When working with data in Python, converting TXT files to CSV is a common and essential task for data analysis, reporting, or sharing data between applications. TXT files often store unstructured plain text, which can be difficult to process, while CSV files organize data into rows and columns, making it easier to work with and prepare for analysis. This tutorial explains how to convert TXT to CSV in Python efficiently, covering single-file conversion, batch conversion, and tips for handling different delimiters.

Table of Contents

• What is a CSV File
• Python TXT to CSV Library - Installation
• Convert a TXT File to CSV in Python (Step-by-Step)
• Automate Batch Conversion of Multiple TXT Files
• Advanced Tips for Python TXT to CSV Conversion
• Conclusion
• FAQs: Python Text to CSV

What is a CSV File?

A CSV (Comma-Separated Values) file is a simple text-based file format used to store tabular data. Each line in a CSV file represents a row, and values within the row are separated by commas (or another delimiter such as tabs or semicolons).

CSV is widely supported by spreadsheet applications, databases, and programming languages like Python. Its simple format makes it easy to import, export, and use across platforms such as Excel, Google Sheets, R, and SQL for data analysis and automation.

An Example CSV File:

Name, Age, City

John, 28, New York

Alice, 34, Los Angeles

Bob, 25, Chicago

Python TXT to CSV Library - Installation

To perform TXT to CSV conversion in Python, we will use Spire.XLS for Python, a powerful library for creating and manipulating Excel and CSV files, without requiring Microsoft Excel to be installed.

You can install it directly from PyPI with the following command:

pip install Spire.XLS

If you need instructions for the installation, visit the guide on How to Install Spire.XLS for Python.

Convert a TXT File to CSV in Python (Step-by-Step)

Converting a text file to CSV in Python is straightforward. You can complete the task in just a few steps. Below is a basic outline of the process:

• Prepare and read the text file: Load your TXT file and read its content line by line.
• Split the text data: Separate each line into fields using a specific delimiter such as a space, tab, or comma.
• Write data to CSV: Use Spire.XLS to write the processed data into a new CSV file.
• Verify the output: Check the CSV in Excel, Google Sheets, or a text editor.

The following code demonstrates how to export a TXT file to CSV using Python:

from spire.xls import *

# Read the txt file
with open("data.txt", "r", encoding="utf-8") as file:
    lines = file.readlines()

# Process each line by splitting based on spaces (you can change the delimiter if needed)
processed_data = [line.strip().split() for line in lines]

# Create an Excel workbook
workbook = Workbook()
# Get the first worksheet
sheet = workbook.Worksheets[0]

# Write data from the processed list to the worksheet
for row_num, row_data in enumerate(processed_data):
    for col_num, cell_data in enumerate(row_data):
        # Write data into cells
        sheet.Range[row_num + 1, col_num + 1].Value = cell_data

# Save the sheet as CSV file (UTF-8 encoded)
sheet.SaveToFile("TxtToCsv.csv", ",", Encoding.get_UTF8())
# Dispose the workbook to release resources
workbook.Dispose()

TXT to CSV Output:

If you are also interested in converting a TXT file to Excel, see the guide on converting TXT to Excel in Python.

Automate Batch Conversion of Multiple TXT Files

If you have multiple text files that you want to convert to CSV automatically, you can loop through all .txt files in a folder and convert them one by one.

The following code demonstrates how to batch convert multiple TXT files to CSV in Python:

import os
from spire.xls import *

# Folder containing TXT files
input_folder = "txt_files"
output_folder = "csv_files"

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

# Function to process a single TXT file
def convert_txt_to_csv(file_path, output_path):
    # Read the TXT file
    with open(file_path, "r", encoding="utf-8") as f:
        lines = f.readlines()
    
    # Process each line (split by space, modify if your delimiter is different)
    processed_data = [line.strip().split() for line in lines if line.strip()]
    
    # Create workbook and access the first worksheet
    workbook = Workbook()
    sheet = workbook.Worksheets[0]
    
    # Write processed data into the sheet
    for row_num, row_data in enumerate(processed_data):
        for col_num, cell_data in enumerate(row_data):
            sheet.Range[row_num + 1, col_num + 1].Value = cell_data
    
    # Save the sheet as CSV with UTF-8 encoding
    sheet.SaveToFile(output_path, ",", Encoding.get_UTF8())
    workbook.Dispose()
    print(f"Converted '{file_path}' -> '{output_path}'")

# Loop through all TXT files in the folder and convert each to a CSV file with the same file name
for filename in os.listdir(input_folder):
    if filename.lower().endswith(".txt"):
        input_path = os.path.join(input_folder, filename)
        output_name = os.path.splitext(filename)[0] + ".csv"
        output_path = os.path.join(output_folder, output_name)
        
        convert_txt_to_csv(input_path, output_path)

Advanced Tips for Python TXT to CSV Conversion

Converting text files to CSV can involve variations in text file layout and potential errors, so these tips will help you handle different scenarios more effectively.

