A Comparison Between Fastexcel Python & IronXL for Python

Professionals in fields like business analytics and software engineering are always on the lookout for powerful tools that can make their work with data and documents more efficient. For Python users, FastExcel Python and IronXL for Python stand out as two solid options.

FastExcel Python is known for its straightforward design and strong capabilities in swiftly handling Excel files. Meanwhile, IronXL for Python brings to the table a wide range of features that not only include Excel file management but also support for multiple file formats.

In this article, we’ll dive into what each of these Python libraries offers in terms of functionality, performance, and practical use cases. We’ll provide a clear comparison to help you figure out which library might be the best fit for your needs when working with Excel files.

Key Features of IronXL for Python

IronXL for Python stands out in the Python ecosystem as a robust tool designed for extensive manipulation and management of Excel files. This section explores the comprehensive capabilities that IronXL for Python brings to those needing detailed control over their spreadsheet data.

1. Easy Excel File Management

IronXL for Python simplifies the process of managing Excel files. It helps users to create, edit, save, and read excel files efficiently. This capability is vital for anyone who regularly works with complex datasets and needs a reliable tool to manage Excel data without the need for Microsoft Excel. It also makes reading Excel files very easy.

2. Data Handling

With IronXL for Python, users can perform advanced data manipulation tasks. This includes reading and writing data to and from Excel files, handling bulk data operations, and applying data validation rules to ensure data integrity. This feature supports a range of data types and structures.

3. Comprehensive Formula Support

One of the standout features of IronXL for Python is its ability to calculate and update Excel formulas programmatically. For projects that require the automation of data calculations within spreadsheets, this feature provides great support. It ensures that all formula-driven data remains accurate and up-to-date. Here is the Python code:

from ironxl import *
# Setting a license key for IronXL
License.LicenseKey = "License-Code"

# Load the Excel workbook
workbook = WorkBook.Load("Realistic_Sample_Excel_File.xlsx")
worksheet = workbook.DefaultWorkSheet

# Set formulas to perform meaningful calculations with the new data
worksheet["E18"].Formula = "SUM(E2:E16)"  # Sum of Total Sales
worksheet["D18"].Formula = "AVERAGE(D2:D16)"  # Average Unit Price
worksheet["E19"].Formula = "MAX(E2:E16)"  # Maximum of Total Sales

# Recalculate all formula values in all sheets.
workbook.EvaluateAll()

# Get the formula's calculated value for the maximum total sales
max_sales_value = worksheet["E19"].ToArray()[0].FormattedCellValue

# Get the formula as a string, e.g., "MAX(E2:E16)"
max_sales_formula = worksheet["E19"].Formula

# Save changes with updated formulas and calculated values.
workbook.Save()

from ironxl import *
# Setting a license key for IronXL
License.LicenseKey = "License-Code"

# Load the Excel workbook
workbook = WorkBook.Load("Realistic_Sample_Excel_File.xlsx")
worksheet = workbook.DefaultWorkSheet

# Set formulas to perform meaningful calculations with the new data
worksheet["E18"].Formula = "SUM(E2:E16)"  # Sum of Total Sales
worksheet["D18"].Formula = "AVERAGE(D2:D16)"  # Average Unit Price
worksheet["E19"].Formula = "MAX(E2:E16)"  # Maximum of Total Sales

# Recalculate all formula values in all sheets.
workbook.EvaluateAll()

# Get the formula's calculated value for the maximum total sales
max_sales_value = worksheet["E19"].ToArray()[0].FormattedCellValue

# Get the formula as a string, e.g., "MAX(E2:E16)"
max_sales_formula = worksheet["E19"].Formula

# Save changes with updated formulas and calculated values.
workbook.Save()

Output Excel File

4. Integration with .NET Projects

IronXL for Python offers seamless integration with .NET projects, allowing for cross-platform operations involving Excel file manipulation. This integration is particularly useful for users who work in a .NET environment but need to use Python for specific tasks or data analysis.

5. High Performance and Efficiency

Designed for performance, IronXL for Python handles large and complex Excel files swiftly. Its optimized processing capabilities ensure that operations on Excel files, even those with large datasets, are performed quickly, reducing the time spent on data processing tasks.

