How to export to Excel File using Python

Introduction

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, doing analysis, and communicating findings with stakeholders. Developers have access to strong 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 file in Python.
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 format cells such as rows, lists of column names, object cells that follow values, or columns, which enables 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 covers a wide range of subjects, 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.
• Effective efficiency: 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 files in the following formats.three file formats: xls (Excel 97–2003), .xlsx file (Excel 2007 and later), comma-separated values file (CSV), and .xlsm (Excel with macros enabled).
• Advanced Data Manipulation: IronXL enables users to do more complex data manipulation operations in Excel spreadsheets, such as sorting, filtering, and aggregating, from which they can get 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 do 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 do 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-written interface provides detailed instructions on how to take full advantage of all of its capabilities.

In essence, IronXL helps Python developers get over 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 creating 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:

• 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

Make 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

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

Export Data To Excel using IronXL

With IronXL 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 *     data = [
    ["Name", "Age", "Salary"],
    ["John", 30, 50000],
    ["Alice", 25, 60000],
    ["Bob", 35, 70000]
]
# Create 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);
for row_idx, row_data in enumerate(data):
    for col_idx, cell_data in enumerate(row_data):
        try:
            worksheet.SetCellValue(row_idx, (col_idx), str(cell_data));
        except Exception as e:
            print("An exception occurred"+str(e))
# Save the workbook to the ExcelWriter object file path
workbook.SaveAs("output.xlsx")

from ironxl import *     data = [
    ["Name", "Age", "Salary"],
    ["John", 30, 50000],
    ["Alice", 25, 60000],
    ["Bob", 35, 70000]
]
# Create 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);
for row_idx, row_data in enumerate(data):
    for col_idx, cell_data in enumerate(row_data):
        try:
            worksheet.SetCellValue(row_idx, (col_idx), str(cell_data));
        except Exception as e:
            print("An exception occurred"+str(e))
# Save the workbook to the ExcelWriter object file path
workbook.SaveAs("output.xlsx")

A sample dataset is created in the code snippet above as a list of lists that represent data rows and columns. Then, we use nested loops to write each data frame to a new Excel worksheet of an existing file we created using IronXL's CreateWorkSheet method and by passing the Excel sheet name as a parameter. like this we can able to create multiple sheets. The output target file name can be saved as "output.xlsx". Which creates a new Excel file on the location.

Customizing the Excel Export

# Customizing Excel export
worksheet["A1"].Style.Font.Bold = True
 worksheet["A1"].Style.BackgroundColor = "Red"
worksheet.Columns[0].Width = "20"
 worksheet.Columns[0].FormatString = "$#,###0.00"
# Save the workbook
workbook.SaveAs("formattedoutput.xlsx")

# Customizing Excel export
worksheet["A1"].Style.Font.Bold = True
 worksheet["A1"].Style.BackgroundColor = "Red"
worksheet.Columns[0].Width = "20"
 worksheet.Columns[0].FormatString = "$#,###0.00"
# Save the workbook
workbook.SaveAs("formattedoutput.xlsx")

Without using any additional Python libraries installed, we may change the look of the produced 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 able to 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 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 Python gives developers the ability to optimize their data export processes and open up new possibilities for data management and visualization, regardless of whether they're creating reports, exchanging insights, or performing analysis. Explore the world of data export with IronXL 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 price, licensing, and a free trial. Alternatively, go to this website to learn more about Iron Software.

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