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

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

Row Layout: Merged Headers, Borders, Banding

Crosstab and summary reports need structure above and across the data rows. Everything here is declared up front, per sheet:

from rustpy_xlsxwriter import FastExcel, Format

banner = Format().set_bold().set_align("center").set_background_color("#1F4E78")
under_header = Format().set_border_bottom("thin")

(
    FastExcel("crosstab.xlsx")
    .sheet(
        "Survey",
        rows,
        header_row=1,                                    # leave row 0 for banners
        merge_ranges=[(0, 1, 0, 2, "Gender", banner)],   # "Gender" spans B:C
        row_heights={0: 28, 1: 22},
        row_formats={1: under_header},                   # rule under the header
        banded_rows="#F2F2F2",                           # alternating fill
    )
    .save()
)

Option	Effect
`header_row`	0-based row for headers; data starts on the next row
`merge_ranges`	`(first_row, first_col, last_row, last_col, value[, format])`
`row_heights`	`{row_index: height}` in points
`row_formats`	`{row_index: Format}` — borders under headers, above totals
`banded_rows`	Background colour for every other data row
`autofilter`	Filter dropdowns over the header row and its data

autofilter=True sizes its own range from the rows actually written, so it follows header_row and needs no manual bounds:

FastExcel("report.xlsx").sheet("Data", rows, autofilter=True, freeze_row=1).save()

Formulas

Append computed columns. {row} becomes that row's sheet row, {first} the first data row:

(
    FastExcel("report.xlsx")
    .sheet(
        "Sales",
        rows,
        formula_columns={
            "total":   "=A{row}*B{row}",
            "running": "=SUM(B${first}:B{row})",
        },
    )
    .save()
)

The text goes to Excel unchanged, so anything Excel accepts works — nested calls, SUMIFS, INDEX/MATCH, cross-sheet references. Modern functions are handled for you: the 131 "future" functions (IFS, TEXTJOIN, MAXIFS, STDEV.P, …) get their required _xlfn. prefix, and the 30 dynamic-array ones (XLOOKUP, UNIQUE, SORT, FILTER, …) additionally get array-formula markup and the xl/metadata.xml part. Writing those by hand is the usual way to end up with a file Excel refuses to open.

totals_row values may also be raw formulas — a value starting with =, with {col} the column letter and {first}/{last} the data range:

totals_row={"qty": "sum", "price": "=ROUND(AVERAGE({col}{first}:{col}{last}),2)"}

Two things to know:

Structure is validated, names are not. Unbalanced parentheses or quotes and an empty formula raise at write time, naming the column and echoing the formula. Function names are not checked: LAMBDA and LET bind their own, workbooks carry user-defined functions, and Excel keeps adding to the list, so a whitelist would reject valid formulas. =NOTAFUNC(A1) therefore reaches the file and shows #NAME? in that cell.

For context, a malformed formula never corrupts the file — every case tested opens fine and shows an error value in the one cell. Validation just moves the discovery from "when someone opens the report" to "when the export runs", which is why it stops at the checks that cannot produce a false positive.

There is no {last} in formula_columns. Those cells are written while rows are still streaming, so the final row is unknown; the placeholder raises with that explanation. Use totals_row for whole-column formulas.

Totals Row

Aggregate formulas in a row below the data. The ranges are derived from the rows actually written:

(
    FastExcel("report.xlsx")
    .sheet(
        "Sales",
        rows,
        totals_row={"amount": "sum", "qty": "sum"},
        totals_label="Total",
        totals_format=Format().set_bold().set_border_top("thin"),
    )
    .save()
)
# amount column gets  =SUM(C2:C101)

Aggregates: sum, average (avg/mean), count, min, max, product, stdev. An unknown one raises rather than writing a broken formula. The row is skipped entirely when there are no data rows, since the range would be empty, and autofilter deliberately stops above it so sorting never drags the total into the data.

totals_format exists because the totals row index depends on how much data there was, so row_formats cannot reach it.

The formulas carry no computed result. This library does not evaluate them; Excel and LibreOffice do, on open. Readers that trust the cached value — pandas.read_excel, openpyxl with data_only=True — get None, not a number. That is deliberate: the cached result is written empty rather than left at the underlying crate's default of 0, which would look like a real total of zero. Use the totals row for files people will open, not for a machine-readable handoff.

Hyperlinks

Name the columns that hold links; the cell text stays the URL:

FastExcel("report.xlsx").sheet("Docs", rows, url_columns=["homepage"]).save()

Accepts what Excel accepts — http(s)://, mailto:, and internal:Sheet2!A1 to jump to another sheet. Anything Excel would reject (ordinary text, a blank, or a URL past its 2083-character limit) is written as plain text instead, so one stray value in a column of thousands never aborts the export. Links keep their banding and column format.

