How to export to Excel File using Python

A crucial necessity for many apps and enterprises in today's data-driven environment is the ability to export data to Excel spreadsheets. Thanks to its familiarity and adaptability, the Excel file continues to be a favored format for producing reports, performing analysis, and communicating findings with stakeholders. Developers have access to robust tools for working with Excel files because of Python's vast ecosystem of libraries.

A reliable option for easily exporting data to Excel spreadsheets is IronXL, which stands out among the others. We will look at how IronXL for Python makes data exporting to a worksheet easier in this post, giving developers more control over how their data export procedures are organized.

How to Export to Excel File Using Python

1. In Visual Studio Code, create a Python file.
2. Install the Pip Python IronXL library.
3. Establish the data that needs to be exported to Excel.
4. Map the data using the Excel library to the specific cell.
5. To export all data frames to the new Excel file, save it.

IronXL

IronXL is a feature-rich Python library designed especially for usage with Excel files. Programmers have a plethora of solutions at their disposal for reading, writing, editing, and altering spreadsheet data. Built on top of the .NET framework, IronXL combines the flexibility of Python with the performance of .NET to offer an efficient means of interacting with Excel files.

One of IronXL's primary strengths is its simplicity of reading data from existing Excel files. Developers may quickly extract data from specific files, such as formatted cells, rows, lists of column names, and object cells that follow values or columns, enabling them to incorporate Excel spreadsheet data into Python programs with ease. Whether you need to retrieve sales, customer, or financial data, IronXL provides the tools you need to work with your Excel file data effectively.

Features of IronXL

Data may be easily read from existing Excel files and written to either newly constructed or pre-existing spreadsheets with the help of IronXL. This includes a wide range of functionalities, including cell value access, formatting, and formulas.

Key Feature Examples

• Cross-Platform Compatibility: Because IronXL is designed to work smoothly on a range of platforms, including Windows, Linux, and macOS, it is a versatile alternative for Python developers regardless of their operating system.
• Efficient Performance: IronXL, which is based on the .NET framework, processes and effectively manipulates Excel files, even when working with large datasets. It does this by combining the flexibility of Python with the efficiency of .NET.
• Support for Excel Formats: IronXL can open and operate a variety of Excel file formats, including: xls (Excel 97–2003), .xlsx (Excel 2007 and later), comma-separated values file (CSV), and .xlsm (Excel with macros enabled).
• Advanced Data Manipulation: IronXL enables users to perform more complex data manipulation operations in Excel spreadsheets, such as sorting, filtering, and aggregating, which can help derive valuable insights.
• Cell Formatting: IronXL's cell formatting tools, which include font styles, colors, borders, and alignment, can improve the visual appeal and readability of Excel spreadsheets.
• Formula Calculation: Users can utilize IronXL to perform dynamic calculations inside Excel spreadsheets, as well as formula evaluation and formula reference updating.
• Integration with the Python Environment: Users may effortlessly integrate IronXL with other Python frameworks and packages to perform comprehensive data visualization and analysis by combining its features with them.
• Ease of Use: IronXL's simple, user-friendly API makes it suitable for Python developers of all expertise levels. Its well-documented interface provides detailed instructions on how to take full advantage of all of its capabilities.

In essence, IronXL helps Python developers overcome the challenges associated with managing Excel files by offering a dependable and intuitive method of integrating Excel functionality into Python applications.

IronXL provides the flexibility and tools needed to succeed in manipulating Excel files within the Python ecosystem, whether you're creating interactive dashboards, automating reporting tasks, or developing data analysis tools that require reading Excel files. Go here to learn more about the IronXL for Python library.

Setup Environment

Prerequisites

Before starting the guide, confirm that the following are installed on your computer:

• .NET 6.0 SDK: IronXL requires the .NET 6.0 SDK to be installed on your computer because it was built using it.
• Python 3.0+: You must have Python 3.0 or a later version installed to follow this tutorial.
• pip: Since IronXL will need the Python package installer pip, install it first.

Install IronXL

Create a Python file named ExportData.py after opening this file in Visual Studio Code. Our script for using IronXL to export Excel files is contained in this file.

