How to Export to Excel in Python

Python's flexibility also includes its smooth integration with Excel, a popular spreadsheet program. It provides developers with several choices for effectively using Python to export data to Excel (XLSX file) using a wide range of open-source tools. This post will examine IronXL's strong performance as a Python library substitute for C# data export to an Excel target file.

How to Export Data to Excel using Python

To export data to Excel using Python, follow these steps:

1. Import the required libraries.
2. Prepare or retrieve your data.
3. Create a Workbook or DataFrame object for exporting data.
4. Populate the object with your data.
5. Save the object to an Excel file using the appropriate method.
6. Optionally, close the file or perform additional operations.

Pandas

Pandas is a powerful Python package for handling data analysis and missing data representation. One of its numerous functions is support for exporting data to Excel. Pandas offers a simple way to export DataFrames to Excel files using the to_excel() function. Developers can alter export settings, including sheet name, index inclusion, optional column label, and formatting choices. Pandas is the recommended option for exporting structured data to Excel due to its interaction with other data processing features.

OpenPyXL

A package called OpenPyXL was created expressly for using Excel files with Python code. OpenPyXL operates at a lower level than Pandas, giving developers more precise control over the format and content of Excel documents. Users may programmatically generate multiple sheets, and edit, and export Excel files using OpenPyXL. For activities requiring sophisticated Excel manipulation, like dynamically inserting formulas, charts, and formatting features, this package is well-suited. Even though OpenPyXL has a higher learning curve than Pandas, it provides unmatched versatility for Excel export operations.

XlsxWriter

A Python library called XlsxWriter is used to create Excel files with an emphasis on memory savings and performance. Large datasets are easily handled by this library, and it produces intricate Excel documents quickly. Many functionalities are supported by XlsxWriter, such as cell merging, chart generation, and worksheet formatting. Because of its optimized architecture, XlsxWriter is the best option for situations requiring fast Excel export, such as batch processing jobs and data-intensive applications.

xlrd and xlwt

The sibling libraries xlrd and xlwt allow you to read and write Excel files in Python, respectively. These libraries are still useful in some situations, even if their main purpose is to handle older Excel file formats (like .xls). xlrd and xlwt are very helpful for developers who have to work with older Excel file formats or legacy systems. However, because of their improved functionality and performance, Pandas, OpenPyXL, or XlsxWriter are typically advised for the more recent Excel formats (.xlsx).

Tablib

A flexible library called Tablib can handle tabular data in many different formats, including Excel. Tablib is a feature-rich tool for exporting data to Excel files; however, it isn't as feature-rich as Pandas or OpenPyXL. For developers who need to export data in a variety of formats with ease, Tablib provides a handy solution with support for several output formats, such as Excel, CSV, and JSON. Tablib is a good choice for small-scale Excel export jobs or projects that need multi-format data export capabilities because of its lightweight design and user-friendly interface.

IronXL

Managing Excel files with ease is essential for many C# programming applications, such as data processing and report creation. One powerful option that shows up is IronXL, which gives developers an extensive toolkit to easily work with Excel files. We will explore the features of IronXL in this post and show you how it may be a strong substitute for Python libraries when it comes to Excel automation tasks.

• IronXL is a well-known C# Excel spreadsheet library for .NET Core and .NET Framework.
• IronXL supports almost every .NET Framework, such as the Web application, Windows Form application, and Console.
• Windows, Linux, and macOS are just a few of the operating systems that IronXL is compatible with.
• Excel file reading is quick and easy with IronXL.
• A variety of Excel file types, including XLSX files, XLS files, CSV, TSV, XLST, XLSM, and others, may be read by IronXL. In addition, we can edit, export, and import datasets.
• We can export and save files with many other suffixes, such as XLS, comma-separated values files, TSV, JSON, and others, using IronXL.
• IronXL can produce computations in Excel and format cells of the Excel sheets.
• Many Excel column data types, including text, integers, formulas, dates, currencies, and percentages, are supported by IronXL.

To know more about IronXL, refer here.

Install IronXL

Using the command line, follow these steps to install IronXL.

In Visual Studio, go to Tools -> NuGet Package Manager -> Package Manager Console.

Write the following syntax into the Package Manager's Console tab:

 pip install IronXL

The package is ready for usage as it downloads and installs to the active project.

Export to Excel file

With IronXL, creating data tables to CSV files is simple and rapid. It facilitates data writing to a fresh CSV file.

We must first include the IronXL namespace, which is necessary to utilize its classes and methods in our code.