6. Advanced Formatting Options

IronXL for Python allows users to apply formatting to Excel sheets, including setting fonts, colors, number formats, and cell borders. This feature enhances the visual presentation of Excel reports by adding conditional formatting and helps to understand Excel files more efficiently.

from ironxl import *
# Setting a license key for IronXL
License.LicenseKey = "License-Code"

# Create a new Excel WorkBook document
workbook = WorkBook.Create()

# Convert XLSX to XLS (if needed for compatibility)
xlsWorkBook = WorkBook.Create(ExcelFileFormat.XLSX)

# Create a blank WorkSheet
worksheet = workbook.CreateWorkSheet("new_sheet")

# Add data and styles to the new worksheet
worksheet["A1"].Value = "Welcome"
worksheet["A1"].Style.Font.Bold = True
worksheet["A1"].Style.Font.Size = 14
worksheet["B1"].Value = "Data Overview"
worksheet["B1"].Style.Font.Italic = True
worksheet["A2"].Value = "1200"
worksheet["A4"].Value = "Completed"
worksheet["A4"].Style.TopBorder.Type = BorderType.Thick

# Save the workbook
workbook.SaveAs("modified_test.xlsx")

from ironxl import *
# Setting a license key for IronXL
License.LicenseKey = "License-Code"

# Create a new Excel WorkBook document
workbook = WorkBook.Create()

# Convert XLSX to XLS (if needed for compatibility)
xlsWorkBook = WorkBook.Create(ExcelFileFormat.XLSX)

# Create a blank WorkSheet
worksheet = workbook.CreateWorkSheet("new_sheet")

# Add data and styles to the new worksheet
worksheet["A1"].Value = "Welcome"
worksheet["A1"].Style.Font.Bold = True
worksheet["A1"].Style.Font.Size = 14
worksheet["B1"].Value = "Data Overview"
worksheet["B1"].Style.Font.Italic = True
worksheet["A2"].Value = "1200"
worksheet["A4"].Value = "Completed"
worksheet["A4"].Style.TopBorder.Type = BorderType.Thick

# Save the workbook
workbook.SaveAs("modified_test.xlsx")

Output Excel File

7. Security and Protection

IronXL for Python provides features to secure Excel files, including options to encrypt files and set detailed access permissions. It helps handle sensitive or confidential data, ensuring that information is protected against unauthorized access.

8. Support for Excel and CSV Formats

IronXL for Python supports not only traditional Excel file formats like XLSX files but also CSV files. This dual capability allows users to interchangeably work with different types of data files depending on their project needs.

Key Features of FastExcel Python

FastExcel Python is designed to enhance productivity and efficiency in handling Excel files within the Python environment. Here we explore the core features that define this powerful library.

1. High-Speed Excel Processing

FastExcel Python is optimized for speed, allowing users to read and write Excel files at high velocities. This is advantageous for applications that require rapid data processing.

2. Simplified API for Common Tasks

The library offers a simplified API that makes common Excel tasks such as reading, writing, and saving spreadsheets straightforward. Users can accomplish more with fewer lines of code.

3. Minimal Memory Usage

FastExcel Python is designed to use minimal memory, making it ideal for handling large Excel files on systems with limited resources. This feature ensures that performance does not degrade even with large datasets.

4. Lightweight Installation

FastExcel Python has a lightweight footprint, making it easy to install and integrate into existing Python environments. This ease of installation simplifies setup and deployment.

5. Support for Multiple Excel Formats

FastExcel Python supports various Excel formats including .xlsx, .xls, and even .csv files. This flexibility allows users to work seamlessly across different versions of Excel and related data formats.

6. Customizable Cell Formatting

The library provides extensive options for formatting cells. Users can modify fonts, colors, and borders, allowing for highly customized output spreadsheets.

from fast_excel import FastExcel

# Load an existing Excel file
workbook = FastExcel.read_excel("data.xlsx")
worksheet = workbook.get_sheet_by_name("Sheet1")

# Set custom font and color for cells in the first row
for cell in worksheet.iter_rows(min_row=1, max_row=1):
    for c in cell:
        c.font = FastExcel.Font(name='Calibri', size=12, bold=True, color='red')

# Save the changes
workbook.save("styled_data.xlsx")

from fast_excel import FastExcel

# Load an existing Excel file
workbook = FastExcel.read_excel("data.xlsx")
worksheet = workbook.get_sheet_by_name("Sheet1")