In Visual Studio Code, select Terminal > New Terminal from the menu to open the command line.

The first thing to do before using IronXL is to install the library. You can rapidly install IronXL by running the following command with pip, Python's package manager:

pip install ironxl

pip install ironxl

IronXL may now be used to read Excel spreadsheet files that you have installed.

Export Data To Excel using IronXL

With IronXL for Python, exporting data to a new or existing Excel file without the need to import Pandas is simple. Let's examine a straightforward illustration of data exporting to an Excel spreadsheet:

from ironxl import WorkBook

# Sample dataset created as a list of lists
data = [
    ["Name", "Age", "Salary"],
    ["John", 30, 50000],
    ["Alice", 25, 60000],
    ["Bob", 35, 70000]
]

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

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

# Write data to Excel worksheet
worksheet.InsertColumn(4)
worksheet.InsertRow(len(data) + 1)

# Loop through rows and columns in the dataset
for row_idx, row_data in enumerate(data):
    for col_idx, cell_data in enumerate(row_data):
        try:
            # Set the cell value
            worksheet.SetCellValue(row_idx, col_idx, str(cell_data))
        except Exception as e:
            print("An exception occurred: " + str(e))

# Save the workbook to the specified file path
workbook.SaveAs("output.xlsx")

from ironxl import WorkBook

# Sample dataset created as a list of lists
data = [
    ["Name", "Age", "Salary"],
    ["John", 30, 50000],
    ["Alice", 25, 60000],
    ["Bob", 35, 70000]
]

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

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

# Write data to Excel worksheet
worksheet.InsertColumn(4)
worksheet.InsertRow(len(data) + 1)

# Loop through rows and columns in the dataset
for row_idx, row_data in enumerate(data):
    for col_idx, cell_data in enumerate(row_data):
        try:
            # Set the cell value
            worksheet.SetCellValue(row_idx, col_idx, str(cell_data))
        except Exception as e:
            print("An exception occurred: " + str(e))

# Save the workbook to the specified file path
workbook.SaveAs("output.xlsx")

The code snippet above creates a sample dataset as a list of lists representing data rows and columns. Then, we use nested loops to write each data frame to a new Excel worksheet created using IronXL's CreateWorkSheet method. We can create multiple sheets similarly. The output target file can be saved as "output.xlsx", which creates a new Excel file at the specified location.

Customizing the Excel Export

# Customizing Excel export
worksheet["A1"].Style.Font.Bold = True  # Make the font in cell A1 bold
worksheet["A1"].Style.BackgroundColor = "Red"  # Set the background color of cell A1 to red
worksheet.Columns[0].Width = "20"  # Set the width of the first column
worksheet.Columns[0].FormatString = "$#,###0.00"  # Format the column as currency

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

# Customizing Excel export
worksheet["A1"].Style.Font.Bold = True  # Make the font in cell A1 bold
worksheet["A1"].Style.BackgroundColor = "Red"  # Set the background color of cell A1 to red
worksheet.Columns[0].Width = "20"  # Set the width of the first column
worksheet.Columns[0].FormatString = "$#,###0.00"  # Format the column as currency

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

Without using any additional Python libraries installed, we may change the look of the generated Excel spreadsheet in this example by bolding the font, changing the background color of cell row A1 to yellow, modifying column B's width, and formatting column C as currency. IronXL can handle missing data representation in the Excel spreadsheet. To learn more about IronXL's code, check here.

Below is the output generated from the above code.

Conclusion

We have looked at how IronXL for Python makes data exporting to Excel spreadsheets easier in this article. IronXL offers a reliable and simple solution for data exporting, from installing the library to modifying the exported data. IronXL for Python gives developers the ability to optimize their data export processes and open up new possibilities for data management and visualization, whether they're creating reports, exchanging insights, or performing analysis. Explore the world of data export with IronXL for Python and enhance your apps that are powered by data.

A permanent license, upgrade options, and a year of software support are included with IronXL's $749 Lite edition. During the trial period, customers can evaluate the product in real-world scenarios. To find out more about IronXL's pricing, licensing, and a free trial. Alternatively, go to this website to learn more about Iron Software.

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