Ordering is enforced, not assumed. Sheets are written row by row and a row that has been flushed cannot be revisited — rust_xlsxwriter would drop a late merge_range with only a message on stderr. So a merge range that reaches header_row or below raises ValueError telling you what to raise header_row to, rather than silently losing the banner.

Banding is applied per cell, not per row. A cell carrying its own format ignores the row's, so shading a row with set_row_format leaves holes in exactly the columns that have a number format. banded_rows instead shades each cell, so float, integer, boolean, datetime and explicitly-formatted columns all stay banded. That costs roughly 20% write time; leave it off when you don't need it.

For write_worksheets, every one of these takes a dict keyed by sheet name (with a "general" fallback key).

String Deduplication

By default every sheet is written in constant-memory mode: strings go inline into the sheet XML and nothing is buffered. Passing dedupe_strings=True takes that sheet out of constant-memory mode and stores each distinct string once in the workbook's shared-string table instead.

(
    FastExcel("report.xlsx")
    .sheet("Events", events, dedupe_strings=True)   # lots of repeated text
    .sheet("Raw", raw_rows)                         # default: streamed
    .save()
)

Measured on 50k rows — the uncompressed XML shrinks a lot, but .xlsx is a zip and deflate already collapses repetition, so the on-disk win is modest and can even be negative:

Data	Uncompressed	On disk	Write time
Short repeats (4 distinct values)	−11%	+2%	1.7x
Long repeats (20 distinct, 120 chars)	−56%	−5%	1.1x
All-unique strings	+11%	−1%	1.5x
20 repeated text columns	−39%	−9%	1.1x

Worth enabling for sheets with many repeated long strings, or when the consumer parses the uncompressed XML. Not worth it for short categorical values. Off by default because it buffers the whole sheet in memory — measure on your own data before turning it on for a large export.

For write_worksheets, pass dedupe_strings as a dict keyed by sheet name (with a "general" fallback key).

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()

Note: dedupe_strings=True buffers the sheet, so it defeats the point of generator streaming. Leave it off for very large streamed exports.

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
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

CSV carries no formatting, so every Excel-only option is dropped — float_format, column_formats, header_format, freeze panes, merges, banding, row heights and formats, password, dedupe_strings. Only delimiter and sanitize_formulas apply. Switching a target from .xlsx to .csv therefore silently changes the output, so the builder warns and names what it discarded:

FastExcel("out.csv").format(float_format="0.00").sheet("S", rows).save()
# UserWarning: CSV/TSV output ignores Excel-only options: float_format. …

The data is still written correctly — only the styling is gone.

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.

Support

If this project saves you time, consider supporting its development via Saweria ☕ — or use the Sponsor button at the top of the repository.

License

This project is licensed under the MIT License.

Acknowledgements

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

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Download URL: rustpy_xlsxwriter-0.6.0.tar.gz
Upload date: Aug 3, 2026
Size: 109.3 kB
Tags: Source
Uploaded using Trusted Publishing? No
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SHA256	`1782ae8d3b1f5eb066ad3eb09180883fdd9043cf73678c96e06000fb47527917`
MD5	`25657d76f613140b4c667777ac07e9f7`
BLAKE2b-256	`4100f8eee2a89c05b5b58d3b12efca934f2d257e8b2c10e84e69dfcd904680a2`

Algorithm	Hash digest
SHA256	`329640b5f5d1b8f23f5367684f227dc50d2bac6b78695d645b9b325671484f30`
MD5	`8e2fa5f3e6febac86cdd468b18f7ce82`
BLAKE2b-256	`9111575d8c5976546d12ac75394c3c8eb9ac4ac46febc957510f2a6624845cc3`

Algorithm	Hash digest
SHA256	`d657e9eacdfb7ede38c56ea0efe1bc7ac40d4023fc78dcc771910043af5f320a`
MD5	`9f7362da1a142b36533447f3cc55ef6b`
BLAKE2b-256	`0451db2bb4aa911d534517b87f34e2db446abcaccf3b469cc322ca642fe43986`

Algorithm	Hash digest
SHA256	`0b817916657046872bdcfa602988d0caeffa1af627d4900fda66d9d70737f528`
MD5	`9ffff9eb074748fdd6f339dde254a8a2`
BLAKE2b-256	`c1f7183f46391442a7e90d748a95116d7cc9ff7d678ac327d87832d1b86ffcdf`

Algorithm	Hash digest
SHA256	`9cd1e7072d452f4daaf723d445f9f2f4d6f151957c39e0e0ac435dbb44fc6cc4`
MD5	`d3e2eda11475adb85e6592ec9e5da565`
BLAKE2b-256	`a0ad5040569e4b93c574ae539ecaed6ea975a78eea146ecd8805c01e4f186a66`

Algorithm	Hash digest
SHA256	`ee9e70cf29157e11d726b37f5f6b51acf742c86994ee54d369696c987bcbb5a3`
MD5	`b84279b0470165530c236ca5dd2d69be`
BLAKE2b-256	`0f2de73a94d362b3fd6685670e5779cbd2165dbdd065432c76081f05618839a4`

