UDFs are currently only available on Windows.
This tutorial gets you quickly started on how to write User Defined Functions. For details of how to control the behaviour of the arguments and return values, have a look at Converters and Options.
One-time Excel preparations¶
Trust access to the VBA project object model under
File > Options > Trust Center > Trust Center Settings > Macro Settings
Recommended: Install the add-in via command prompt:
xlwings addin install (see Command Line Client) to be
able to easily import the functions.
The easiest way to start a new project is to run
xlwings quickstart myproject on a command prompt (see Command Line Client).
Alternative ways of getting the xlwings VBA module into your workbook are described under VBA: Calling Python from Excel
A simple UDF¶
The default settings (see VBA settings) expect a Python source file in the way it is created
- in the same directory as the Excel file
- with the same name as the Excel file, but with a
.pyending instead of
Alternatively, you can point to a specific source file by setting the
UDF_PATH in the VBA settings.
Let’s assume you have a Workbook
myproject.xlsm, then you would write the following code in
import xlwings as xw @xw.func def double_sum(x, y): """Returns twice the sum of the two arguments""" return 2 * (x + y)
Now click on
Import Python UDFsin the xlwings tab to pick up the changes made to
myproject.py. If you don’t want to install/use the add-in, you could also run the
ImportPythonUDFsmacro directly (one possibility to do that is to hit
Alt + F8and select the macro from the pop-up menu).
Enter the formula
=double_sum(1, 2)into a cell and you will see the correct result:
This formula can be used in VBA, too.
The docstring (in triple-quotes) will be shown as function description in Excel.
- You only need to re-import your functions if you change the function arguments or the function name.
- Code changes in the actual functions are picked up automatically (i.e. at the next calculation of the formula,
e.g. triggered by
Ctrl-Alt-F9), but changes in imported modules are not. This is the very behaviour of how Python imports work. The easiest way to come around this is by working with the debug server that can easily be restarted, see: Debugging. If you aren’t working with the debug server, the
pythonw.exeprocess currently has to be killed via Windows Task Manager.
@xw.funcdecorator is only used by xlwings when the function is being imported into Excel. It tells xlwings for which functions it should create a VBA wrapper function, otherwise it has no effect on how the functions behave in Python.
Array formulas: Get efficient¶
Calling one big array formula in Excel is much more efficient than calling many single-cell formulas, so it’s generally a good idea to use them, especially if you hit performance problems.
You can pass an Excel Range as a function argument, as opposed to a single cell and it will show up in Python as list of lists.
For example, you can write the following function to add 1 to every cell in a Range:
@xw.func def add_one(data): return [[cell + 1 for cell in row] for row in data]
To use this formula in Excel,
- Click on
Import Python UDFsagain
- Fill in the values in the range
- Select the range
- Type in the formula
Ctrl+Shift+Enterto create an array formula. If you did everything correctly, you’ll see the formula surrounded by curly braces as in this screenshot:
Number of array dimensions: ndim¶
The above formula has the issue that it expects a “two dimensional” input, e.g. a nested list of the form
[[1, 2], [3, 4]].
Therefore, if you would apply the formula to a single cell, you would get the following error:
TypeError: 'float' object is not iterable.
To force Excel to always give you a two-dimensional array, no matter whether the argument is a single cell, a column/row or a two-dimensional Range, you can extend the above formula like this:
@xw.func @xw.arg('data', ndim=2) def add_one(data): return [[cell + 1 for cell in row] for row in data]
Array formulas with NumPy and Pandas¶
Often, you’ll want to use NumPy arrays or Pandas DataFrames in your UDF, as this unlocks the full power of Python’s ecosystem for scientific computing.
To define a formula for matrix multiplication using numpy arrays, you would define the following function:
import xlwings as xw import numpy as np @xw.func @xw.arg('x', np.array, ndim=2) @xw.arg('y', np.array, ndim=2) def matrix_mult(x, y): return x @ y
If you are not on Python >= 3.5 with NumPy >= 1.10, use
x.dot(y) instead of
x @ y.
A great example of how you can put Pandas at work is the creation of an array-based
CORREL formula. Excel’s
CORREL only works on 2 datasets and is cumbersome to use if you want to quickly get the correlation
matrix of a few time-series, for example. Pandas makes the creation of an array-based
CORREL2 formula basically
import xlwings as xw import pandas as pd @xw.func @xw.arg('x', pd.DataFrame, index=False, header=False) @xw.ret(index=False, header=False) def CORREL2(x): """Like CORREL, but as array formula for more than 2 data sets""" return x.corr()
@xw.arg and @xw.ret decorators¶
These decorators are to UDFs what the
options method is to
Range objects: they allow you to apply converters and their
options to function arguments (
@xw.arg) and to the return value (
@xw.ret). For example, to convert the argument
a pandas DataFrame and suppress the index when returning it, you would do the following:
@xw.func @xw.arg('x', pd.DataFrame) @xw.ret(index=False) def myfunction(x): # x is a DataFrame, do something with it return x
For further details see the Converters and Options documentation.
Dynamic Array Formulas¶
As seen above, to use Excel’s array formulas, you need to specify their dimensions up front by selecting the
result array first, then entering the formula and finally hitting
Ctrl-Shift-Enter. While this makes sense from
a data integrity point of view, in practice, it often turns out to be a cumbersome limitation, especially when working
with dynamic arrays such as time series data. Since v0.10, xlwings offers dynamic UDF expansion:
This is a simple example that demonstrates the syntax and effect of UDF expansion:
import numpy as np @xw.func @xw.ret(expand='table') def dynamic_array(r, c): return np.random.randn(int(r), int(c))
Note: Expanding array formulas will overwrite cells without prompting and leave an empty border around them, i.e. they will clear the row to the bottom and the column to the right of the array.
The following sample shows how to include docstrings both for the function and for the arguments x and y that then show up in the function wizard in Excel:
import xlwings as xw @xw.func @xw.arg('x', doc='This is x.') @xw.arg('y', doc='This is y.') def double_sum(x, y): """Returns twice the sum of the two arguments""" return 2 * (x + y)
The “vba” keyword¶
It’s often helpful to get the address of the calling cell. Right now, one of the easiest ways to
accomplish this is to use the
vba, in fact, allows you to access any available VBA expression
Application. Note, however, that currently you’re acting directly on the pywin32 COM object:
@xw.func @xw.arg('xl_app', vba='Application') def get_caller_address(xl_app) return xl_app.Caller.Address
On Windows, as alternative to calling macros via RunPython, you can also use the
import xlwings as xw @xw.sub def my_macro(): """Writes the name of the Workbook into Range("A1") of Sheet 1""" wb = xw.Book.caller() wb.sheets.range('A1').value = wb.name
After clicking on
Import Python UDFs, you can then use this macro by executing it via
Alt + F8 or by
binding it e.g. to a button. To to the latter, make sure you have the
Developer tab selected under
Options > Customize Ribbon. Then, under the
Developer tab, you can insert a button via
Insert > Form Controls.
After drawing the button, you will be prompted to assign a macro to it and you can select