Excel formatting to automate the process and save significant time.

This tutorial shows you how to format Excel with Python using the library Spire.XLS for Python. We’ll cover basic styling, advanced formatting, and practical use cases, while also explaining the key classes and properties that make Excel formatting in Python efficient.

Here's What's Covered:

• Why Use Python for Excel Formatting
• Setting Up the Environment and Project
• Basic Excel Formatting in Python
• Extended Excel Formatting in Python
• Key APIs for Excel Styling in Python
• Use Case: Formatting an Excel Report with Python
• Conclusion
• FAQ

Why Use Python for Excel Formatting

Formatting Excel manually is time-consuming, especially when handling large datasets or generating reports dynamically. By using Python Excel formatting, you can:

• Apply consistent formatting to multiple workbooks.
• Automate repetitive tasks like setting fonts, borders, and colors.
• Generate styled reports programmatically for business or research.
• Save time while improving accuracy and presentation quality.

With Python, you can quickly build scripts that apply professional-looking styles to your spreadsheets. Next, let’s see how to set up the environment.

Setting Up the Environment and Project

To follow this tutorial, you need to install Spire.XLS for Python, a library designed for working with Excel files. It supports creating, reading, modifying, and formatting Excel documents programmatically.

Install Spire.XLS for Python

Install the library via pip:

pip install Spire.XLS

Then import it in your Python script:

from spire.xls import *

Creating or Loading an Excel Workbook

Before we start formatting, we need a workbook to work with.

Create a new workbook:

workbook = Workbook()
sheet = workbook.Worksheets[0]

Or load an existing file:

workbook = Workbook()
workbook.LoadFromFile("input.xlsx")
sheet = workbook.Worksheets[0]

After applying formatting, save the result:

workbook.SaveToFile("output/formatted_output.xlsx", ExcelVersion.Version2016)

With the workbook ready, let’s move on to formatting examples.

Basic Excel Formatting in Python

Before diving into advanced operations, it’s important to master the fundamental Excel formatting features in Python. These basic techniques—such as fonts, alignment, borders, background colors, and adjusting column widths or row heights—are the building blocks of clear, professional spreadsheets. Once familiar with them, you can combine and extend these methods to create more complex styles later.

1. Formatting Fonts

Changing font properties is one of the most frequent tasks when working with styled Excel sheets. In Spire.XLS for Python, font settings are accessed through the CellRange.Style.Font object, which lets you control the typeface, size, color, and emphasis (bold, italic, underline).

cell = sheet.Range[2, 2]
cell.Text = "Python Excel Formatting"

cell.Style.Font.FontName = "Arial"
cell.Style.Font.Size = 14
cell.Style.Font.Color = Color.get_Blue()
cell.Style.Font.IsBold = True

This modifies the text appearance directly within the cell by adjusting its style attributes.

2. Alignment and Wrapping

Cell alignment is managed through the HorizontalAlignment and VerticalAlignment properties of the Style object. In addition, the WrapText property ensures that longer text fits within a cell without overflowing.

cell = sheet.Range[4, 2]
cell.Text = "This text is centered and wrapped."

cell.Style.HorizontalAlignment = HorizontalAlignType.Center
cell.Style.VerticalAlignment = VerticalAlignType.Center
cell.Style.WrapText = True

This produces neatly centered text that remains readable even when it spans multiple lines.

3. Adding Borders

Borders are defined through the Borders collection on the Style object, where you can set the line style and color for each edge individually.

cell = sheet.Range[6, 2]
cell.Text = "Border Example"

cell.Style.Borders[BordersLineType.EdgeBottom].LineStyle = LineStyleType.Thin
cell.Style.Borders[BordersLineType.EdgeBottom].Color = Color.get_Black()

This adds a thin black line to separate the cell from the content below.

4. Background Colors

Cell background is controlled by the Style.Color property. This is often used to highlight headers or important values.

cell = sheet.Range[8, 2]
cell.Text = "Highlighted Cell"
cell.Style.Color = Color.get_Yellow()

This makes the cell stand out in the worksheet.

5. Setting Column Widths and Row Heights

Besides styling text and borders, adjusting the column widths and row heights ensures that your content fits properly without overlapping or leaving excessive blank space.

# Set specific column width
sheet.Columns[1].ColumnWidth = 20
# Set specific row height
sheet.Rows[7].RowHeight = 20

In addition to specifying widths and heights, Excel rows and columns can also be automatically adjusted to fit their content:

• Use Worksheet.AutoFitColumn(columnIndex) or Worksheet.AutoFitRow(rowIndex) to adjust a specific column or row.
• Use CellRange.AutoFitColumns() or CellRange.AutoFitRows() to adjust all columns or rows in a selected range.

