User Defined Functions (UDFs)¶

This tutorial gets you quickly started on how to write User Defined Functions.

Note UDFs are currently only available on Windows.

For details of how to control the behaviour of the arguments and return values, have a look at Converters and Options .

. For a comprehensive overview of the available decorators and their options, check out the corresponding API docs: UDF decorators .

One-time Excel preparations¶ 1) Enable Trust access to the VBA project object model under File > Options > Trust Center > Trust Center Settings > Macro Settings Install the add-in via command prompt: xlwings addin install (see Add-in & Settings ).

Workbook preparation¶ The easiest way to start a new project is to run xlwings quickstart myproject on a command prompt (see Command Line Client (CLI)). This automatically adds the xlwings reference to the generated workbook.

A simple UDF¶ The default addin settings expect a Python source file in the way it is created by quickstart : in the same directory as the Excel file

with the same name as the Excel file, but with a .py ending instead of .xlsm . Alternatively, you can point to a specific module via UDF Modules in the xlwings ribbon. Let’s assume you have a Workbook myproject.xlsm , then you would write the following code in myproject.py : 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 UDFs in the xlwings tab to pick up the changes made to myproject.py .

Enter the formula =double_sum(1, 2) into a cell and you will see the correct result:

The docstring (in triple-quotes) will be shown as function description in Excel. Note 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. If you want to make sure everything is in a fresh state, click Restart UDF Server .

), but changes in imported modules are not. This is the very behaviour of how Python imports work. If you want to make sure everything is in a fresh state, click . The @xw.func decorator 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 UDFs again

again Fill in the values in the range A1:B2

Select the range D1:E2

Type in the formula =add_one(A1:B2)

Press Ctrl+Shift+Enter to 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 Note 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 version of 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 a one-liner: 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 x into 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¶ Note If your version of Excel supports the new native dynamic arrays, then you don’t have to do anything special, and you shouldn’t use the expand decorator! To check if your version of Excel supports it, see if you have the =UNIQUE() formula available. Native dynamic arrays were introduced in Office 365 Insider Fast at the end of September 2018. 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 . In practice, it often turns out to be a cumbersome process, 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

Pre v0.15.0 doesn’t allow to have volatile functions as arguments, e.g. you cannot use functions like =TODAY() as arguments. Starting with v0.15.0, you can use volatile functions as input, but the UDF will be called more than 1x.

as arguments. Starting with v0.15.0, you can use volatile functions as input, but the UDF will be called more than 1x. Dynamic Arrays have been refactored with v0.15.0 to be proper legacy arrays: To edit a dynamic array with xlwings >= v0.15.0, you need to hit Ctrl-Shift-Enter while in the top left cell. Note that you don’t have to do that when you enter the formula for the first time.

Docstrings¶ 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 keyword. vba , in fact, allows you to access any available VBA expression e.g. 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

Macros¶ On Windows, as alternative to calling macros via RunPython, you can also use the @xw.sub decorator: 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 [ 0 ] . 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 File > 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 my_macro .

Call UDFs from VBA¶ Imported functions can also be used from VBA. For example, for a function returning a 2d array: Sub MySub () Dim arr () As Variant Dim i As Long , j As Long arr = my_imported_function (...) For j = LBound ( arr , 2 ) To UBound ( arr , 2 ) For i = LBound ( arr , 1 ) To UBound ( arr , 1 ) Debug . Print "(" & i & "," & j & ")" , arr ( i , j ) Next i Next j End Sub