xllify 0.8.0

Python SDK for creating Excel XLL add-ins with xllify

xllify Python SDK

A Python SDK for creating high-performance Excel add-ins with xllify. Write Python functions and call them from Excel with automatic type conversion, error handling, and real-time updates.

Installation

You it is strongly recommended that you create a virtual environment and activate it.

pip install virtualenv # if you don't have it
virtualenv venv
source venv/bin/activate # mac OR
source venv\Scripts\activate # win

Then install xllify.

pip install xllify
xllify-install

Quick Start

1. Create your Python functions

# my_functions.py
import xllify

@xllify.fn("xllipy.Hello")
def hello(name: str = "World") -> str:
    return f"Hello, {name}!"

@xllify.fn("xllipy.Add", category="Math")
def add(a: float, b: float) -> float:
    return a + b

2. Build

xllify MyAddin.xll my_functions.py

3. Use in Excel

Functions are immediately available after you open the .xll in Excel.

=xllipy.Hello("World")       -> "Hello, World!"
=xllipy.Add(5, 10)           -> 15

Functions execute asynchronously - Excel shows #N/A while processing, then updates automatically when complete.

Workflow

Build and deployment

For production, Python files are embedded directly into your XLL:

xllify build MyAddin.xll main.py --requirements requirements.txt

When the XLL loads in Excel, Python files are extracted to %LOCALAPPDATA%\xllify\MyAddin\python\ and the Python process starts automatically.

Distribution

Just distribute the XLL file. Everything else (Python files, dependencies) is embedded and will be extracted automatically on first load.

Features

• Async by default - Functions run asynchronously so Excel never freezes
• Auto-reload - Hot reload functions during development without restarting Excel
• Matrix support - Return 2D arrays and pandas DataFrames directly to Excel
• Type-safe - Full type hints with CellValue, Matrix, and ExcelValue
• Simple API - Just add the @xllify.fn() decorator to your existing functions

Best practices

Our recommended approach is for you to implement your business logic in plain Python modules and call them from short functions wrapped with @xllify.fn. This keeps your core logic testable, reusable, and independent of Excel.

# my_logic.py - Pure Python, no xllify dependencies
def calculate_option_price(spot, strike, time, rate, volatility):
    """Black-Scholes call option pricing - testable business logic"""
    # ... implementation ...
    return price

# excel_functions.py - Thin xllify wrapper
import xllify
from my_logic import calculate_option_price

@xllify.fn("xllipy.BSCall", category="Finance")
def bs_call(s: float, k: float, t: float, r: float, sigma: float) -> float:
    """Excel wrapper for Black-Scholes calculation"""
    return calculate_option_price(s, k, t, r, sigma)

Benefits:

• Testable: Run pytest on my_logic.py without Excel or xllify
• Reusable: Use the same logic in web apps, CLIs, or other contexts without needing to import xllify
• Maintainable: Separate concerns between business logic and Excel integration
• Debuggable: Test and debug core logic independently

Advanced usage

Batching configuration

By default, xllify batches RTD updates for better performance (batch_size=500, batch_timeout_ms=50). You can customize this:

import xllify

# Configure batching before registering functions
xllify.configure_batching(
    enabled=True,
    batch_size=1000,        # Batch up to 1000 updates together
    batch_timeout_ms=100    # Wait up to 100ms before flushing
)

@xllify.fn("xllipy.Hello")
def hello(name: str) -> str:
    return f"Hello, {name}!"

When to adjust batching:

• High-volume scenarios: Increase batch_size (1000+) and batch_timeout_ms (100+) for better throughput when handling many concurrent calculations
• Low-latency requirements: Decrease batch_timeout_ms (10-20ms) or disable batching entirely for faster individual responses
• Balanced performance: Use defaults (batch_size=500, batch_timeout_ms=50)

Disable batching:

xllify.configure_batching(enabled=False)  # Send updates immediately

Parameter metadata

Provide detailed parameter information for better documentation:

from xllify import fn, Parameter

@fn(
    "xllipy.Calculate",
    description="Perform calculation with optional delay",
    category="Math",
    parameters=[
        Parameter("value", type="number", description="Value to process"),
        Parameter("delay", type="number", description="Delay in seconds (optional)")
    ],
    return_type="number"
)
def calculate(value: float, delay: float = 1.0) -> float:
    """Process a value with optional delay"""
    import time
    if delay > 0:
        time.sleep(delay)
    return value * 2

