rustpy-xlsxwriter 0.5.2

High-performance Excel and CSV file generation powered by Rust (~7-9x faster)

RustPy-XlsxWriter

High-performance Excel and CSV file generation for Python, powered by Rust. ~7x-9x faster than XlsxWriter, ~5x faster CSV (records) than Python's csv module, and ~12x faster Pandas DataFrame → CSV than pandas.to_csv via zero-copy Arrow.

from rustpy_xlsxwriter import FastExcel

FastExcel("report.xlsx").sheet("Sheet1", records).save()

Installation

pip install rustpy-xlsxwriter

Performance

Benchmarked via benchmark.py — run python benchmark.py to reproduce:

Output	Input type	Records	RustPy	Baseline	Speedup
Excel	Records (list of dicts)	500K	~2.99s	~26.72s	8.9x
		1M	~5.94s	~51.92s	8.7x
	Pandas DataFrame	500K	~1.21s	~9.11s	7.6x
		1M	~2.41s	~18.17s	7.5x
	Polars DataFrame	500K	~1.20s	~8.59s	7.1x
		1M	~2.42s	~17.07s	7.1x
CSV	Records (generator)	500K	~0.16s	~0.77s	4.8x
		1M	~0.32s	~1.53s	4.8x
	Pandas DataFrame	1M	—	—	~12x†
	Polars DataFrame	1M	—	—	(use Polars' native write_csv — already faster)†

Baselines: Excel → Python xlsxwriter; Records CSV → Python csv module; Pandas DataFrame CSV → DataFrame.to_csv().

† DataFrame → CSV rows measured on a separate machine — only speedup ratio shown. The Pandas path goes through zero-copy Arrow C Data Interface.

Key optimizations

1. Arrow zero-copy for DataFrames — reads memory buffers directly via Arrow C Data Interface (Excel and CSV paths)
2. First-row type caching for Records — detect column types once, skip type cascade
3. LTO (Link-Time Optimization) and single codegen unit
4. Constant memory mode for large files
5. Pre-allocated Format objects (created once, reused across all cells)
6. Dict values() iteration instead of per-key hash lookups
7. Lazy processing of Python iterables (including generators)
8. High-precision floating point with ryu
9. Efficient zlib compression

Features

Data Sources

• List of dicts, generators/iterators, Pandas DataFrame, Polars DataFrame
• All Python types: str, int, float, bool, None, datetime, date
• Numpy scalar types (numpy.int64, numpy.float64, numpy.bool_)

Formatting & Styling

• Float number format (e.g. "0.00")
• Custom datetime format (e.g. "dd/mm/yyyy")
• Bold headers and bold index columns
• Freeze panes (rows, columns, per-sheet overrides)

Output Options

• .xlsx (Excel) — auto-detected from file extension
• .csv / .tsv — auto-detected; ~5x faster than Python csv (records), ~12x faster than pandas.to_csv (Pandas DataFrame, via Arrow zero-copy)
• io.BytesIO in-memory buffer
• Password protection (Excel only)
• Optional column auto-fit (autofit=True/False)
• Multiple sheets in a single file (Excel only)

API

• Fluent builder via FastExcel class
• Context manager (with statement) for auto-save
• Lower-level functional API (write_worksheet, write_worksheets)

Quick Start

from rustpy_xlsxwriter import FastExcel

# Simple
FastExcel("output.xlsx").sheet("Users", [{"Name": "Alice", "Age": 30}]).save()

# Full-featured with context manager
with FastExcel("report.xlsx", password="secret") as f:
    f.format(
        float_format="0.00",
        datetime_format="dd/mm/yyyy",
        bold_headers=True,
        index_columns=["ID"],
    )
    f.freeze(row=1)
    f.sheet("Employees", employee_records)
    f.sheet("Departments", dept_records)

Usage Examples

Pandas & Polars DataFrames

import pandas as pd
import polars as pl
from rustpy_xlsxwriter import FastExcel

# Pandas — Arrow zero-copy, dtype-aware
df_pd = pd.DataFrame({"Name": ["Alice", "Bob"], "Score": [88.5, 92.3]})
FastExcel("pandas.xlsx").sheet("Data", df_pd).save()

# Polars — native support, no .to_pandas() needed
df_pl = pl.DataFrame({"Name": ["Alice", "Bob"], "Score": [88.5, 92.3]})
FastExcel("polars.xlsx").sheet("Data", df_pl).save()

Freeze Panes

# Freeze header row on all sheets
FastExcel("frozen.xlsx").freeze(row=1).sheet("Sheet1", data).save()

# Per-sheet freeze configuration
(
    FastExcel("custom.xlsx")
    .freeze(row=1)                             # all sheets
    .freeze(row=1, col=2, sheet="Details")     # override for Details
    .sheet("Summary", summary_data)
    .sheet("Details", detail_data)
    .save()
)

Column widths

Override autofit with explicit widths (Excel character units):

from rustpy_xlsxwriter import FastExcel

(
    FastExcel("out.xlsx")
    .sheet("RawData", rows, column_width=15)                    # uniform
    .sheet("Meta", meta, column_widths={"row": 7, "var": 22})   # per-column (by name)
    .sheet("Other", rows, column_widths=[7, 22, 40])            # per-column (positional)
    .save()
)

column_widths overrides column_width per named/positional column, and explicit widths win over autofit=True. Unknown column names and out-of-range list indices emit a warning and are skipped. For write_worksheets, pass column_width / column_widths as dicts keyed by sheet name (with a "general" fallback key).