This helps create well-structured spreadsheets where data is both readable and neatly aligned.

Preview of Basic Formatting Result

Here’s what the basic Excel formatting looks like in practice:

In addition to these visual styles, you can also customize how numbers, dates, or currency values are displayed—see How to Set Excel Number Format in Python for details.

Extended Excel Formatting in Python

Instead of formatting individual cells, you can also work with ranges and reusable styles. These operations typically involve the CellRange object for merging and sizing, and the Workbook.Styles collection for creating reusable Style definitions.

1. Merging Cells

Merging cells is often used to create report titles or section headers that span multiple columns.

range = sheet.Range[2, 2, 2, 4]
range.Merge()
range.Text = "Quarterly Report"
range.Style.HorizontalAlignment = HorizontalAlignType.Center
range.RowHeight = 30

Here, three columns (B2:D2) are merged into one, styled as a bold, centered title with a shaded background—perfect for highlighting key sections in a report.

2. Applying Built-in Styles

Excel comes with a set of predefined styles that can be applied quickly. In Spire.XLS, you can directly assign these built-in styles to ranges.

range.BuiltInStyle = BuiltInStyles.Heading1

This instantly applies Excel’s built-in Heading 1 style, giving the range a consistent and professional appearance without manually setting fonts, borders, or colors.

3. Creating a Custom Style and Applying It

Creating a custom style is useful when you need to apply the same formatting rules across multiple cells or sheets. The Workbook.Styles.Add() method allows you to define a named style that can be reused throughout the workbook.

# Create a custom style
custom_style = workbook.Styles.Add("CustomStyle")
custom_style.Font.FontName = "Calibri"
custom_style.Font.Size = 12
custom_style.Font.Color = Color.get_DarkGreen()
custom_style.Borders[BordersLineType.EdgeTop].LineStyle = LineStyleType.MediumDashDot

# Apply style to a cell
cell = sheet.Range[4, 2]
cell.Text = "Custom Style Applied"
cell.Style = custom_style

This method makes Excel style management in Python more efficient, especially when working with large datasets.

Preview of Advanced Formatting Result

Below is an example showing the results of merged cells, built-in styles, and a custom style applied:

For highlighting data patterns and trends, you can also use conditional rules—see How to Apply Conditional Formatting in Python Excel.

Key APIs for Excel Styling in Python

After exploring both basic and advanced formatting examples, it’s helpful to step back and look at the core classes, properties, and methods that make Excel styling in Python possible. Understanding these elements will give you the foundation to write scripts that are flexible, reusable, and easier to maintain.

The following table summarizes the most important classes, properties, and methods for formatting Excel with Python. You can use it as a quick reference whenever you need to recall the key styling options.

With these building blocks, you can handle common formatting tasks—such as fonts, borders, alignment, colors, and number formats—in a structured and highly customizable way. This ensures that your spreadsheets look professional, remain easy to manage, and can be consistently styled across different Python automation projects. For more details, see the Spire.XLS for Python API reference.

Real-World Use Case: Formatting an Annual Excel Sales Report with Python

Now that we’ve explored both basic and advanced Excel formatting techniques, let’s apply them in a real-world scenario. The following example demonstrates how to generate a comprehensive Excel annual sales report with Python. By combining structured data, regional breakdowns, and advanced formatting, the report becomes much easier to interpret and presents business data in a clear and professional way.

from spire.xls import *

workbook = Workbook()
sheet = workbook.Worksheets[0]
sheet.Name = "Sales Report"

# Report title
title = sheet.Range[1, 1, 1, 7]
title.Merge()
title.Text = "Annual Sales Report - 2024"
title.Style.Font.IsBold = True
title.Style.Font.Size = 16
title.Style.HorizontalAlignment = HorizontalAlignType.Center
title.Style.Color = Color.get_LightGray()
title.RowHeight = 30

# Data
data = [
    ["Product", "Region", "Q1", "Q2", "Q3", "Q4", "Total"],
    ["Laptop", "North", 1200, 1500, 1300, 1600, 5600],
    ["Laptop", "South", 1000, 1200, 1100, 1300, 4600],
    ["Tablet", "North", 800, 950, 1000, 1200, 3950],
    ["Tablet", "South", 700, 850, 900, 1000, 3450],
    ["Phone", "North", 2000, 2200, 2100, 2500, 8800],
    ["Phone", "South", 1800, 1900, 2000, 2200, 7900],
    ["Accessories", "All", 600, 750, 720, 900, 2970],
    ["", "", "", "", "", "Grand Total", 39370]
]

for r in range(len(data)):
    for c in range(len(data[r])):
        sheet.Range[r+2, c+1].Text = str(data[r][c])

