How to Export to Excel in Python

Introduction

Python's flexibility also includes its smooth integration with Excel, a popular spreadsheet program. It provides developers with several choices for effectively using Python export 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 Excel target file name.

How to Export Data to Excel using Python

1. Bring in the required libraries.
2. Get your data ready or retrieved.
3. Make a Workbook or DataFrame object for exporting data.
4. Fill the item with your information.
5. Using the proper technique, save the object to an Excel file.
6. Close the file, if desired, or carry out any further actions.

Pandas

Pandas is a potent Python package for handling data analysis and missing data representation. Support for exporting data to Excel is one of its numerous functions. Pandas offers a simple way to export Data Frames to Excel files using the to_excel() function output file stored in the Excelwriter object file path. Also, we can load an existing file with the help of import pandas as pd. 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 because of its interaction with other data processing features.

OpenPyXL

A package called OpenPyXL was created expressly for using Excel files with Python code. OpenPyXL functions 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 formulae, charts, and formatting features, this package is well-suited. Even while 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 forms 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 jobs.

• 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, formulae, 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 interface.

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

 pip install IronXL

The file is ready for usage and is now downloading and installing 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, as seen in the code screenshot below. Once IronXL is presented, we may utilize its classes and methods in our code.

Excel files may be created using IronXL and subsequently transformed into workbook objects. Once they are objects, we may work with them in a variety of ways. The sample code below generates an Excel file by converting a Datatable into an Excel worksheet.

using IronXL;
using IronXL.Options;
using System.Data;
static void main(String [] arg)
{
    exporttoexcel("H:\\test.xls");
}
public static void exporttoexcel(string filepath)
{
    DataTable table = new DataTable();
    table.Columns.Add("DataSet_Fruits", typeof(string));
    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");
    WorkBook wb = WorkBook.Create(ExcelFileFormat.XLS);
    var writer = wb.DefaultWorkSheet;
    int rowCount = 1;
    foreach (DataRow row in table.Rows)
    {
        writer ["A" + (rowCount)].Value = row [0].ToString();
        rowCount++;
    }
    wb.SaveAs(filepath);
}

using IronXL;
using IronXL.Options;
using System.Data;
static void main(String [] arg)
{
    exporttoexcel("H:\\test.xls");
}
public static void exporttoexcel(string filepath)
{
    DataTable table = new DataTable();
    table.Columns.Add("DataSet_Fruits", typeof(string));
    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");
    WorkBook wb = WorkBook.Create(ExcelFileFormat.XLS);
    var writer = wb.DefaultWorkSheet;
    int rowCount = 1;
    foreach (DataRow row in table.Rows)
    {
        writer ["A" + (rowCount)].Value = row [0].ToString();
        rowCount++;
    }
    wb.SaveAs(filepath);
}

In the code sample above, we are exporting the data table to an Excel file. After a DataTable has been established, column headers are produced. Once the first column is established, we add the rows one at a time. After adding the rows and columns to the DataTable object, we construct the workbook object. The workbook object may be used to add data to an Excel sheet, after which the sheet can be stored somewhere else. We are building the worksheet object, which we can add to the workbook object, to generate worksheets.

Before adding the value to the worksheet, we use a foreach loop to read each value from the DataTable. The SaveAs() function is used to save (create a new Excel file) the data into an Excel file once they have all been put into the worksheet. Also, we can load the existing Excel file with the help of the Worksheet.Load() method.

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 big 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 speed, 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 IronSoftware.