Cell formatting

Build a reusable Format and attach it per column or to the header row:

from rustpy_xlsxwriter import FastExcel, Format

money = Format().set_num_format("$#,##0.00").set_font_color("#006600")
header = Format().set_bold().set_background_color("#1F4E78").set_font_color("white")

(
    FastExcel("out.xlsx")
    .sheet("Products", rows, header_format=header, column_formats={"price": money})
    .save()
)

Format chains setters (font, fill, border, alignment, number format). Colors accept "#RRGGBB" or names ("red"); enum-valued setters take lowercase strings (set_align("center"), set_border("thin")). A column's format wins over float_format / datetime_format. For write_worksheets, pass column_formats / header_format as dicts keyed by sheet name (with a "general" fallback key).

Note: a column format wins entirely over the automatic number format. On a date/datetime column, a Format without set_num_format(...) makes cells show the raw Excel serial number — chain .set_num_format("yyyy-mm-dd") (or similar) to keep a date display.

Generator Streaming

def rows():
    for i in range(1_000_000):
        yield {"id": i, "value": f"row_{i}"}

FastExcel("streamed.xlsx").sheet("Data", rows()).save()

In-Memory Buffer (Web Frameworks)

import io
from rustpy_xlsxwriter import FastExcel

buf = io.BytesIO()
FastExcel(buf).sheet("Sheet1", records).save()
xlsx_bytes = buf.getvalue()  # send as HTTP response

CSV / TSV Output

# Auto-detected from file extension — same API, no code change
FastExcel("output.csv").sheet("Sheet1", records).save()
FastExcel("output.tsv").sheet("Sheet1", records).save()

# Or use write_csv directly
from rustpy_xlsxwriter import write_csv

write_csv(records, "output.csv")
write_csv(records, "output.csv", delimiter=";")  # custom delimiter

Functional API

from rustpy_xlsxwriter import write_worksheet, write_worksheets

write_worksheet(records, "output.xlsx", sheet_name="Sheet1", password="secret")

write_worksheets(
    [("Sheet1", records1), ("Sheet2", records2)],
    "output.xlsx",
    freeze_panes={"general": {"row": 1, "col": 0}},
)

API Reference

FastExcel Class

Method	Description
FastExcel(target, *, password=None, autofit=True)	Create writer for file path or BytesIO buffer
.format(*, float_format, datetime_format, index_columns, bold_headers)	Set number/datetime format and styling
.freeze(*, row=None, col=None, sheet=None)	Configure freeze panes (general or per-sheet)
.sheet(name, data)	Add a worksheet (list of dicts, generator, or DataFrame)
.save()	Write all sheets and save

Supports context manager (with statement) — auto-saves on exit, skips save on exception.

Functional API

Function	Description
write_worksheet(records, file_name, ...)	Write single Excel sheet
write_worksheets(records_with_sheet_name, file_name, ...)	Write multiple Excel sheets
write_csv(records, file_name, delimiter=",")	Write CSV/TSV file
validate_sheet_name(name)	Check if sheet name is valid for Excel

Supported Data Types

Python Type	Excel Output
str	Text
int	Number
float	Number (with optional format)
bool	Boolean
None	Empty cell
datetime.datetime	DateTime (with optional format)
datetime.date	Date (with optional format)
numpy.int64 / numpy.float64	Number
numpy.bool_	Boolean
dict, other	String representation

Examples

See examples/ for 16 runnable scripts + a Jupyter notebook:

File	Description
01_basic.py	Single sheet from list of dicts
02_multiple_sheets.py	Multiple sheets in one file
03_dataframe.py	Pandas DataFrame with styling
04_freeze_panes.py	Freeze rows, columns, per-sheet config
05_bytesio.py	In-memory buffer for web frameworks
06_generator.py	Memory-efficient streaming (100K rows)
07_password.py	Password-protected workbook
08_full_featured.py	All features combined
09_polars.py	Polars DataFrame (native support)
10_context_manager.py	Auto-save with with statement
11_datetime_format.py	Custom datetime/date formatting
12_bold_headers.py	Bold header row
13_autofit.py	Column auto-fit toggle
14_csv_tsv.py	CSV/TSV output (~5x faster)
15_column_widths.py	Uniform & per-column widths
16_cell_formats.py	Cell formatting (Format class)
quickstart.ipynb	Jupyter notebook walkthrough

Testing

# Unit tests (~1 second)
pytest tests/ -m "not benchmark"

# All tests including benchmarks
pytest tests/

# Benchmark only
python benchmark.py

Test structure (86 tests)

File	Coverage
test_metadata.py	Package metadata functions
test_validation.py	Sheet name validation (unicode, length, special chars)
test_write_single.py	Single sheet: all types, generator, context manager, autofit
test_write_multi.py	Multiple sheets
test_write_functional.py	Functional API
test_freeze_panes.py	Freeze panes (single & multi-sheet)
test_password.py	Password protection
test_bytesio.py	In-memory buffer I/O
test_dataframe.py	Pandas DataFrame, numpy scalar types
test_polars.py	Polars DataFrame: types, datetime, date, null, styling
test_styling.py	Float format, datetime format, bold headers, index columns
test_benchmark.py	Performance benchmarks (Records + Pandas + Polars vs xlsxwriter)

Contributing

Contributions are welcome! Please submit issues or pull requests on the GitHub repository.

License

This project is licensed under the MIT License.

Acknowledgements

This project is powered by rust_xlsxwriter, PyO3, and maturin.