Python XLSB/XLSX/XLSM Reader & Writer

A Python library for reading and writing XLSB and XLSX files efficiently, with existing-workbook updates for macro-enabled XLSM packages.

Installation

pip install xlspy

Usage

Basic Example

from xlspy import XlsbWriter
import datetime
from decimal import Decimal

data = [
    ["Name", "Age", "City", "info"],
    [-123, 2147483647, 2147483648, 2147483999],
    ["x", "y", "z", datetime.datetime.today()],
    ["Alice", 25, "New York", datetime.date.today()],
    ["Bob", 30, "London", Decimal(3.14)],
    ["Charlie", 35, "Paris", datetime.datetime.now()],
    [True, False, None, datetime.datetime.utcnow()]
]

# Initialize writer with a specific compression level
with XlsbWriter("output.xlsb", compressionLevel=6) as writer:
    # Add a visible sheet
    writer.add_sheet("Visible Sheet")
    writer.write_sheet(data)

    # Add a hidden sheet
    writer.add_sheet("Hidden Sheet", hidden=True)
    writer.write_sheet([["This sheet is hidden."]])

XlsxWriter Example

from xlspy import XlsxWriter
import datetime
from decimal import Decimal

data = [
    ["Name", "Age", "City", "info"],
    [-123, 2147483647, 2147483648, 2147483999],
    ["x", "y", "z", datetime.datetime.today()],
    ["Alice", 25, "New York", datetime.date.today()],
    ["Bob", 30, "London", Decimal(3.14)],
    ["Charlie", 35, "Paris", datetime.datetime.now()],
    [True, False, None, datetime.datetime.utcnow()]
]

# Initialize writer with a specific compression level
with XlsxWriter("output.xlsx", compressionLevel=6) as writer:
    # Add a visible sheet
    writer.add_sheet("Visible Sheet")
    writer.write_sheet(data)

    # Add a hidden sheet
    writer.add_sheet("Hidden Sheet", hidden=True)
    writer.write_sheet([["This sheet is hidden."]])

Cell Formatting (Number, Date, DateTime)

xlspy supports custom cell formatting for numbers, dates, and datetimes in both XLSB and XLSX output. Pass a (value, format_string) tuple to apply a format to a specific cell.

from xlspy import XlsxWriter, F  # or XlsbWriter
import datetime

with XlsxWriter("formatted.xlsx") as writer:
    writer.add_sheet("Formats")
    writer.write_sheet([
        ["Description", "Value"],
        ["Thousands separator", (100000, F.THOUSANDS_SEP)],
        ["Currency PLN",       (100000, F.CURRENCY_PLN)],
        ["Currency EUR",       (100000, F.CURRENCY_EUR)],
        ["Percentage",         (100000, F.PERCENTAGE)],
        ["Scientific",         (100000, F.SCIENTIFIC)],
        ["Two decimals",       (100000, F.TWO_DECIMALS)],
        ["Text",               (100000, F.TEXT)],
        ["Leading zeros",      (100000, F.LEADING_ZEROS)],
        ["Short date",         (datetime.date(2026,6,1), F.DATE_SHORT)],
        ["Long date",          (datetime.date(2026,6,1), F.DATE_LONG)],
        ["ISO date",           (datetime.date(2026,6,1), F.DATE_ISO)],
        ["Month + year",       (datetime.date(2026,6,1), F.DATE_MONTH_YEAR)],
        ["Weekday + date",     (datetime.date(2026,6,1), F.DATE_WEEKDAY)],
        ["Short datetime",     (datetime.datetime(2026,6,1,14,34), F.DATETIME_SHORT)],
        ["Time only",          (datetime.datetime(2026,6,1,14,34), F.TIME_HH_MM)],
        ["12h time",           (datetime.datetime(2026,6,1,14,34), F.TIME_12H)],
        ["ISO datetime",       (datetime.datetime(2026,6,1,14,34), F.DATETIME_ISO)],
    ])

Available Format Constants (xlspy.F)

You can also use custom format strings directly:

writer.write_sheet([
    ["Custom", (1234.56, '#,##0.00 "USD"')],
    ["Date",   (datetime.date(2026,6,1), 'dd.mm.yyyy')],
])

The formatting works transparently on both XlsxWriter and XlsbWriter.