# Header formatting
header = sheet.Range[2, 1, 2, 7]
header.Style.Font.IsBold = True
header.Style.Color = Color.get_LightBlue()
header.Style.HorizontalAlignment = HorizontalAlignType.Center
header.Style.Borders[BordersLineType.EdgeBottom].LineStyle = LineStyleType.Thin

# Numeric columns as currency
for row in range(3, 10):
    for col in range(3, 8):
        cell = sheet.Range[row, col]
        if cell.Text.isdigit():
            cell.NumberValue = float(cell.Text)
            cell.NumberFormat = "$#,##0"

# Highlight totals
grand_total = sheet.Range[10, 7]
grand_total.Style.Color = Color.get_LightYellow()
grand_total.Style.Font.IsBold = True

# Freeze the first row and the first two columns
sheet.FreezePanes(2, 3)

# Auto fit columns
sheet.AllocatedRange.AutoFitColumns()

workbook.SaveToFile("output/annual_sales_report.xlsx", ExcelVersion.Version2016)

This script combines several formatting techniques—such as a merged and styled title, bold headers with borders, currency formatting, highlighted totals, and frozen panes—into a single workflow. Applying these styles programmatically improves readability and ensures consistency across every report, which is especially valuable for business and financial data.

Here’s the final styled annual sales report generated with Python:

Conclusion

Formatting Excel with Python is a practical way to automate report generation and ensure professional presentation of data. By combining basic styling with advanced techniques like custom styles and column adjustments, you can create clear, consistent, and polished spreadsheets.

Whether you are working on financial reports, research data, or business dashboards, formatting Excel with Python helps you save time while maintaining presentation quality. With the right use of styles, properties, and formatting options, your spreadsheets will not only contain valuable data but also deliver it in a visually effective way.

You can apply for a free temporary license to unlock the full functionality of Spire.XLS for Python or try Free Spire.XLS for Python to get started quickly.

FAQ - Excel Fpormatting in Python

Q1: Can you format Excel with Python?

Yes. Python provides libraries that allow you to apply fonts, colors, borders, alignment, conditional formatting, and more to Excel files programmatically.

Q2: How to do formatting in Python?

For Excel formatting, you can use a library such as Spire.XLS for Python. It lets you change fonts, set alignment, adjust column widths, merge cells, and apply custom or built-in styles through code.

Q3: Can I use Python to edit Excel?

Yes. Python can not only format but also create, read, modify, and save Excel files, making it useful for dynamic reporting and data automation.

Q4: What’s the best way to automate repeated Excel styling tasks?

Define custom styles or reusable functions that standardize formatting rules across multiple workbooks and sheets. This ensures consistency and saves time.

JSON (JavaScript Object Notation) is the most widely used format for structured data in web applications, APIs, and configuration files. CSV (Comma-Separated Values) is a popular format for data analysis, reporting, and seamless integration with Excel. Converting JSON to CSV is a common requirement for developers, data engineers, and analysts who need to transform hierarchical JSON data into a tabular format for easier processing, visualization, and automation.

This step-by-step guide demonstrates how to convert JSON to CSV using Python, including techniques for handling flat and nested JSON data, and optimizing performance for large JSON datasets. By following this tutorial, you can streamline your data workflows and efficiently integrate JSON data into Excel or other tabular systems.

What You Will Learn

• Why Convert JSON to CSV
• Understanding JSON Structures
• Python JSON to CSV Converter – Installation
• Step-by-Step JSON to CSV Conversion in Python
  • Handling Flat JSON Data
  • Handling Nested JSON with Dictionaries and Arrays
• Performance Tips for Exporting Large or Complex JSON Files to CSV
• Conclusion
• FAQs

Why Convert JSON to CSV?

Although JSON is ideal for hierarchical and complex data, CSV offers several practical advantages:

• Spreadsheet Compatibility: CSV files can be opened in Excel, Google Sheets, and other BI tools.
• Ease of Analysis: Tabular data is easier to filter, sort, summarize, and visualize.
• Data Pipeline Integration: Many ETL workflows and reporting systems rely on CSV for seamless data integration.

Real-World Use Cases:

• API Data Extraction: Transform JSON responses from web APIs into CSV for analysis or reporting.
• Reporting Automation: Convert application or system logs into CSV for automated dashboards or scheduled reports.
• Data Analytics: Prepare hierarchical JSON data for Excel, Google Sheets, or BI tools like Tableau to perform pivot tables and visualizations.
• ETL Pipelines: Flatten and export JSON from databases or log files into CSV for batch processing or integration into data warehouses.