1. Handle Different Delimiters

Not all text files use spaces to separate values. If your TXT file uses tabs, commas, or other characters, you can adjust the split() function to match the delimiter.

• For tab-separated files (.tsv):

processed_data = [line.strip().split('\t') for line in lines]

• For comma-separated files:

processed_data = [line.strip().split(',') for line in lines]

• For custom delimiters (e.g., |):

processed_data = [line.strip().split('|') for line in lines]

This ensures that your data is correctly split into columns before writing to CSV.

2. Add Error Handling

When reading or writing files, it's a good practice to use try-except blocks to catch potential errors. This makes your script more robust and prevents unexpected crashes.

try:
    # your code here
except Exception as e:
print("Error:", e)

Tip: Use descriptive error messages to help understand the problem.

1. Skip Empty Lines
   Sometimes, text files may have empty lines. You can filter out the blank lines to avoid creating empty rows in CSV:

processed_data = [line.strip().split() for line in lines if line.strip()]

Conclusion

In this article, you learned how to convert a TXT file to CSV format in Python using Spire.XLS for Python. This conversion is an essential step in data preparation, helping organize raw text into a structured format suitable for analysis, reporting, and sharing. With Spire.XLS for Python, you can automate the text to CSV conversion, handle different delimiters, and efficiently manage multiple text files.

If you have any questions or need technical assistance about Python TXT to CSV conversion, visit our Support Forum for help.

FAQs: Python Text to CSV

Q1: Can I convert TXT files to CSV without Microsoft Excel installed?

A1: Yes. Spire.XLS for Python works independently of Microsoft Excel, allowing you to create and export CSV files directly.

Q2: How to batch convert multiple TXT files to CSV in Python?

A2: Use a loop to read all TXT files in a folder and apply the conversion function for each. The tutorial includes a ready-to-use Python example for batch conversion.

Q3: How do I handle empty lines or inconsistent rows in TXT files when converting to CSV?

A3: Filter out empty lines during processing and implement checks for consistent column counts to avoid errors or blank rows in the output CSV.

Q4: How do I convert TXT files with tabs or custom delimiters to CSV in Python?

A4: You can adjust the split() function in your Python script to match the delimiter in your TXT file-tabs (\t), commas, or custom characters-before writing to CSV.

PDF (Portable Document Format) is widely used for sharing, distributing, and preserving documents because it maintains a consistent layout and formatting across platforms. Developers often need to edit PDF files in C#, whether it's to replace text, insert images, add watermarks, or extract pages.

In this step-by-step tutorial, you will learn how to programmatically edit PDFs in C# with the Spire.PDF for .NET library.

Table of Contents

• Why Edit PDFs Programmatically in C#
• C# Library to Edit PDFs
• Step-by-Step Guide: Editing PDF in C#
  • Step 1: Set Up Your C# Project
  • Step 2: Install Spire.PDF
  • Step 3: Load an Existing PDF
  • Step 4: Edit PDF Content
    • 4.1 Edit Text
    • 4.2 Insert and Update Images
    • 4.3 Add, Remove, or Extract Pages
    • 4.4 Add Watermarks
  • Step 5: Save the Modified PDF
• Tips for Efficient PDF Editing in C#
• Conclusion
• FAQs

Why Edit PDFs Programmatically in C

While tools like Adobe Acrobat provide manual PDF editing, programmatically editing PDFs has significant advantages:

• Automation: Batch process hundreds of documents without human intervention.
• Integration: Edit PDFs as part of a workflow, such as generating reports, invoices, or certificates dynamically.
• Consistency: Apply uniform styling, stamps, or watermarks across multiple PDFs.
• Flexibility: Extract or replace content programmatically to integrate with databases or external data sources.