Writing pandas / polars DataFrames (optional)

xlspy can write a DataFrame directly to .xlsx or .xlsb. pandas and polars are optional — xlspy has no required dependencies and imports them lazily only when you actually pass a DataFrame:

pip install xlspy[pandas]   # pandas support
pip install xlspy[polars]   # polars support

The quickest way is the one-liner to_xlsx / to_xlsb:

import pandas as pd
from xlspy import to_xlsx, to_xlsb

df = pd.DataFrame({
    "name": ["Alice", "Bob"],
    "age": [25, 30],
    "joined": pd.to_datetime(["2024-01-15", "2023-06-01"]),
})

to_xlsx(df, "people.xlsx")
to_xlsb(df, "people.xlsb")

The same works for polars, with an identical API:

import polars as pl
from xlspy import to_xlsb

df = pl.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
to_xlsb(df, "people.xlsb")

For full control, use the writer method, which behaves exactly like write_sheet and writes into the last sheet added with add_sheet:

import pandas as pd
from xlspy import XlsxWriter, F

df = pd.DataFrame({
    "joined": pd.to_datetime(["2024-01-15", "2023-06-01"]),
    "age": [25, 30],
})

with XlsxWriter("report.xlsx") as writer:
    writer.add_sheet("Data")
    writer.write_dataframe(
        df,
        include_index=True,
        na_rep="n/a",
        formats={"joined": F.DATE_ISO, "age": F.THOUSANDS_SEP},
    )
    writer.add_sheet("Notes")
    writer.write_sheet([["Generated by xlspy"]])

Options

Values are converted to the types the writers understand:

See examples/dataframe_*.py and examples/polars_to_xlsx_xlsb.py for full, runnable scripts, indexed in examples/README.md.

Updating an Existing Workbook

XlsxUpdater (also for macro-enabled .xlsm files) and XlsbUpdater replace the data region of an existing workbook while keeping the workbook structure, styles, drawings and unrelated sheets. They are useful when a workbook contains pivot tables: the worksheet source range and pivot-cache refresh metadata are updated together.

from xlspy import XlsxUpdater, XlsbUpdater

rows = [
    ["Alice", 42],
    ["Bob", 37],
]

updater = XlsxUpdater("template.xlsx")
print(updater.get_sheet_names())
updater.replace_sheet_data("Data", rows, headers=["Name", "Amount"])
updater.save("result.xlsx")       # use save() to replace the input atomically

# The XLSB API is identical:
binary_updater = XlsbUpdater("template.xlsb")
binary_updater.replace_sheet_data("Data", rows, headers=["Name", "Amount"])
binary_updater.save("result.xlsb")

# XLSM uses the same XML updater.  The VBA project is preserved byte-for-byte.
macro_updater = XlsxUpdater("template.xlsm")
macro_updater.replace_sheet_data("Data", rows, headers=["Name", "Amount"])
macro_updater.save("result.xlsm")

XlsmUpdater is an explicit alias of XlsxUpdater for .xlsm inputs. The updater does not parse, execute or rewrite VBA; xl/vbaProject.bin and other unrelated macro parts are copied as opaque ZIP members.

For a database cursor or another one-pass source, use the streaming variant. Rows are written through bounded-memory temporary files, so the complete result set is not kept in Python memory. Multiple sheets may be streamed on the same updater before save():

def rows_from_cursor(cursor):
    while True:
        row = cursor.fetchone()
        if row is None:
            break
        yield list(row)

updater = XlsxUpdater("template.xlsx")  # also accepts .xlsm; use XlsbUpdater for .xlsb
updater.replace_sheet_data_stream(
    "Data",
    rows_from_cursor(cursor),
    headers=["Name", "Amount"],
)
updater.save("result.xlsx")              # or result.xlsb

The streaming API requires save() for its memory-bounded write path. to_bytes() necessarily creates the complete archive in memory because it returns the complete file as bytes. The original replace_sheet_data() API is unchanged and remains available for in-memory inputs.