Converting JSON files to CSV format bridges the gap between structured storage and table-based analytics, making it a common requirement in reporting, data migration, and analytics pipelines.

Understanding JSON Structures

Before implementing JSON to CSV conversion in Python, it is important to understand the two common structures of JSON data:

• Flat JSON: Each object contains simple key-value pairs.

[
    {"name": "Alice", "age": 28, "city": "New York"},
    {"name": "Bob", "age": 34, "city": "Los Angeles"}
]

• Nested JSON: Objects contain nested dictionaries or arrays.

[
    {
        "name": "Alice",
        "age": 28,
        "contacts": {"email": "alice@example.com", "phone": "123-456"}
    }
]

Flat JSON can be directly mapped to CSV columns, while nested JSON requires flattening to ensure proper tabular representation.

Python JSON to CSV Converter - Installation

To export JSON to CSV in Python, you can use Spire.XLS for Python, a spreadsheet library that enables Python developers to create, read, manipulate, and export spreadsheet files directly from Python. It supports common formats like .xls, .xlsx, .csv, and .ods.

Installation

You can install the library from PyPI via pip:

pip install spire.xls

If you need assistance with the installation, refer to this tutorial: How to Install Spire.XLS for Python on Windows.

Once installed, you can import it into your Python scripts:

from spire.xls import *

This setup allows seamless JSON-to-CSV conversion with complete control over the workbook structure and output format.

Step-by-Step JSON to CSV Conversion in Python

Converting JSON to CSV involves four main steps: loading JSON data, creating a workbook, writing headers and rows, and exporting the final file as CSV. Below, we’ll go through the process for both flat JSON and nested JSON.

Handling Flat JSON Data

Step 1: Load JSON and Create Workbook

First, load your JSON file into Python and create a new workbook where the data will be written.

import json
from spire.xls import *

# Load JSON data
with open('data.json') as f:
    data = json.load(f)

# Create workbook and worksheet
workbook = Workbook()
sheet = workbook.Worksheets[0]

Explanation:

• json.load parses your JSON file into Python objects (lists and dictionaries).
• Workbook() creates a new Excel workbook in memory.
• workbook.Worksheets[0] accesses the first worksheet where data will be written.

Step 2: Write Headers Dynamically

Next, generate column headers from the JSON keys. This ensures that the CSV reflects the structure of your data.

# Extract headers from JSON keys
headers = list(data[0].keys())

# Write headers to the first row
for col, header in enumerate(headers, start=1):
    sheet.Range[1, col].Value = header

Explanation:

• list(data[0].keys()) retrieves all top-level JSON keys.
• sheet.Range[1, col].Value writes headers in the first row, starting from column 1.

Step 3: Write Data Rows

After headers are set, populate the worksheet row by row with values from each JSON object.

# Populate values from each JSON object to the subsequent rows
for row_index, item in enumerate(data, start=2):
    for col_index, key in enumerate(headers, start=1):
        value = item.get(key, "")
        sheet.Range[row_index, col_index].Value = str(value) if value is not None else ""

Explanation:

• Loop starts from row 2 because row 1 is reserved for headers.
• Each JSON object is mapped to a row, and each key is mapped to a column.

Step 4: Save the Worksheet as CSV

Finally, save the worksheet as a CSV file and clean up resources.

# Save the worksheet as a CSV file
sheet.SaveToFile("output.csv", ",", Encoding.get_UTF8())
workbook.Dispose()

Resulting CSV (output.csv):

This example uses Python’s built-in json module to parse JSON data. For more details on its functions and usage, refer to the Python json module documentation.

If you also want to implement JSON to Excel conversion, see our guide on converting JSON to/from Excel in Python.

Handling Nested JSON with Dictionaries and Arrays

When JSON objects contain nested dictionaries or arrays, direct CSV export is not possible because CSV is a flat format. To ensure compatibility, nested fields must be flattened to preserve all data in a readable CSV format.

Suppose you have the following JSON file (nested.json) with nested data:

[
    {
        "Name": "Alice",
        "Age": 28,
        "City": "New York",
        "Contacts": {"Email": "alice@example.com", "Phone": "123-456"},
        "Skills": ["Python", "Excel", "SQL"]
    },
    {
        "Name": "Bob",
        "Age": 34,
        "City": "Los Angeles",
        "Contacts": {"Email": "bob@example.com", "Phone": "987-654"},
        "Skills": ["Java", "CSV", "JSON"]
    }
]

This JSON contains:

• Flat fields: Name, Age, City
• Nested dictionary: Contacts
• Array: Skills

To export all fields to CSV, nested dictionaries need to be flattened, and arrays need to be joined into semicolon-separated strings. The following steps show you how to achieve this.

