python xlsxwriter vs openpyxl: Choosing the Right Excel Library
python xlsxwriter vs openpyxl: Compare xlsxwriter and openpyxl for Python Excel workflows: API design, performance, formatting, and when to use each.
python xlsxwriter vs openpyxl requires a clear understanding of the core syntax, runtime behavior, and practical implementation patterns demonstrated in the examples below.
When a Python project needs to produce Excel files, xlsxwriter and openpyxl are the two libraries most developers evaluate. Both generate .xlsx files, but they take fundamentally different approaches to writing, reading, and formatting data. Understanding those differences matters because the choice affects memory usage, file size, and how much code you need to maintain.
Core Differences in API Design
The most visible difference is that xlsxwriter is write-only. It creates new workbooks and writes data into them, but it cannot read or modify an existing file. openpyxl, on the other hand, can load an existing workbook, change cells, and save it back. This single distinction drives most other design choices.
With xlsxwriter, you create a workbook and add worksheets, then write cell by cell or row by row:
import xlsxwriter workbook = xlsxwriter.Workbook('report.xlsx') worksheet = workbook.add_worksheet() worksheet.write('A1', 'Quarter') worksheet.write('B1', 'Revenue') worksheet.write(1, 0, 'Q1') worksheet.write(1, 1, 12000) workbook.close()
With openpyxl, you can do the same, but you also have the option to load an existing file:
from openpyxl import Workbook wb = Workbook() ws = wb.active ws['A1'] = 'Quarter' ws['B1'] = 'Revenue' ws.append(['Q1', 12000]) wb.save('report.xlsx')
Both APIs are straightforward, but openpyxl uses a more object-oriented cell model where you assign values to cell objects, while xlsxwriter uses explicit write() methods. This difference becomes more pronounced when you handle formulas, formatting, and merged cells.
Writing Data: Performance and Memory Behavior
The libraries differ significantly in how they build the final file. xlsxwriter writes data to the file in a streaming fashion. It constructs the XML parts of the .xlsx archive as you call write(), keeping only a minimal amount of data in memory. This makes it a strong choice for generating very large files where memory is a concern.
openpyxl keeps the entire workbook in memory. When you modify a cell, it updates an in-memory representation of the worksheet. When you call save(), it serializes the whole workbook to disk. This approach is necessary for reading and modifying existing files, but it means memory usage grows with the number of cells and the amount of formatting.
For a one-time script that writes a million rows, xlsxwriter will use a fraction of the memory that openpyxl requires. The tradeoff is that openpyxl can handle round-trip workflows: load, edit, save. If you never need to read an existing file, xlsxwriter is often the better fit for large data dumps.
Formatting and Styling Capabilities
Both libraries support cell formatting, but their feature sets overlap only partially. xlsxwriter is known for its rich formatting options: conditional formatting, data bars, color scales, sparklines, and a wide range of number formats. It also supports charts and images., and and is generally more complete for generating polished reports from scratch.
openpyxl also supports formatting, charts, and images, but its conditional formatting and some advanced features are less extensive. However, because openpyxl can read existing formatting, it is the only choice when you need to preserve or update styles in a workbook that was created elsewhere.
For example, to apply a currency format in xlsxwriter:
currency_format = workbook.add_format({'num_format': '$#,##0'}) worksheet.write('B2', 12000, currency_format)
In openpyxl, the equivalent is:
from openpyxl.styles import numbers ws['B2'] = 12000 ws['B2'].number_format = '$#,##0'
The APIs differ, but both give you control over the final appearance. The deciding factor is whether you need to read existing formatting or whether you are generating a fresh file.
Reading and Modifying Existing Workbooks
openpyxl is the clear winner when you need to read an .xlsx file, change a few cells, and save it back. Its load_workbook() function parses the file and gives you access to worksheets, cells, and styles. xlsxwriter cannot do this at all.
from openpyxl import load_workbook wb = load_workbook('existing.xlsx') ws = wb.active ws['A1'] = 'Updated value' wb.save('existing_modified.xlsx')
This capability is essential for automated reporting that must fill in a pre-designed template. If your workflow starts with a template that contains formulas, logos, and formatting, openpyxl is the only viable option among these two.