Rows may be any iterable, including a generator. Only trailing rows consisting entirely of None are removed; blank rows in the middle are preserved. By default, the updater inherits the dominant style of each existing column. Use style_fallback="general" to write new cells with the General style. Existing writer-style (value, format_string) tuples are accepted for compatibility, but updating a workbook does not create new styles.

When a pivot cache is present, keep the source column schema (column order and count) unchanged; changing the schema requires rebuilding the pivot cache and is outside the updater's scope.

On Windows, the generated workbook can be checked with the installed Excel COM server (the script opens files read-only using Excel's normal loader):

powershell -ExecutionPolicy Bypass -File tools\validate_excel_com.ps1 result.xlsx result.xlsb

Reading XLSB and XLSX Files

Reading files is done via the ExcelReader class, which automatically detects the format.

from xlspy import ExcelReader

with ExcelReader("input.xlsx") as reader:  # or .xlsb
    names = reader.get_sheet_names()
    print(f"Sheets: {names}")

    for sheet_name in names:
        rows = reader.read_all(sheet_name)
        for row in rows:
            print(row)

# Generator usage (memory efficient for large files):
with ExcelReader("large_file.xlsb") as reader:
    for row in reader.get_rows("Sheet1"):
        print(row)

Streaming from a Database (Netezza)

This example shows how to stream data directly from a database query into an XLSB file using nzpy-extended. This is highly memory-efficient as it doesn't load the entire dataset into memory.

First, ensure you have nzpy-extended installed:

pip install nzpy-extended

Then, you can use a generator function to feed data to XlsbWriter.

import os
from typing import Generator
from xlspy import XlsbWriter

# --- Configuration ---
NZ_CONFIG = {
    "host": os.environ.get("NZ_DEV_HOST", "your_host"),
    "port": int(os.environ.get("NZ_DEV_PORT", "5480")),
    "database": os.environ.get("NZ_DEV_DB", "your_db"),
    "user": os.environ.get("NZ_DEV_USER", "your_user"),
    "password": os.environ.get("NZ_DEV_PASSWORD", "your_password"),
}
QUERY = "SELECT * FROM YourTable"
OUTPUT_FILENAME = "db_output.xlsb"


def row_generator(cursor) -> Generator[list, None, None]:
    """Yields column headers first, then each data row."""
    headers = [column[0] for column in cursor.description]
    yield headers
    while row := cursor.fetchone():
        yield list(row)


# --- Main Execution ---
try:
    import nzpy_extended.sync as nzpy

    with nzpy.connect(**NZ_CONFIG) as conn:
        cursor = conn.cursor()
        cursor.execute(QUERY)

        with XlsbWriter(OUTPUT_FILENAME) as writer:
            writer.add_sheet("Database Export")
            writer.write_sheet(row_generator(cursor))
            writer.add_sheet("SQL Query", hidden=True)
            writer.write_sheet([["SQL"], [QUERY]])

    print(f"Successfully created '{OUTPUT_FILENAME}'")

except Exception as e:
    print(f"An unexpected error occurred: {e}")

Performance

xlspy is designed for high performance. Since version 0.1.0, the library includes a C extension (_c_core) that accelerates XLSB read and write. The C extension is enabled by default (compiled automatically on install). Set XLSPY_DISABLE_C_EXT=1 to force the pure Python fallback.

All benchmarks: 50000 × 50 dataset (2.5M cells). Tests performed on Windows 11 (Python 3.14, AMD64).

Write

Read

Analysis

The 4.6× read speedup comes from two factors:

• ~60–70% — native C compilation, no interpreter overhead per record
• ~30–40% — algorithm simplification: flat array indexed by col − first_col instead of Dict[int, Any], no isinstance per cell, no BiffReader.read_worksheet() method call per record

Run the benchmarks yourself with examples/performance_test.py.

Repository

https://github.com/KrzysztofDusko/xlspy/