C# Library to Edit PDFs

Spire.PDF for .NET is a robust .NET PDF library that enables developers to generate, read, edit, and convert PDF files in .NET applications. It's compatible with both .NET Framework and .NET Core applications.

This library provides a rich set of features for developers working with PDFs:

• PDF Creation: Generate new PDFs from scratch or from existing documents.
• Text Editing: Add, replace, or delete text on any page.
• Image Editing: Insert images, resize, or remove them.
• Page Operations: Insert, remove, extract, or reorder pages.
• Annotations: Add stamps, comments, and shapes for marking content.
• Watermarking: Add text or image watermarks for branding or security.
• Form Handling: Create and fill PDF forms programmatically.
• Digital Signatures: Add and validate signatures for authenticity.
• Encryption: Apply password protection and user permissions.

Step-by-Step Guide: Editing PDF in C

Modifying a PDF file in C# involves several steps: setting up a C# project, installing the library, loading the PDF file, making necessary changes, and saving the document. Let's break down each step in detail.

Step 1: Set Up Your C# Project

Before you start editing PDFs, you need to create a new C# project by following the steps below:

• Open Visual Studio.
• Create a new project. You can choose a Console App or a Windows Forms App depending on your use case.
• Name your project (e.g., PdfEditorDemo) and click Create.

Step 2: Install Spire.PDF

Next, you need to install the Spire.PDF library, which provides all the functionality required to read, edit, and save PDF files programmatically.

You can simply install it via the NuGet Package Manager Console with the following command:

Install-Package Spire.PDF

Alternatively, you can use the NuGet Package Manager GUI to search for Spire.PDF and click Install.

Step 3: Load an Existing PDF

Before you can modify an existing PDF file, you need to load it into a PdfDocument object. This gives you access to its pages, text, images, and structure.

using Spire.Pdf;
PdfDocument pdf = new PdfDocument();
pdf.LoadFromFile("example.pdf");

Step 4: Edit PDF Content

Text editing, image insertion, page management, and watermarking are common operations when working with PDFs. This step covers all these editing tasks.

4.1 Edit Text

Text editing is one of the most common operations when working with PDFs. Depending on your needs, you might want to replace existing text or add new text to specific pages.

Replace existing text:

Replacing text in PDF allows you to update content across a single page or an entire PDF while maintaining formatting consistency. Using the PdfTextReplacer class, you can quickly find and replace text programmatically:

// Get the first page
PdfPageBase page = pdf.Pages[0];
// Create a PdfTextReplacer
PdfTextReplacer textReplacer = new PdfTextReplacer(page);

// Replace all occurrences of target text with new text
textReplacer.ReplaceAllText("Old text", "New text");

Add new text:

In addition to replacing existing content, you may need to insert new text into a PDF. With just one line of code, you can add text to any location on a PDF page:

page.Canvas.DrawString(
    "Hello, World!",
    new PdfTrueTypeFont(new Font("Arial Unicode MS", 15f, FontStyle.Bold), true),
    new PdfSolidBrush(Color.Black),
    90, 30
);

4.2 Insert and Update Images

PDFs often contain visual elements such as logos, charts, or illustrations. You can insert new images or update outdated graphics to enhance the document's visual appeal.

Insert an Image:

// Load an image
PdfImage image = PdfImage.FromFile("logo.png");
// Draw the image at a specific location with defined size
page.Canvas.DrawImage(image, 100, 150, 200, 100);

Update an image:

// Load the new image
PdfImage newImage = PdfImage.FromFile("image1.jpg");
// Create a PdfImageHelper instance
PdfImageHelper imageHelper = new PdfImageHelper();
// Get the image information from the page
PdfImageInfo[] imageInfo = imageHelper.GetImagesInfo(page);
// Replace the first image on the page with the new image
imageHelper.ReplaceImage(imageInfo[0], newImage);

4.3 Add, Remove, or Extract Pages

Managing page structure is another important aspect of PDF editing, such as adding new pages, removing unwanted pages, and extracting particular pages to a new document.