Step 1: Flatten Nested JSON Data

Flattening nested JSON with dictionaries and arrays involves converting nested dictionaries into dot-notation keys and joining array values into a single string. The following helper function performs this flattening:

import json
from spire.xls import *

# ---------------------------
# Step 1: Flatten nested JSON
# ---------------------------
def flatten_json(item):
    flat = {}
    for key, value in item.items():
        if isinstance(value, dict):
            for sub_key, sub_value in value.items():
                flat[f"{key}.{sub_key}"] = sub_value
        elif isinstance(value, list):
            flat[key] = "; ".join(map(str, value))
        else:
            flat[key] = value
    return flat

with open('nested.json') as f:
    data = json.load(f)

flat_data = [flatten_json(item) for item in data]

Explanation:

• flatten_json() checks each field in the JSON object.
• If the value is a dictionary, its keys are prefixed with the parent key (dot notation).
• If the value is a list, all elements are joined into one string separated by semicolons.
• Other values are kept as they are.
• Finally, the original nested JSON is transformed into flat_data, which can now be exported using the same workflow as flat JSON.

Step 2: Export Flattened Data to CSV

After flattening, the export process is the same as flat JSON: create a workbook, generate headers, write rows, and save as CSV.

# ------------------------------------------------
# Step 2: Export flattened data to CSV
# (create workbook, write headers, populate rows, save)
# ------------------------------------------------
workbook = Workbook()
sheet = workbook.Worksheets[0]

# Generate headers from flattened JSON keys
headers = list(flat_data[0].keys())
for col, header in enumerate(headers, start=1):
    sheet.Range[1, col].Value = header

# Populate rows from flat_data
for row_index, item in enumerate(flat_data, start=2):
    for col_index, key in enumerate(headers, start=1):
        value = item.get(key, "")
        sheet.Range[row_index, col_index].Value = str(value) if value is not None else ""

# Save the worksheet as CSV (comma delimiter, UTF-8)
sheet.SaveToFile("output.csv", ",", Encoding.get_UTF8())

# Clean up resources
workbook.Dispose()

Explanation:

• Headers and rows are generated from flat_data (instead of the original data).
• This ensures nested fields (Contacts.Email, Contacts.Phone) and arrays (Skills) are properly included in the CSV.

Resulting CSV (output.csv):

Performance Tips for Exporting Large or Complex JSON Files to CSV

When working with large or complex JSON files, applying a few practical strategies can help optimize memory usage, improve performance, and ensure your CSV output remains clean and accurate.

• For very large datasets, process JSON in chunks to avoid memory issues.
• When arrays are very long, consider splitting into multiple rows instead of joining with semicolons.
• Maintain consistent JSON keys for cleaner CSV output.
• Test the CSV output in Excel to verify formatting, especially for dates and numeric values.

Conclusion

Converting JSON to CSV in Python is essential for developers and data engineers. With Spire.XLS for Python, you can:

• Convert flat and nested JSON into structured CSV.
• Maintain consistent headers and data formatting.
• Handle large datasets efficiently.
• Export directly to CSV or Excel without additional dependencies.

By following this guide, you can seamlessly transform JSON into CSV for reporting, analytics, and integration.

FAQs

Q1: Can Spire.XLS for Python convert nested JSON objects to CSV?

A1: Yes. You can flatten nested JSON objects, including dictionaries and arrays, and export them as readable CSV columns with dynamic headers. This ensures all hierarchical data is preserved in a tabular format.

Q2: How do I install Spire.XLS for Python?

A2: You can install it via pip with the command:

pip install spire.xls

Then import it into your Python script using:

from spire.xls import

This setup enables seamless JSON-to-CSV conversion in Python.

Q3: Can I join array elements from JSON into a single CSV cell?

A3: Yes. You can join array elements from JSON into a single CSV cell using a delimiter like ;. This keeps multiple values readable and consistent in your exported CSV file.

Q4: How can I optimize performance when converting large JSON files to CSV in Python?

A4: To handle large JSON files efficiently: flatten nested JSON before writing, process data row-by-row, and export in batches. This minimizes memory usage and ensures smooth CSV generation.

Q5: Can I customize the CSV delimiter or column order when exporting JSON to CSV?

A5: Yes. Spire.XLS allows you to set custom delimiters (e.g., comma, semicolon, or tab) and manually define the column order, giving you full control over professional CSV output.