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": "[email protected]", "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": "[email protected]", "Phone": "123-456"},
        "Skills": ["Python", "Excel", "SQL"]
    },
    {
        "Name": "Bob",
        "Age": 34,
        "City": "Los Angeles",
        "Contacts": {"Email": "[email protected]", "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.

Converting HTML to Markdown using Python is a common task for developers managing web content, documentation, or API data. While HTML provides powerful formatting and structure, it can be verbose and harder to maintain for tasks like technical writing or static site generation. Markdown, by contrast, is lightweight, human-readable, and compatible with platforms such as GitHub, GitLab, Jekyll, and Hugo.

Automating HTML to Markdown conversion with Python streamlines workflows, reduces errors, and ensures consistent output. This guide covers everything from converting HTML files and strings to batch processing multiple files, along with best practices to ensure accurate Markdown results.

What You Will Learn

• Why Convert HTML to Markdown
• Install HTML to Markdown Library for Python
• Convert an HTML File to Markdown in Python
• Convert an HTML String to Markdown in Python
• Batch Conversion of Multiple HTML Files
• Best Practices for HTML to Markdown Conversion
• Conclusion
• FAQs

Why Convert HTML to Markdown?

Before diving into the code, let’s look at why developers often prefer Markdown over raw HTML in many workflows:

• Simplicity and Readability
  Markdown is easier to read and edit than verbose HTML tags.
• Portability Across Tools
  Markdown is supported by GitHub, GitLab, Bitbucket, Obsidian, Notion, and static site generators like Hugo and Jekyll.
• Better for Version Control
  Being plain text, Markdown makes it easier to track changes with Git, review diffs, and collaborate.
• Faster Content Creation
  Writing Markdown is quicker than remembering HTML tag structures.
• Integration with Static Site Generators
  Popular frameworks rely on Markdown as the main content format. Converting from HTML ensures smooth migration.
• Cleaner Documentation Workflows
  Many documentation systems and wikis use Markdown as their primary format.

In short, converting HTML to Markdown improves maintainability, reduces clutter, and fits seamlessly into modern developer workflows.

Install HTML to Markdown Library for Python

Before converting HTML content to Markdown in Python, you’ll need a library that can handle both formats effectively. Spire.Doc for Python is a reliable choice that allows you to transform HTML files or HTML strings into Markdown while keeping headings, lists, images, and links intact.

You can install it from PyPI using pip:

pip install spire.doc

Once installed, you can automate the HTML to Markdown conversion in your Python scripts. The same library also supports broader scenarios. For example, when you need editable documents, you can rely on its HTML to Word conversion feature to transform web pages into Word files. And for distribution or archiving, HTML to PDF conversion is especially useful for generating standardized, platform-independent documents.

Convert an HTML File to Markdown in Python

One of the most common use cases is converting an existing .html file into a .md file. This is especially useful when migrating old websites, technical documentation, or blog posts into Markdown-based workflows, such as static site generators (Jekyll, Hugo) or Git-based documentation platforms (GitHub, GitLab, Read the Docs).

Steps

• Create a new Document instance.
• Load the .html file into the document using LoadFromFile().
• Save the document as a .md file using SaveToFile() with FileFormat.Markdown.
• Close the document to release resources.

Code Example

from spire.doc import *

# Create a Document instance
doc = Document()

# Load an existing HTML file
doc.LoadFromFile("input.html", FileFormat.Html)

# Save as Markdown file
doc.SaveToFile("output.md", FileFormat.Markdown)

# Close the document
doc.Close()

This converts input.html into output.md, preserving structural elements such as headings, paragraphs, lists, links, and images.

If you’re also interested in the reverse process, check out our guide on converting Markdown to HTML in Python.

Convert an HTML String to Markdown in Python

Sometimes, HTML content is not stored in a file but is dynamically generated—for example, when retrieving web content from an API or scraping. In these scenarios, you can convert directly from a string without needing to create a temporary HTML file.