Add a new page:

// Add a new page
PdfPageBase newPage = pdf.Pages.Add();

Remove a page:

// Remove the last page
pdf.Pages.RemoveAt(pdf.Pages.Count - 1);

Extract a page to a new document:

// Create a new PDF document
PdfDocument newPdf = new PdfDocument();
// Extract the third page to a new PDF document
newPdf.InsertPage(pdf, pdf.Pages[2]);
// Save the new PDF document
newPdf.SaveToFile("extracted_page.pdf");

4.4 Add Watermarks

Adding Watermarks to PDFs can help indicate confidentiality, add branding, or protect intellectual property. You can easily add them programmatically to any page:

// Iterate through each page in the PDF document
foreach (PdfPageBase page in pdf.Pages)
{
    // Create a tiling brush for the watermark
    // The brush size is set to half the page width and one-third of the page height
    PdfTilingBrush brush = new PdfTilingBrush(
        new SizeF(page.Canvas.ClientSize.Width / 2, page.Canvas.ClientSize.Height / 3));

    // Set the brush transparency to 0.3 for a semi-transparent watermark
    brush.Graphics.SetTransparency(0.3f);

    // Save the current graphics state for later restoration
    brush.Graphics.Save();

    // Move the origin of the brush to its center to prepare for rotation
    brush.Graphics.TranslateTransform(brush.Size.Width / 2, brush.Size.Height / 2);

    // Rotate the coordinate system by -45 degrees to angle the watermark
    brush.Graphics.RotateTransform(-45);

    // Draw the watermark text on the brush
    // Using Helvetica font, size 24, violet color, centered alignment
    brush.Graphics.DrawString(
        "DO NOT COPY",
        new PdfFont(PdfFontFamily.Helvetica, 24),
        PdfBrushes.Violet,
        0, 0,
        new PdfStringFormat(PdfTextAlignment.Center));

    // Restore the previously saved graphics state, undoing rotation and translation
    brush.Graphics.Restore();

    // Reset the transparency to fully opaque
    brush.Graphics.SetTransparency(1);

    // Draw the brush over the entire page area to apply the watermark
    page.Canvas.DrawRectangle(brush, new RectangleF(new PointF(0, 0), page.Canvas.ClientSize));
}

Step 5: Save the Modified PDF

After making all the necessary edits, the final step is to save your changes.

// Save the Modified PDF and release resources
pdf.SaveToFile("modified.pdf");
pdf.Close();

Output PDF

The output modified.pdf looks like this:

Tips for Efficient PDF Editing in C

When editing PDFs programmatically, it's important to keep a few best practices in mind to ensure the output remains accurate, readable, and efficient.

• Batch Processing: For repetitive tasks, process multiple PDF files in a loop rather than handling them individually. This approach improves efficiency and reduces manual effort.
• Text Placement: Use coordinates carefully when inserting new text. Proper positioning prevents content from overlapping with existing elements and maintains a clean layout.
• Fonts and Encoding: Choose fonts that support the characters you need. This is especially critical for languages such as Chinese, Arabic, or other scripts that require extended font support.
• Memory Management: Always release resources by disposing of PdfDocument objects after use. Proper memory management helps avoid performance issues in larger applications.

Conclusion

This tutorial demonstrates how to edit PDF in C# using Spire.PDF. From replacing text, inserting images, and managing pages, to adding watermarks, each step includes practical code examples. Developers can now automate PDF editing, enhance document presentation, and handle PDFs efficiently within professional applications.

FAQs

Q1: How can I programmatically edit text in a PDF using C#?

A1: You can use a C# PDF library like Spire.PDF to replace existing text or add new text to a PDF. Classes such as PdfTextReplacer and page.Canvas.DrawString() provide precise control over text editing while preserving formatting.

Q2: How do I replace or add text in a PDF using C#?

A2: With C#, libraries like Spire.PDF let you search and replace existing text using PdfTextReplacer or add new text anywhere on a page using page.Canvas.DrawString().

Q3: Can I insert or update images in a PDF programmatically?

A3: Yes. You can load images into your project and use classes like PdfImage and PdfImageHelper to draw or replace images on a PDF page.

Q4: Is it possible to add watermarks to a PDF using code?

A4: Absolutely. You can add text or image watermarks programmatically, control transparency, rotation, and position, and apply them to one or all pages of a PDF.

Q5: How can I extract specific pages from a PDF?

A5: You can create a new PDF document and insert selected pages from the original PDF, enabling you to extract single pages or ranges for separate use.

In today's data-driven world, Python developers frequently need to convert lists (a fundamental Python data structure) into Excel spreadsheets. Excel remains the standard for data presentation, reporting, and sharing across industries. Whether you're generating reports, preparing data for analysis, or sharing information with non-technical stakeholders, the ability to efficiently export Python lists to Excel is a valuable skill.

