Developers often need to export a Pandas DataFrame to Excel. Excel is an excellent tool for sharing and reviewing reports. Python with Pandas makes this simple, but using Aspose.Cells for Python gives you more control. You can convert a DataFrame to Excel directly, through CSV, via JSON, or even export multiple DataFrames into one file. In this article, you will learn how to convert a Pandas dataframe to Excel with four methods step by step.

Why Use Aspose.Cells for Pandas to Excel

Pandas has a built-in to_excel() function. It works for basic exports but is limited in features. Aspose.Cells for Python is one of the best Python Excel libraries for developers provides a complete Excel engine. It lets you save DataFrames to Excel with high reliability. You can also work with charts, formulas, formatting, and large files.

This article will show you how to export a Pandas DataFrame to Excel using Aspose.Cells in different ways.

Before getting started, make sure you have the following installed:

  1. Download Aspose.Cells for Python from releases or install with pip:
pip install aspose-cells-python
  1. Pandas – install with pip:
pip install pandas

These two libraries will allow you to convert Pandas DataFrames into Excel files.

Method 1: Convert DataFrame to Excel Directly with Aspose.Cells

The most common task is to export a Pandas DataFrame to Excel directly. With Aspose.Cells, you can create a workbook, import the DataFrame values, and save it as an Excel file.

Follow the steps below to convert DataFrame to Excel:

  1. Build a sample dataset.
  2. Initialize an empty Excel workbook.
  3. Access the first sheet in the workbook.
  4. Insert DataFrame column names into Excel cells.
  5. Loop through each row in the DataFrame and put values in cells.
  6. Export the final workbook to an Excel file.

The following code example shows how to convert a Pandas to Excel directly:

Convert DataFrame to Excel Directly with Aspose.Cells

Convert DataFrame to Excel Directly with Aspose.Cells.

This code saves the Pandas DataFrame to Excel at the given location. You can change the path to match your system.

This method gives you full control over how Pandas DataFrame data is written to Excel. It is the most direct way to convert a DF to Excel using Aspose.Cells.

Method 2: Convert DataFrame to Excel via CSV

Another simple way is to save your Pandas DataFrame as CSV first, then convert that CSV file into Excel using Aspose.Cells. This is useful when your process already generates CSV output and you still need a clean Excel file.

Please follow these steps to convert DataFrame to Excel via CSV:

  1. Build sample data.
  2. Save DataFrame as CSV using the to_csv() method from Pandas.
  3. Open the CSV as a workbook.
  4. Export the workbook to .xlsx file.

The following code example shows how to convert Pandas to Excel via CSV:

This approach first creates a CSV file, then converts it into Excel. It ensures that your Pandas to Excel conversion works even if your pipeline already relies on CSV files.

Use this method when you want to go from CSV to Excel quickly while keeping the flexibility of Pandas and Aspose.Cells.

Method 3: Convert DataFrame to Excel via JSON

Many APIs use JSON. You can also pass JSON to Aspose.Cells to build an Excel sheet. This method converts a Pandas DataFrame to JSON first, then loads that JSON into Excel as a table. It keeps your pandas to Excel export clean and reliable. It also helps when you want strict control over headers and data types.

Please follow the steps below:

  1. Build sample data for the demo.
  2. Call the df.to_json(orient='records') to convert.
  3. Initialize the Workbook() class object and get the first worksheet.
  4. Enable array_as_table for proper tabular import.
  5. Call the JsonUtility.import_data() method to import JSON into worksheet cells.
  6. Write the final file to the .xlsx.

The following code example shows how to convert a Pandas DF to Excel via JSON:

The orient='records' produces a list of objects. Each object maps keys to column names. array_as_table=True tells Aspose.Cells to treat the array as a proper table with headers. The importer writes values into cells starting at A1. This gives you a predictable layout in Excel.

Method 4: Export Multiple DataFrames to Excel Sheets

You can write more than one Pandas DataFrame to a single Excel file. Each DataFrame goes to its own sheet. This method gives you a clean pandas to Excel export for reports and grouped tables.

Please follow the step below:

  1. Create a function that writes many DataFrames to many sheets.
  2. Initialize a new workbook.
  3. Add a sheet for each DataFrame and name it from sheet_names.
  4. Put column names in the first row.
  5. Loop through DataFrame tuples and write cell values.
  6. Export as XLSX with the workbook.save() method.

The following code example shows how to export multiple Pandas DataFrames to Excel sheets:

Export Multiple Pandas DataFrames to Excel Sheets with Aspose.Cells

Export Multiple Pandas DataFrames to Excel Sheets with Aspose.Cells.

The function pairs each DataFrame with a sheet name. It writes headers at row 0 and data from row 1. Aspose.Cells writes values to cells with strong typing. The final XLSX file keeps your tables clear and ready to share.

Get a Free License

Evaluate Aspose.Cells for Python via .NET without limits. Request a free temporary license from the license page. Apply it in your code to remove evaluation restrictions. Test every feature, including DF to Excel, charts, formulas, and large files.

PD to Excel: Free Resources

Please use these resources to enhance your knowledge and improve your understanding.

Conclusion

You have learned various ways to export a Pandas DataFrame to Excel with Aspose.Cells for Python. You can save a DataFrame directly, use CSV, use JSON, or write multiple DataFrames into one file. Each method is simple, fast, and reliable. If you are looking for advanced Excel features beyond Pandas, Aspose.Cells is the right choice. Try it in your next project and move from Pandas to Excel with full control.

If you have any questions, visit our free support forum. We will be glad to assist you.

See Also