Steps

• Create a new Document instance.
• Add a Section to the document.
• Add a Paragraph to the section.
• Append the HTML string to the paragraph using AppendHTML().
• Save the document as a Markdown file using SaveToFile().
• Close the document to release resources.

Code Example

from spire.doc import *

# Sample HTML string
html_content = """
<h1>Welcome</h1>
<p>This is a <strong>sample</strong> paragraph with <em>emphasis</em>.</p>
<ul>
  <li>First item</li>
  <li>Second item</li>
</ul>
"""

# Create a Document instance
doc = Document()

# Add a section
section = doc.AddSection()

# Add a paragraph and append the HTML string
paragraph = section.AddParagraph()
paragraph.AppendHTML(html_content)

# Save the document as Markdown
doc.SaveToFile("string_output.md", FileFormat.Markdown)

# close the document to release resources
doc.Close()

The resulting Markdown will look like this:

Batch Conversion of Multiple HTML Files

For larger projects, you may need to convert multiple .html files in bulk. A simple loop can automate the process.

import os
from spire.doc import *

# Define the folder containing HTML files to convert
input_folder = "html_files"

# Define the folder where converted Markdown files will be saved
output_folder = "markdown_files"

# Create the output folder if it doesn't exist
os.makedirs(output_folder, exist_ok=True)

# Loop through all files in the input folder
for filename in os.listdir(input_folder):
    # Process only files with .html extension
    if filename.endswith(".html"):
        # Create a new Document object
        doc = Document()

        # Load the HTML file into the Document object
        doc.LoadFromFile(os.path.join(input_folder, filename), FileFormat.Html)

        # Generate the output file path by replacing .html with .md
        output_file = os.path.join(output_folder, filename.replace(".html", ".md"))

        # Save the Document as a Markdown file
        doc.SaveToFile(output_file, FileFormat.Markdown)

        # Close the Document to release resources
        doc.Close()

This script processes all .html files inside html_files/ and saves the Markdown results into markdown_files/.

Best Practices for HTML to Markdown Conversion

Turning HTML to Markdown makes content easier to read, manage, and version-control. To ensure accurate and clean conversion, follow these best practices:

• Validate HTML Before Conversion
  Ensure your HTML is properly structured. Invalid tags can cause incomplete or broken Markdown output.
• Understand Markdown Limitations
  Markdown does not support advanced CSS styling or custom HTML tags. Some formatting might get lost.
• Choose File Encoding Carefully
  Always be aware of character encoding. Open and save your files with a specific encoding (like UTF-8) to prevent issues with special characters.
• Batch Processing

If converting multiple files, create a robust script that includes error handling (try-except blocks), logging, and skips problematic files instead of halting the entire process.

Conclusion

Converting HTML to Markdown in Python is a valuable skill for developers handling documentation pipelines, migrating web content, or processing data from APIs. With Spire.Doc for Python, you can:

• Convert individual HTML files into Markdown with ease.
• Transform HTML strings directly into .md files.
• Automate batch conversions to efficiently manage large projects.

By applying these methods, you can streamline your workflows and ensure your content remains clean, maintainable, and ready for modern publishing platforms.

FAQs

Q1: Can I convert Markdown back to HTML in Python?

A1: Yes, Spire.Doc supports the conversion of Markdown to HTML, allowing for seamless transitions between these formats.

Q2: Will the conversion preserve complex HTML elements like tables?

A2: While Spire.Doc effectively handles standard HTML elements, it's advisable to review complex layouts, such as tables and nested elements, to ensure accurate conversion results.

Q3: Can I automate batch conversion for multiple HTML files?

A3: Absolutely! You can automate batch conversion using scripts in Python, enabling efficient processing of multiple HTML files at once.

Q4: Is Spire.Doc free to use?

A4: Spire.Doc provides both free and commercial versions, giving developers the flexibility to access essential features at no cost or unlock advanced functionality with a license.