# Set custom font and color for cells in the first row
for cell in worksheet.iter_rows(min_row=1, max_row=1):
    for c in cell:
        c.font = FastExcel.Font(name='Calibri', size=12, bold=True, color='red')

# Save the changes
workbook.save("styled_data.xlsx")

Installation Steps

Install IronXL for Python

Before proceeding with the IronXL installation, ensure that the .NET 6.0 SDK is installed on your system. This is because IronXL relies on .NET 6.0 to function. If you do not have it installed, download and install it from the official .NET website. To install IronXL, you will need to use pip, the Python package installer.

• Open your command prompt (Windows) or terminal (macOS or Linux).

  • Type the following command and press Enter:

   pip install IronXL

This command will download and install the IronXL library and all necessary dependencies.

Install FastExcel

FastExcel provides efficient operations for reading and writing Excel files in Python. Follow these steps to install it:

• Ensure your command prompt or terminal is still open.

  • Enter the following command and hit Enter:

  pip install fastexcel

  pip install fastexcel

  SHELL

This command installs FastExcel along with any dependencies it requires.

Comparative Overview: IronXL for Python vs FastExcel Python

Choosing the right Excel library for Python projects involves weighing the capabilities and performance of tools like IronXL for Python and FastExcel Python. This comparison highlights key aspects such as efficiency, features, suitability for different use cases, and user experience.

Efficiency in Handling Excel Data

IronXL for Python is designed to be highly efficient when working with complex Excel files in a Python environment. It can handle extensive data manipulations and supports advanced Excel features, which makes it suitable for comprehensive data processing tasks.

In contrast, FastExcel Python focuses on high-speed processing for both reading and writing Excel files. It is lightweight in environments where basic performance and minimal memory usage are critical.

Feature Comparison

IronXL for Python provides a rich set of features that include full support for reading, writing, and manipulating Excel files. It can manage security settings, apply advanced formatting, and perform complex data manipulations. IronXL's capabilities make it a robust choice for developers needing a comprehensive tool for extensive Excel manipulations.

FastExcel Python, on the other hand, emphasizes speed and simplicity. It offers a streamlined API that makes common Excel tasks quick and easy. While it may lack some of the more advanced features of IronXL, it excels in basic Excel operations.

Use Cases

IronXL for Python is ideal for projects needing to handle complex, multi-faceted Excel files. It is particularly suitable for projects where complex operations need to be performed on spreadsheets, such as in finance and legal domains. It's particularly useful for enterprise-level applications where robustness and a wide array of features are necessary.

FastExcel Python is best suited for applications that require rapid processing of Excel data, such as data analyses that need to be efficient and light on resources.

User Experience and Ease of Integration

IronXL for Python has a steeper learning curve due to its extensive feature set and capabilities. It is valuable for developers who are new to the language or want to learn the advanced features of IronXL. Its documentation covers all aspects of the library.

FastExcel Python is recognized for its ease of use, making it accessible for beginners and those who prefer a straightforward approach to Excel file manipulation. Its minimalistic design and focus on core functionalities allow for quick integration and ease of use in a wide range of applications.

Conclusion

Throughout this article, we've delved into the capabilities and differences between IronXL for Python and FastExcel Python. We've examined how each library caters to different aspects of Excel file manipulation and which scenarios they best suit.

FastExcel Python excels in high-speed data processing and is streamlined for efficiency, making it a great choice for projects where basic performance and minimal memory usage are crucial. However, it does not offer the broad feature set that some complex tasks might require, and its focus is primarily on speed rather than extensive functionality.

Conversely, IronXL for Python stands out as a more comprehensive solution for handling Excel files in Python. With its robust feature set, IronXL for Python is well-suited for projects that demand a high level of Excel manipulation, including advanced formatting, complex formula calculations, and integration with .NET projects. Its ability to handle security features, like file encryption and setting access permissions, adds another layer of utility for enterprise applications. IronXL for Python also provides thorough documentation with numerous code examples, making it easier for developers at all levels to implement its features effectively. Moreover, IronXL for Python is supported by a responsive engineering team, ensuring assistance is readily available when needed.

IronXL for Python pages.