Algorithm	Hash digest
SHA256	`6bd3d675657baa76ef78429ad351ed8233f72d6ce6992d51e3b606b25ae018fe`
MD5	`afa69c1916d655dc9ca123a22c80c83f`
BLAKE2b-256	`37e6f9dab0cbd4ce8d7185fe9de85bcac333e6b2ed9fbd2b9a42fc9e8d5aa406`

rustpy-xlsxwriter 0.6.0

RustPy-XlsxWriter

Installation

Performance

Features

Quick Start

Usage Examples

Pandas & Polars DataFrames

Freeze Panes

Column widths

Cell formatting

Row Layout: Merged Headers, Borders, Banding

Formulas

Totals Row

Hyperlinks

String Deduplication

Generator Streaming

In-Memory Buffer (Web Frameworks)

CSV / TSV Output

Functional API

API Reference

FastExcel Class

Functional API

Supported Data Types

Examples

Testing

Contributing

Support

License

Acknowledgements

Project links

Key dates

1 maintainer

Credits

License

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

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`FastExcel` Class

Algorithm	Hash digest
SHA256	`986890b152987f8f793d6de6f2c35ec41becf0f3a00a85adb556b973ae5a6b87`
MD5	`20d41feab15454b8464ffbb23ccb2616`
BLAKE2b-256	`db8395169d10890d040847f4b8be8a152715d9e824b11636e9dc890ecb9c7f8d`

Algorithm	Hash digest
SHA256	`cc2108379b9c2f52b275c563e864e20426b6cbb8e0f70be1699729f390188646`
MD5	`63079d9d1a4dc91b61d98ed2cbdd23b4`
BLAKE2b-256	`2b8c12c686bdce8035c8d1d95e6bd23b80a3f7b4e75bb0c16042f94c96769254`

Algorithm	Hash digest
SHA256	`9216c8b44226734bb381e7bb58b970305d85c63965c13615fdb582019a3090f8`
MD5	`f128800871b2d0668230293d6bbc30dc`
BLAKE2b-256	`e2c8d9a4ab2bd972fa9e54ad7b61c4cae347a4f171d28a413f00173ea80a2cf7`

Algorithm	Hash digest
SHA256	`63390e3a06062dfd177e2f4fbba4fe15cbdcefd678d9b69cebf417f2e8dcfafc`
MD5	`1ffd0fb84d94d5f04ab81a848c433427`
BLAKE2b-256	`1dbe30bf0e2bbdcc3776a5a8668e82ffcc9c1b8a847c0e3ff0d3f532b2ef2345`

Algorithm	Hash digest
SHA256	`81e528eeb90bd5bc370f36de92621f24a2d39870da90b429bd44bb4005e7cf1f`
MD5	`b2f16dd6b399d7b520bb226658aeaa68`
BLAKE2b-256	`62e5157171208a3d7d202f4e1d85f9e9f2112bfb35f45467b088559006448358`

Algorithm	Hash digest
SHA256	`81d9f05935831a5245367e1f0eb8858faea41acaa09472d7d249bdd8e5eae6de`
MD5	`80f304dd386d4f8ccdb55ee97fbf0f2a`
BLAKE2b-256	`b9a9479724a10da56b893ab0e92387e64f6104a53503d90ddb14b1c0037432b3`

Algorithm	Hash digest
SHA256	`696077150b68db47cfb0c631155fb6f1e48447d0b839a2bfdbef6572d2105a35`
MD5	`fb700245280a6ba3320371e83767824f`
BLAKE2b-256	`9cd2c881f040b21b74dfdd764873d8211ea42acea57d196cfa1e859bc7056a2a`

Algorithm	Hash digest
SHA256	`2129ce0071142b8b1818c4287c6ee3e037f359a2f78af7c8dab034197eb306ca`
MD5	`3a9f70aa23fd7390ab055b6a19759e7b`
BLAKE2b-256	`15e6196ea3f701ae1db44a931e104c02f0702197c433653dca1bd6e2e6c73a4e`

Algorithm	Hash digest
SHA256	`e53b25b69c786c85e308a3e2bb781e8ff3f896c507c351e61d401b93a79610a0`
MD5	`0a2e2a33cdbf21a93f4281e16f3be1d9`
BLAKE2b-256	`8ac15b7cc3ef895d3ef14d49f00ce9644a46cdf84a844aa057b958eed7fb7dd7`

Algorithm	Hash digest
SHA256	`76bb71debd01ebd6e994d846d52df89adec2761bd16a23a606ca0ac2908fbca5`
MD5	`77fc02984044da3d572398406d759f28`
BLAKE2b-256	`ce81c4bf9d67f2f2d4ed096ddb75a620ff2842ebd007c6730768d1ebbd90e8b2`