python openpyxl vs xlsxwriter: Choosing the Right Excel Library

python openpyxl vs xlsxwriter requires a clear understanding of the core syntax, runtime behavior, and practical implementation patterns demonstrated in the examples below.

When you need to generate Excel files from Python, two libraries dominate: openpyxl and xlsxwriter. Both can produce .xlsx files, but they take different approaches. openpyxl can read and write Excel files, while xlsxwriter is write-only. That single difference drives most of the decision.

Core Design Differences

openpyxl loads the entire workbook into memory. This allows you to read existing files, modify cells, and write new files. Because the workbook is an in-memory object graph, you can inspect and manipulate any part of it before saving. xlsxwriter, on the other hand, streams data directly to a file. It never builds a full in-memory representation, so it cannot read or modify existing workbooks. This streaming design gives xlsxwriter a predictable memory footprint and often better performance for large write-only workloads.

Feature Comparison

The two libraries overlap heavily for writing features, but openpyxl has the additional read and modify capability. Here is a high-level comparison:

Both libraries support cell styles, merged cells, conditional formatting, data validation, and most Excel features you would expect. The key differentiator is that openpyxl can round-trip an existing file, while xlsxwriter always starts from a blank sheet.

Performance and Memory Behavior

Because openpyxl keeps the entire workbook in memory, memory usage grows with the size of the data. For a few thousand rows this is rarely a problem, but when you are generating files with hundreds of thousands of rows, the object graph can consume significant RAM. xlsxwriter writes each row as it is processed, so memory usage stays low and flat regardless of the total data volume. This also means xlsxwriter often finishes faster for large exports because it avoids the overhead of building and then serializing a large in-memory structure.

There is no universal benchmark that favors one side; the right choice depends on whether you need to read or modify data. If you only need to write, xlsxwriter is usually the safer bet for large files. If you need to update an existing workbook, openpyxl is the only option.

Practical Example: Writing a Workbook with Both

Here is a simple example that writes a header row and a few data rows with basic formatting.

openpyxl

from openpyxl import Workbook
from openpyxl.styles import Font

wb = Workbook()
ws = wb.active
ws.title = "Products"

headers = ["Name", "Price", "Quantity"]
ws.append(headers)
for cell in ws[1]:
    cell.font = Font(bold=True)

rows = [
    ("Widget", 9.99, 100),
    ("Gadget", 14.49, 50),
]
for row in rows:
    ws.append(row)

wb.save("products_openpyxl.xlsx")

openpyxl uses an append-style API. The workbook is built in memory, and you save it at the end. This works well when you need to manipulate cells after writing them.

xlsxwriter

import xlsxwriter

workbook = xlsxwriter.Workbook("products_xlsxwriter.xlsx")
worksheet = workbook.add_worksheet("Products")

headers = ["Name", "Price", "Quantity"]
bold = workbook.add_format({"bold": True})

for col, header in enumerate(headers):
    worksheet.write(0, col, header, bold)

rows = [
    ("Widget", 9.99, 100),
    ("Gadget", 14.49, 50),
]
for row_idx, row in enumerate(rows, start=1):
    for col_idx, value in enumerate(row):
        worksheet.write(row_idx, col_idx, value)

workbook.close()

xlsxwriter uses a cell-based write API. You must close the workbook to flush the file. There is no in-memory copy, so you cannot revisit or modify cells after writing them.

When to Choose openpyxl

Choose openpyxl when you need to:

• Read data from an existing .xlsx file.
• Modify specific cells, styles, or formulas in an existing workbook.
• Work with the workbook as a data structure before saving.
• Use features that require reading, such as copying sheets or merging workbooks.

openpyxl is also a good choice for small to medium files where memory is not a concern and you want the flexibility of a full in-memory model.

When to Choose xlsxwriter

Choose xlsxwriter when you only need to write new files and:

• You are generating large datasets and want to keep memory usage low.
• You want a simpler, write-only API that is easy to reason about.
• You need to stream data from a generator or a database cursor without holding it all in memory.
• You want to avoid the overhead of building an entire workbook object graph.

xlsxwriter is particularly well-suited for server-side report generation where the output file is written to disk or sent as a download.

Edge Cases and Limitations

One common mistake is trying to use xlsxwriter to append to an existing file. It cannot do that. If you need to append, you must load the file with openpyxl, add the data, and save again. Similarly, openpyxl can be memory-heavy for very large files. If you hit memory limits, consider xlsxwriter or using openpyxl in read-only mode (which only helps when reading, not writing).

Both libraries write formulas as strings; they do not evaluate them. Excel calculates the results when the file is opened. If you need calculated values, you must compute them in Python and write the results as plain values.

Charts and images work in both libraries, but the APIs differ. If you are porting code from one library to the other, expect to rewrite the chart and image sections because the object models are not compatible.

Making the Decision in Practice

The practical rule is simple: if you need to read or modify an existing workbook, use openpyxl. If you are generating a new file from scratch and care about memory or speed, use xlsxwriter. For small files, either works, so choose based on which API feels more natural to you. For large exports, xlsxwriter's streaming model is usually the better engineering choice because it keeps memory usage flat and avoids the overhead of building a full in-memory workbook.

When you do choose openpyxl for a large write-only job, you can mitigate memory usage by writing rows in batches and periodically clearing the in-memory structures, but that adds complexity. xlsxwriter avoids that problem entirely by design.