Handling Large Datasets
When generating very large files, the streaming behavior of xlsxwriter becomes a practical advantage. You can write rows in a loop without accumulating the entire dataset in memory. openpyxl also has a write-only mode (write_only=True) that reduces memory usage, but it is less flexible than the normal mode and still does not match xlsxwriter's streaming efficiency for pure data generation.
Consider a scenario where you need to export a database table with hundreds of thousands of rows. With xlsxwriter, you can iterate over the query result and write each row directly:
import xlsxwriter workbook = xlsxwriter.Workbook('export.xlsx') worksheet = workbook.add_worksheet() for row_idx, row in enumerate(query_result): worksheet.write_row(row_idx, 0, row) workbook.close()
With openpyxl in write-only mode, the code looks similar but the library still builds the workbook structure in memory until you call save(). For extremely large files, xlsxwriter tends to produce smaller files as well because it does not retain all cell data for post-processing.
Choosing Based on Your Use Case
The decision between xlsxwriter and openpyxl should be driven by whether you need to read existing workbooks and by the size of the data you generate.
Use xlsxwriter when you are creating new files from scratch, especially if you are generating large datasets or need advanced formatting like conditional formatting and sparklines. Its streaming model keeps memory usage low and its formatting API is comprehensive.
Use openpyxl when you must read or modify an existing .xlsx file. This includes filling in templates, updating specific cells, or preserving the original formatting. It is also a reasonable choice for small to medium files where memory is not a concern.
For a project that requires both reading and writing, you might end up using both libraries: openpyxl to load and modify a template, and xlsxwriter to generate a large data sheet that will be merged later. This is a common pattern in reporting pipelines.
Compatibility and File Format Details
Both libraries generate .xlsx files that are compatible with Excel 2007 and later. They also support .xlsm for macro-enabled workbooks, though xlsxwriter cannot write macros itself; it can only preserve them if you copy a file that already contains them. openpyxl can load and save .xlsm files, making it the better choice when you need to manipulate macro-enabled workbooks.
Another subtle difference is how the libraries handle formulas. xlsxwriter writes formulas as strings and does not calculate their results; it leaves that to Excel when the file is opened. openpyxl can also write formulas, but it also has the ability to read cached formula results from an existing file. If your workflow relies on reading calculated values, openpyxl gives you that access.
These compatibility details matter when you are integrating with systems that consume the files programmatically, such as data pipelines that parse values without opening Excel.
Final Code Example: A Practical Comparison
To see the two libraries side by side, consider a simple task: create a workbook with a header row, a few data rows, and a sum formula. Here is the xlsxwriter version:
import xlsxwriter workbook = xlsxwriter.Workbook('sales.xlsx') ws = workbook.add_worksheet('Sales') headers = ['Product', 'Units', 'Price']] ws.write_row(0, 0, headers) data = [['Widget', 10, 5.5], ['Gadget', 7, 9.0]] for r, row in enumerate(data, start=1): ws.write_row(r, 0, row) ws.write(4,, 0, 'Total') ws.write(4, 2, '=SUM(C2:C3)') workbook.close()
And the openpyxl equivalent:
from openpyxl import Workbook wb = Workbook() ws = wb.active ws.title = 'Sales' headers = ['Product', 'Units', 'Price'] ws.append(headers) data = [['Widget', 10, 5.5], ['Gadget', 7, 9.0]] for row in data: ws.append(row) ws['A5'] = 'Total' ws['C5'] = '=SUM(C2:C3)' wb.save('sales.xlsx')
Both produce a valid .xlsx file. The xlsxwriter version uses explicit row indices, while openpyxl uses append() which is more convenient when you are adding rows sequentially. The choice is not about which syntax is better, but about the broader capabilities you need beyond this simple case.
When you are building a new reporting system, evaluate your data volume and whether you need to read existing files. Those two factors will point you to the right library more reliably than any feature list.