Working with arrays and matrices

Excel ranges are passed as 2D lists. You can also return matrices to Excel:

from xllify import Matrix

# Input: Accept ranges as 2D lists
@xllify.fn("xllipy.SumArray", description="Sum all numbers in a range")
def sum_array(numbers: list) -> float:
    """Sum a 2D array from Excel range"""
    total = 0.0
    for row in numbers:
        for cell in row:
            if isinstance(cell, (int, float)):
                total += cell
    return total

# Output: Return matrices to Excel
@xllify.fn("xllipy.GetData")
def get_data() -> Matrix:
    """Return a 2D array to Excel"""
    return [
        [1.0, True, "hello"],
        [2.0, False, None],    # None displays as empty cell
        [3.0, None, "world"]
    ]

In Excel:

=xllipy.SumArray(A1:C10)           -> Sum of all numbers
=xllipy.GetData()                  -> Spills 3x3 array into cells

Pandas DataFrames

Return DataFrames directly - automatically converted to Excel ranges with headers:

import pandas as pd

@xllify.fn("xllipy.GetDataFrame")
def get_dataframe() -> pd.DataFrame:
    """Return pandas DataFrame to Excel"""
    return pd.DataFrame({
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Score': [95.5, 87.3, 92.1]
    })

In Excel, =xllipy.GetDataFrame() spills as:

Name      Age    Score
Alice     25     95.5
Bob       30     87.3
Charlie   35     92.1

Type mapping

Excel Type	Python Type
Number	float
String	str
Boolean	bool
Range	List[List] (2D array)
Empty	None

API reference

Type definitions

from xllify import CellValue, Matrix, ExcelValue

CellValue = Union[float, bool, str, None]
Matrix = List[List[CellValue]]
ExcelValue = Union[CellValue, Matrix, Any]

• CellValue: A single Excel cell value (number, boolean, string, or None for empty cells)
• Matrix: A 2D array of cell values
• ExcelValue: Any value that can be returned to Excel (scalar, matrix, or pandas DataFrame)

Decorators

@xllify.fn(name, description="", category="", parameters=None, return_type="")

Register a Python function as an Excel function.

Arguments:

• name (str): Excel function name (e.g., "xllipy.MyFunc")
• description (str, optional): Function description (defaults to docstring)
• category (str, optional): Excel function category
• parameters (List[Parameter], optional): Parameter metadata
• return_type (str, optional): Return type override

Example:

@xllify.fn("xllipy.Add", description="Add numbers", category="Math")
def add(a: float, b: float) -> float:
    return a + b

CLI commands

# Install the xllify tool and xllify-lua
xllify-install

# Clear async function cache
xllify-clear-cache

Error handling

Python exceptions are caught and returned to Excel as error strings:

@xllify.fn("xllipy.Divide")
def divide(a, b):
    if b == 0:
        raise ValueError("Division by zero")
    return a / b

In Excel:

=xllipy.Divide(10, 0)    -> #ERROR: Division by zero

More examples

Slow operations (async)

Functions run asynchronously - Excel never freezes:

@xllify.fn("xllipy.SlowCalc")
def slow_calc(seconds: float) -> str:
    import time
    time.sleep(seconds)
    return f"Done after {seconds}s"

Excel shows #N/A while waiting, then updates automatically.

It is worth pointing out that this WILL however block your Python process. A simple workaround is to run multiple processes of the same Python script. asyncio support is planned.

Black-Scholes option pricing

from math import log, sqrt, exp, erf

@xllify.fn("xllipy.BSCall", category="Finance")
def black_scholes_call(s: float, k: float, t: float, r: float, sigma: float) -> float:
    """Black-Scholes call option price"""
    if t <= 0:
        return max(s - k, 0)

    d1 = (log(s / k) + (r + 0.5 * sigma ** 2) * t) / (sigma * sqrt(t))
    d2 = d1 - sigma * sqrt(t)

    def norm_cdf(x):
        return 0.5 * (1 + erf(x / sqrt(2)))

    return s * norm_cdf(d1) - k * exp(-r * t) * norm_cdf(d2)

Usage: =xllipy.BSCall(100, 95, 0.25, 0.05, 0.2)

HTTP requests

@xllify.fn("xllipy.FetchPrice")
def fetch_price(symbol: str) -> float:
    import requests
    resp = requests.get(f"https://api.example.com/price/{symbol}")
    return resp.json()["price"]

System info

@xllify.fn("xllipy.GetInfo")
def get_info() -> str:
    import sys
    import platform
    return f"Python {sys.version.split()[0]} on {platform.system()}"

Development

Running tests

pip install -e ".[dev]"
pytest tests/ -v

Code formatting

black xllify/ tests/ examples/

License

MIT

Support

• Issues: https://github.com/xllifycom/xllify-python/issues