Below is a sample code for exporting a DataTable to an Excel file using IronXL:

using IronXL;
using IronXL.Formatting;
using System.Data;

public class ExcelExporter
{
    public static void Main(string[] args)
    {
        ExportToExcel("H:\\test.xls");
    }

    public static void ExportToExcel(string filePath)
    {
        // Create a DataTable and define columns
        DataTable table = new DataTable();
        table.Columns.Add("DataSet_Fruits", typeof(string));

        // Adding rows to the DataTable
        table.Rows.Add("Apple");
        table.Rows.Add("Orange");
        table.Rows.Add("Strawberry");
        table.Rows.Add("Grapes");
        table.Rows.Add("Watermelon");
        table.Rows.Add("Bananas");
        table.Rows.Add("Lemons");

        // Create a workbook and set the format to XLS
        WorkBook workbook = WorkBook.Create(ExcelFileFormat.XLS);
        var worksheet = workbook.CreateWorkSheet("Sheet1");

        // Add the DataTable data to the worksheet
        int rowCount = 0;
        foreach (DataRow row in table.Rows)
        {
            worksheet["A" + (rowCount + 1)].Value = row[0].ToString();
            rowCount++;
        }

        // Save the workbook to the specified file path
        workbook.SaveAs(filePath);
    }
}

using IronXL;
using IronXL.Formatting;
using System.Data;

public class ExcelExporter
{
    public static void Main(string[] args)
    {
        ExportToExcel("H:\\test.xls");
    }

    public static void ExportToExcel(string filePath)
    {
        // Create a DataTable and define columns
        DataTable table = new DataTable();
        table.Columns.Add("DataSet_Fruits", typeof(string));

        // Adding rows to the DataTable
        table.Rows.Add("Apple");
        table.Rows.Add("Orange");
        table.Rows.Add("Strawberry");
        table.Rows.Add("Grapes");
        table.Rows.Add("Watermelon");
        table.Rows.Add("Bananas");
        table.Rows.Add("Lemons");

        // Create a workbook and set the format to XLS
        WorkBook workbook = WorkBook.Create(ExcelFileFormat.XLS);
        var worksheet = workbook.CreateWorkSheet("Sheet1");

        // Add the DataTable data to the worksheet
        int rowCount = 0;
        foreach (DataRow row in table.Rows)
        {
            worksheet["A" + (rowCount + 1)].Value = row[0].ToString();
            rowCount++;
        }

        // Save the workbook to the specified file path
        workbook.SaveAs(filePath);
    }
}

Imports IronXL
Imports IronXL.Formatting
Imports System.Data

Public Class ExcelExporter
	Public Shared Sub Main(ByVal args() As String)
		ExportToExcel("H:\test.xls")
	End Sub

	Public Shared Sub ExportToExcel(ByVal filePath As String)
		' Create a DataTable and define columns
		Dim table As New DataTable()
		table.Columns.Add("DataSet_Fruits", GetType(String))

		' Adding rows to the DataTable
		table.Rows.Add("Apple")
		table.Rows.Add("Orange")
		table.Rows.Add("Strawberry")
		table.Rows.Add("Grapes")
		table.Rows.Add("Watermelon")
		table.Rows.Add("Bananas")
		table.Rows.Add("Lemons")

		' Create a workbook and set the format to XLS
		Dim workbook As WorkBook = WorkBook.Create(ExcelFileFormat.XLS)
		Dim worksheet = workbook.CreateWorkSheet("Sheet1")

		' Add the DataTable data to the worksheet
		Dim rowCount As Integer = 0
		For Each row As DataRow In table.Rows
			worksheet("A" & (rowCount + 1)).Value = row(0).ToString()
			rowCount += 1
		Next row

		' Save the workbook to the specified file path
		workbook.SaveAs(filePath)
	End Sub
End Class

In the code sample above, we are exporting the DataTable to an Excel file. The DataTable is initialized and populated with data representing a list of fruits. We create a new Excel workbook with the WorkBook.Create() method and add the data from the DataTable to the workbook using a loop. Finally, the SaveAs() method is used to save the workbook to the specified file path.

Above is the output of the code sample that was run. Every piece of information from the data table has been separately added to the freshly created Excel sheet in the screenshot.

To learn more about the IronXL code example, click here.

Conclusion

Python's open-source Excel export modules enable developers to work effectively and efficiently on a broad range of tasks, including creating complicated Excel reports, processing significant information, and interfacing with legacy Excel formats. Developers may improve their productivity in Python-based applications and optimize their Excel export workflows by utilizing the capabilities and best practices of these packages.

IronXL is a potent substitute for Excel data export for C# developers, offering complete Excel compatibility, excellent performance, and smooth integration with the .NET framework. IronXL makes the process of exporting Excel documents in C# simpler with its user-friendly API and fine-grained control over Excel documents. This allows developers to create dynamic Excel reports, data visualizations, and more. C# developers may depend on IronXL to simplify Excel-related processes and enable the complete functionality of Excel within their C# programs, regardless of whether they are creating desktop, online, or mobile apps.

At launch, IronXL is available for $749. For updates and product assistance, users may also choose to pay a one-year membership fee. IronXL provides security for unrestricted redistribution for an extra charge. Click here to look up more approximate cost information. Go here to learn more about Iron Software.