Markdown (.md) is widely used in web development, documentation, and technical writing. Its simple syntax makes content easy to write and read. However, web browsers do not render Markdown directly. Converting Markdown to HTML ensures your content is structured, readable, and compatible with web platforms.

In this step-by-step guide, you will learn how to efficiently convert Markdown (.md) files into HTML using Python and Spire.Doc for Python, complete with practical code examples, clear instructions, and best practices for both single-file and batch conversions.

Table of Contents

• What is Markdown
• Why Convert Markdown to HTML
• Introducing Spire.Doc for Python
• Step-by-Step Guide: Converting Markdown to HTML in Python
• Automating Batch Conversion
• Best Practices for Markdown to HTML Conversion
• Conclusion
• FAQs

What is Markdown?

Markdown is a lightweight markup language designed for readability and ease of writing. Unlike HTML, which can be verbose and harder to write by hand, Markdown uses simple syntax to indicate headings, lists, links, images, and more.

Example Markdown:

# This is a Heading

This is a paragraph with \*\*bold text\*\* and \*italic text\*.

- Item 1

- Item 2

Even in its raw form, Markdown is easy to read, which makes it popular for documentation, blogging, README files, and technical writing.

For more on Markdown syntax, see the Markdown Guide.

Why Convert Markdown to HTML?

While Markdown is excellent for authoring content, web browsers cannot render it natively. Converting Markdown to HTML allows you to:

• Publish content on websites – Most CMS platforms require HTML for web pages.
• Enhance styling – HTML supports CSS and JavaScript for advanced formatting and interactivity.
• Maintain compatibility – HTML is universally supported by browsers, ensuring content displays correctly everywhere.
• Integrate with web frameworks – Frameworks like React, Vue, and Angular require HTML as the base for rendering components.

Introducing Spire.Doc for Python

Spire.Doc for Python is a robust library for handling multiple document formats. It supports reading and writing Word documents, Markdown files, and exporting content to HTML. The library allows developers to convert Markdown directly to HTML with minimal code, preserving proper formatting and structure.

In addition to HTML, Spire.Doc for Python also allows you to convert Markdown to Word in Python or convert Markdown to PDF in Python, making it particularly useful for developers who want a unified tool for handling Markdown across different output formats.

Benefits of Using Spire.Doc for Python for Markdown to HTML Conversion

• Easy-to-use API – Simple, intuitive methods that reduce development effort.
• Accurate formatting – Preserves all Markdown elements such as headings, lists, links, and emphasis in HTML.
• No extra dependencies – Eliminates the need for manual parsing or third-party libraries.
• Flexible usage – Supports both single-file conversion and automated batch processing.

Step-by-Step Guide: Converting Markdown to HTML in Python

Now that you understand the purpose and benefits of converting Markdown to HTML, let’s walk through a clear, step-by-step process to transform your Markdown files into structured, web-ready HTML.

Step 1: Install Spire.Doc for Python

First, ensure that Spire.Doc for Python is installed in your environment. You can install it directly from PyPI using pip:

pip install spire.doc

Step 2: Prepare Your Markdown File

Next, create a sample Markdown file that you want to convert. For example, example.md:

Step 3: Write the Python Script

Now, write a Python script that loads the Markdown file and converts it to HTML:

from spire.doc import *

# Create a Document object
doc = Document()

# Load the Markdown file
doc.LoadFromFile("example.md", FileFormat.Markdown)

# Save the document as HTML
doc.SaveToFile("example.html", FileFormat.Html)

# Close the document
doc.Close()

Explanation of the code:

• Document() initializes a new document object.
• LoadFromFile("example.md", FileFormat.Markdown) loads the Markdown file into memory.
• SaveToFile("example.html", FileFormat.Html) converts the loaded content into HTML and saves it to disk.
• doc.Close() ensures resources are released properly, which is particularly important when processing multiple files or running batch operations.