While lightweight libraries like pandas can handle basic exports, Spire.XLS for Python gives you full control over Excel formatting, styles, and file generation – all without requiring Microsoft Excel. In this comprehensive guide, we'll explore how to use the library to convert diverse list structures into Excel in Python, complete with detailed examples and best practices.

• Why Convert Python Lists to Excel?
• Installation Guide
• Basic – Convert a Simple Python List to Excel
• Convert Nested Lists to Excel in Python
• Convert a List of Dictionaries to Excel
• 4 Tips to Optimize Your Excel Outputs
• Conclusion
• FAQs

Why Convert Python Lists to Excel?​

Lists in Python are versatile for storing structured or unstructured data, but Excel offers advantages in:

• Collaboration: Excel is universally used, and stakeholders can edit, sort, or filter data without Python knowledge.​
• Reporting: Add charts, pivot tables, or summaries to Excel after export.​
• Compliance: Many industries require data in Excel for audits or record-keeping.​
• Visualization: Excel’s formatting tools (colors, borders, headers) make data easier to read than raw Python lists.

Whether you’re working with sales data, user records, or survey results, writing lists to Excel in Python ensures your data is accessible and professional.

Installation Guide

To get started with Spire.XLS for Python, install it using pip:

pip install Spire.XLS

The Python Excel library supports Excel formats like .xls or .xlsx and lets you customize formatting (bold headers, column widths, colors), perfect for production-ready files.

To fully experience the capabilities of Spire.XLS for Python, you can request a free 30-day trial license here.

Basic – Convert a Simple Python List to Excel

For a basic one-dimensional list, iterate through the items and write them to consecutive cells in a single column.

This code example converts a list of text strings into a single column. If you need to convert a list of numeric values, you can set their number format before saving.

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

# Create a Workbook object
workbook = Workbook()

# Clear the default sheets
workbook.Worksheets.Clear()

# Add a new worksheet
worksheet = workbook.Worksheets.Add("Simple List")

# Sample list
data_list = ["Alexander", "Bob", "Charlie", "Diana", "Eve"]

# Write list data to Excel cells (starting from row 1, column 1)
for index, value in enumerate(data_list):
    worksheet.Range[index + 1, 1].Value = value

# Set column width for better readability
worksheet.Range[1, 1].ColumnWidth = 15

# Save the workbook
workbook.SaveToFile("SimpleListToExcel.xlsx", ExcelVersion.Version2016)
workbook.Dispose()

If you need to write the list in a single row, use the following:

for index, value in enumerate(data_list):
    worksheet.Range[1, index + 1].Value = value

Output: A clean Excel file with one column of names, properly spaced.

Convert Nested Lists to Excel in Python

Nested lists (2D Lists) represent tabular data with rows and columns, making them perfect for direct conversion to Excel tables. Let’s convert a nested list of employee data (name, age, department) to an Excel table.

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

# Create a Workbook object
workbook = Workbook()

# Clear the default sheets
workbook.Worksheets.Clear()

# Add a new worksheet
worksheet = workbook.Worksheets.Add("Employee Data")

# Nested list (rows: [Name, Age, Department])
employee_data = [
    ["Name", "Age", "Department"],  # Header row
    ["Alexander", 30, "HR"],
    ["Bob", 28, "Engineering"],
    ["Charlie", 35, "Marketing"],
    ["Diana", 29, "Finance"]
]

# Write nested list to Excel
for row_idx, row_data in enumerate(employee_data):
    for col_idx, value in enumerate(row_data):
        if isinstance(value, int):
            worksheet.Range[row_idx + 1, col_idx + 1].NumberValue = value
        else:
            worksheet.Range[row_idx + 1, col_idx + 1].Value = value

# Format header row 
worksheet.Range["A1:C1"].Style.Font.IsBold = True
worksheet.Range["A1:C1"].Style.Color = Color.get_Yellow()

# Set column widths
worksheet.Range[1, 1].ColumnWidth = 10  
worksheet.Range[1, 2].ColumnWidth = 6 
worksheet.Range[1, 3].ColumnWidth = 15

# Save the workbook
workbook.SaveToFile("NestedListToExcel.xlsx", ExcelVersion.Version2016)
workbook.Dispose()

Explanation:

• Nested List Structure: The first sub-list acts as headers, and subsequent sub-lists are data rows.
• 2D Loop: We use nested loops to write each row and column to Excel cells.

