from ironxl import * # Supported for XLSX, XLS, XLSM, XLTX, CSV, and TSV workbook = WorkBook.Load("sample.xlsx") # Select worksheet at index 0 worksheet = workbook.WorkSheets[0] # Get any existing worksheet first_sheet = workbook.DefaultWorkSheet # Select a cell and return the converted value cell_value = worksheet["A2"].IntValue # Read from ranges of cells elegantly. for cell in worksheet["A2:A10"]: print("Cell {} has value '{}'".format(cell.AddressString, cell.Text)) # Calculate aggregate values such as Min, and Sum total_sum = worksheet["A2:A10"].Sum()

from ironxl import * # Load existing spreadsheet workbook = WorkBook.Load("sample.xls") worksheet = workbook.WorkSheets[0] # Get a range from an Excel worksheet selected_range = worksheet["A1:H10"] # Get the first cell in the range cell = selected_range.ToArray()[0] # Set background color of the cell with an RGB string cell.Style.SetBackgroundColor("#428D65") # Apply styling to the whole range # Set underline property to the font # FontUnderlineType is an enum that stands for different types of font underline selected_range.Style.Font.Underline = FontUnderlineType.SingleAccounting # Define whether to use a horizontal line through the text or not selected_range.Style.Font.Strikeout = False # Define whether the font is bold or not selected_range.Style.Font.Bold = True # Define whether the font is italic or not selected_range.Style.Font.Italic = False # Get or set the script property of a font # FontScript enum stands for available options selected_range.Style.Font.FontScript = FontScript.Super # Set the type of the border line # There are also TopBorder, LeftBorder, RightBorder, DiagonalBorder properties # BorderType enum indicates the line style of a border in a cell selected_range.Style.BottomBorder.Type = BorderType.MediumDashed # Indicate whether the cell should be auto-sized selected_range.Style.ShrinkToFit = True # Set alignment of the cell selected_range.Style.VerticalAlignment = VerticalAlignment.Bottom # Set border color selected_range.Style.DiagonalBorder.SetColor("#20C96F") # Define border type and border direction as well selected_range.Style.DiagonalBorder.Type = BorderType.Thick # DiagonalBorderDirection enum stands for direction of diagonal border inside cell selected_range.Style.DiagonalBorderDirection = DiagonalBorderDirection.Forward # Set background color of cells selected_range.Style.SetBackgroundColor(Color.Aquamarine) # Set fill pattern of the cell # FillPattern enum indicates the style of fill pattern selected_range.Style.FillPattern = FillPattern.Diamonds # Set the number of spaces to indent the text selected_range.Style.Indention = 5 # Indicate if the text is wrapped selected_range.Style.WrapText = True # Save changes with applied styling options workbook.SaveAs("stylingOptions.xls")

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A Comparison Between Fastexcel Python & IronXL Python

Published June 6, 2024

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 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 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 Python

A Comparison Between Fastexcel Python & IronXL Python: Figure 1 - IronXL for Python: The Python Excel Library

IronXL 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 Python brings to those needing detailed control over their spreadsheet data.

1. Easy Excel File Management

IronXL 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 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 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 makes sure that all formula-driven data remains accurate and up-to-date. Here is the python code:

from ironxl import *     
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
# Force 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(F2:F16)"
max_sales_formula = worksheet["E19"].Formula
# Save changes with updated formulas and calculated values.
workbook.Save()

from ironxl import *     
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
# Force 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(F2:F16)"
max_sales_formula = worksheet["E19"].Formula
# Save changes with updated formulas and calculated values.
workbook.Save()

PYTHON

Output Excel file

A Comparison Between Fastexcel Python & IronXL Python: Figure 2 - Excel output generated programmatically to Excel formulas using IronXL for Python

4. Integration with .NET Projects

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

from ironxl import *     
License.LicenseKey = "License-Code";
# Create 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 *     
License.LicenseKey = "License-Code";
# Create 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")

PYTHON

Output Excel File

A Comparison Between Fastexcel Python & IronXL Python: Figure 3 - Output: Formatted Excel file using IronXL.

7. Security and Protection

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

8. Support for Excel and CSV Formats

IronXL 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

A Comparison Between Fastexcel Python & IronXL Python: Figure 4 - 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. It helps 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")

PYTHON

Installation Steps

Install IronXL 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
```
PYTHON

This command installs FastExcel along with any dependencies it requires.

Comparative Overview: IronXL Python vs FastExcel Python

Choosing the right Excel library for Python projects involves weighing the capabilities and performance of tools like IronXL 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 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 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 Python is ideal for projects needing to handle complex, multi-faceted Excel files. It is very suitable for the projects where complex operations need to be performed on spreadsheets like in finance, legal, etc. 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 analysis that need to be efficient and light on resources.

User Experience and Ease of Integration

IronXL Python, has a very learning curve due to its extensive feature set and capabilities. It is very valuable and helpful to the 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 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 Python stands out as a more comprehensive solution for handling Excel files in Python. With its robust feature set, IronXL 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 Python also provides thorough documentation with numerous code examples, making it easier for developers at all levels to implement its features effectively. Moreover, IronXL Python is supported by a responsive engineering team, ensuring assistance is readily available when needed.

IronXL Python pages.

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