Step 4: Verify the HTML Output

Finally, open the generated example.html file in a web browser or HTML editor. Verify that the Markdown content has been correctly converted.

Automating Batch Conversion

You can convert multiple Markdown files in a folder automatically:

import os
from spire.doc import *

# Set the folder containing Markdown files
input_folder = "markdown_files"

# Set the folder where HTML files will be saved
output_folder = "html_files"

# Create the output folder if it doesn't exist
os.makedirs(output_folder, exist_ok=True)

# Loop through all files in the input folder
for filename in os.listdir(input_folder):
    # Process only Markdown files
    if filename.endswith(".md"):
        # Create a new Document object for each file
        doc = Document()

        # Load the Markdown file into the Document object
        doc.LoadFromFile(os.path.join(input_folder, filename), FileFormat.Markdown)

        # Construct the output file path by replacing .md extension with .html
        output_file = os.path.join(output_folder, filename.replace(".md", ".html"))

        # Save the loaded Markdown content as HTML
        doc.SaveToFile(output_file, FileFormat.Html)

        # Close the document to release resources
        doc.Close()

This approach allows you to process multiple Markdown files efficiently and generate corresponding HTML files automatically.

Best Practices for Markdown to HTML Conversion

While the basic steps are enough to complete a Markdown-to-HTML conversion, following a few best practices will help you avoid common pitfalls, improve compatibility, and ensure your output is both clean and professional:

• Use proper Markdown syntax – Ensure headings, lists, links, and emphasis are correctly written.
• Use UTF-8 Encoding: Always save your Markdown files in UTF-8 encoding to avoid issues with special characters or non-English text.
• Batch Processing: If you need to convert multiple files, wrap your script in a loop and process entire folders. This saves time and ensures consistent formatting across documents.
• Enhance Styling: Remember that HTML gives you the flexibility to add CSS and JavaScript for custom layouts, responsive design, and interactivity—something not possible in raw Markdown.

Conclusion

Converting Markdown to HTML using Python with Spire.Doc is simple, reliable, and efficient. It preserves formatting, supports automation, and produces clean HTML output ready for web use. By following this guide, you can implement a smooth Markdown to HTML workflow for both single documents and batch operations.

FAQs

Q1: Can I convert multiple Markdown files to HTML in Python?

A1: Yes, you can automate batch conversions by iterating through Markdown files in a directory and applying the conversion logic to each file.

Q2: Will the HTML preserve all Markdown formatting?

A2: Yes, Spire.Doc effectively preserves all essential Markdown formatting, including headings, lists, bold and italic text, links, and more.

Q3: Is there a way to handle images in Markdown during conversion?

A3: Yes, Spire.Doc supports the conversion of images embedded in Markdown, ensuring they are included in the resulting HTML.

Q4: Do I need additional libraries besides Spire.Doc?

A4: No additional libraries are required. Spire.Doc for Python provides a comprehensive solution for converting Markdown to HTML without any external dependencies.

Q5: Can I use the generated HTML in web frameworks?

A5: Yes, the HTML produced is fully compatible with popular web frameworks such as React, Vue, and Angular, making integration seamless.

HTML (HyperText Markup Language) is a markup language used to create web pages, allowing developers to build rich and visually appealing layouts. However, HTML files often contain a large number of tags, which makes them difficult to read if you only need the main content. By using Python to convert HTML to text, this problem can be easily solved. Unlike raw HTML, the converted text file strips away all unnecessary markup, leaving only clean and readable content that is easier to store, analyze, or process further.

• Install HTML to Text Converter in Python
• Python Convert HTML File to Text
• Python Convert HTML String to Text
• The Conclusion
• FAQs

Install HTML to Text Converter in Python

To simplify the task, we recommend using Spire.Doc for Python. This Python Word library allows you to quickly remove HTML markup and extract clean plain text with ease. It not only works as an HTML-to-text converter, but also offers a wide range of features—covering almost everything you can do in Microsoft Word.