Output: An Excel table with bold yellow headers and correctly typed data.

To make your Excel files more professional, you can add cell borders, set conditional formatting, or apply other formatting options with Spire.XLS for Python.

Convert a List of Dictionaries to Excel

Lists of dictionaries are common in Python for storing structured data with labeled fields. This example converts a list of dictionaries (e.g., customer records) to Excel and auto-extracts headers from dictionary keys.

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

# Create a Workbook object
workbook = Workbook()

# Clear the default sheets
workbook.Worksheets.Clear()

# Add a new worksheet
worksheet = workbook.Worksheets.Add("Customer Data")

# List of dictionaries
customers = [
    {"ID": 101, "Name": "John Doe", "Email": "john@example.com"},
    {"ID": 102, "Name": "Jane Smith", "Email": "jane@example.com"},
    {"ID": 103, "Name": "Mike Johnson", "Email": "mike@example.com"}
]

# Extract headers from dictionary keys
headers = list(customers[0].keys())

# Write headers to row 1
for col, header in enumerate(headers):
    worksheet.Range[1, col + 1].Value = header
    worksheet.Range[1, col + 1].Style.Font.IsBold = True  # Bold headers

# Write data rows
for row, customer in enumerate(customers, start=2):  # Start from row 2
    for col, key in enumerate(headers):
        value = customer[key]
        if isinstance(value, (int, float)):
            worksheet.Range[row, col + 1].NumberValue = value
        else:
            worksheet.Range[row, col + 1].Value = value

# Adjust column widths
worksheet.AutoFitColumn(2) 
worksheet.AutoFitColumn(3) 

# Save the file
workbook.SaveToFile("CustomerDataToExcel.xlsx", ExcelVersion.Version2016)
workbook.Dispose()

Why This Is Useful:

• Auto-Extracted Headers: Saves time. No need to retype headers like “ID” or “Email”.
• Auto-Fit Columns: Excel automatically adjusts column width to fit the longest text.
• Scalable: Works for large lists of dictionaries (e.g., 1000+ customers).

Output: Excel file with headers auto-created, data types preserved, and columns automatically sized.

4 Tips to Optimize Your Excel Outputs​

• Preserve Data Types: Always use NumberValue for numbers (avoids issues with Excel calculations later).​
• Auto-Fit Columns: Use worksheet.AutoFitColumn() to skip manual width adjustments.​
• Name Worksheets Clearly: Instead of “Sheet1”, use names like “Q3 Sales” to make files user-friendly.​
• Dispose of Workbooks: Always call workbook.Dispose() to free memory (critical for large datasets).

Conclusion

Converting lists to Excel in Python is a critical skill for data professionals, and Spire.XLS makes it easy to create polished, production-ready files. Whether you’re working with simple lists, nested data, or dictionaries, the examples above can be adapted to your needs.​

For even more flexibility (e.g., adding charts or formulas), explore Spire.XLS’s documentation.​

FAQs for List to Excel Conversion

Q1: How is Spire.XLS different from pandas for converting lists to Excel?

A: Pandas is great for quick, basic exports, but it lacks fine-grained control over Excel formatting. Spire.XLS is better when you need:

• Custom styles (colors, fonts, borders).
• Advanced Excel features (freeze panes, conditional formatting, charts).
• Standalone functionality (no Excel installation required).

Q2: How do I save my Excel file in different formats?

A: Use the ExcelVersion parameter in SaveToFile:

workbook.SaveToFile("output.xlsx", ExcelVersion.Version2016)
workbook.SaveToFile("output.xls", ExcelVersion.Version97to2003)

Q3: How does Spire.XLS handle different data types?

A: Spire.XLS provides specific properties for different data types:

• Use .Text for strings
• Use .NumberValue for numerical data
• Use .DateTimeValue for dates
• Use .BooleanValue for True/False values

Q4: Why clear default worksheets before adding new ones?

A: Spire.XLS for Python creates default sheets when you create a Workbook. Therefore, if you don't clear it with the Workbook.Worksheets.Clear(), your file will have extra empty sheets.

Q5: My data isn't showing correctly in Excel. What's wrong?

A: Check that you're using 1-based indexing and that your data types match the expected format. Also, verify that you're saving the file before disposing of the workbook.