To install it, you can run the following pip command:

pip install spire.doc

Alternatively, you can download the Spire.Doc package and install it manually.

Python Convert HTML Files to Text in 3 Steps

After preparing the necessary tools, let's dive into today's main topic: how to convert HTML to plain text using Python. With the help of Spire.Doc, this task can be accomplished in just three simple steps: create a new document object, load the HTML file, and save it as a text file. It’s straightforward and efficient, even for beginners. Let’s take a closer look at how this process can be implemented in code!

Code Example – Converting an HTML File to a Text File:

from spire.doc import *
from spire.doc.common import *

# Open an html file
document = Document()
document.LoadFromFile("/input/htmlsample.html", FileFormat.Html, XHTMLValidationType.none)
# Save it as a Text document.
document.SaveToFile("/output/HtmlFileTotext.txt", FileFormat.Txt)

document.Close()

The following is a preview comparison between the source document (.html) and the output document (.txt):

Note that if the HTML file contains tables, the output text file will only retain the values within the tables and cannot preserve the original table formatting. If you want to keep certain styles while removing markup, it is recommended to convert HTML to a Word document . This way, you can retain headings, tables, and other formatting, making the content easier to edit and use.

How to Convert an HTML String to Text in Python

Sometimes, we don’t need the entire content of a web page and only want to extract specific parts. In such cases, you can convert an HTML string directly to text. This approach allows you to precisely control the information you need without further editing. Using Python to convert an HTML string to a text file is also straightforward. Here’s a detailed step-by-step guide:

Steps to convert an HTML string to a text document using Spire.Doc:

• Input the HTML string directly or read it from a local file.
• Create a Document object and add sections and paragraphs.
• Use Paragraph.AppendHTML() method to insert the HTML string into a paragraph.
• Save the document as a .txt file using Document.SaveToFile() method.

The following code demonstrates how to convert an HTML string to a text file using Python:

from spire.doc import *
from spire.doc.common import *

#Get html string.
#with open(inputFile) as fp:
    #HTML = fp.read()

# Load HTML from string
html = """<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>HTML to Text Example</title>
  <style>
    body { font-family: Arial, sans-serif; margin: 20px; }
    header { background: #f4f4f4; padding: 10px; }
    nav a { margin: 0 10px; text-decoration: none; color: #333; }
    main { margin-top: 20px; }
  </style>
</head>
<body>
  <header>
    <h1>My Demo Page</h1>
    <nav>
      <a href="#">Home</a>
      <a href="#">About</a>
      <a href="#">Contact</a>
    </nav>
  </header>
  
  <main>
    <h2>Convert HTML to Text</h2>
    <p>This is a simple demo showing how HTML content can be displayed before converting it to plain text.</p>
  </main>
</body>
</html>
"""

# Create a new document
document = Document()
section = document.AddSection()
section.AddParagraph().AppendHTML(html)

# Save directly as TXT
document.SaveToFile("/output/HtmlStringTotext.txt", FileFormat.Txt)
document.Close()

Here's the preview of the converted .txt file:

The Conclusion

In today’s tutorial, we focused on how to use Python to convert HTML to a text file. With the help of Spire.Doc, you can handle both HTML files and HTML strings in just a few lines of code, easily generating clean plain text files. If you’re interested in the other powerful features of the Python Word library, you can request a 30-day free trial license and explore its full capabilities for yourself.

FAQs about Converting HTML to Text in Python

Q1: How can I convert HTML to plain text using Python?

A: Use Spire.Doc to load an HTML file or string, insert it into a Document object with AppendHTML(), and save it as a .txt file.

Q2: Can I keep some formatting when converting HTML to text?

A: To retain styles like headings or tables, convert HTML to a Word document first, then export to text if needed.

Q3: Is it possible to convert only part of an HTML page to text?

A: Yes, extract the specific HTML segment as a string and convert it to text using Python for precise control.