Converters and Options

Introduced with v0.7.0, converters define how Excel ranges and their values are converted both during reading and writing operations. They also provide a consistent experience across xlwings.Range objects and User Defined Functions (UDFs).

Converters are explicitely set in the options method when manipulating Range objects or in the @xw.arg and @xw.ret decorators when using UDFs. If no converter is specified, the default converter is applied when reading. When writing, xlwings will automatically apply the correct converter (if available) according to the object’s type that is being written to Excel. If no converter is found for that type, it falls back to the default converter.

All code samples below depend on the following import:

>>> import xlwings as xw

Syntax:

  xw.Range UDFs
reading xw.Range.options(convert=None, **kwargs).value @arg('x', convert=None, **kwargs)
writing xw.Range.options(convert=None, **kwargs).value = myvalue @ret(convert=None, **kwargs)

Note

Keyword arguments (kwargs) may refer to the specific converter or the default converter. For example, to set the numbers option in the default converter and the index option in the DataFrame converter, you would write:

xw.Range('A1:C3').options(pd.DataFrame, index=False, numbers=int).value

Default Converter

If no options are set, the following conversions are performed:

  • single cells are read in as floats in case the Excel cell holds a number, as unicode in case it holds text, as datetime if it contains a date and as None in case it is empty.
  • columns/rows are read in as lists, e.g. [None, 1.0, 'a string']
  • 2d cell ranges are read in as list of lists, e.g. [[None, 1.0, 'a string'], [None, 2.0, 'another string']]

The following options can be set:

  • ndim

    Force the value to have either 1 or 2 dimensions regardless of the shape of the range:

    >>> import xlwings as xw
    >>> sht = xw.Book().sheets[0]
    >>> sht.range('A1').value = [[1, 2], [3, 4]]
    >>> sht.range('A1').value
    1.0
    >>> sht.range('A1').options(ndim=1).value
    [1.0]
    >>> sht.range('A1').options(ndim=2).value
    [[1.0]]
    >>> sht.range('A1:A2').value
    [1.0 3.0]
    >>> sht.range('A1:A2').options(ndim=2).value
    [[1.0], [3.0]]
    
  • numbers

    By default cells with numbers are read as float, but you can change it to int:

    >>> sht.range('A1').value = 1
    >>> sht.range('A1').value
    1.0
    >>> sht.range('A1').options(numbers=int).value
    1
    

    Alternatively, you can specify any other function or type which takes a single float argument.

    Using this on UDFs looks like this:

    @xw.func
    @xw.arg('x', numbers=int)
    def myfunction(x):
        # all numbers in x arrive as int
        return x
    

    Note: Excel always stores numbers internally as floats.

  • dates

    By default cells with dates are read as datetime.datetime, but you can change it to datetime.date:

    • Range:

      >>> import datetime as dt
      >>> sht.range('A1').options(dates=dt.date).value
      
    • UDFs: @xw.arg('x', dates=dt.date)

    Alternatively, you can specify any other function or type which takes the same keyword arguments as datetime.datetime, for example:

    >>> my_date_handler = lambda year, month, day, **kwargs: "%04i-%02i-%02i" % (year, month, day)
    >>> sht.range('A1').options(dates=my_date_handler).value
    '2017-02-20'
    
  • empty

    Empty cells are converted per default into None, you can change this as follows:

    • Range: >>> sht.range('A1').options(empty='NA').value
    • UDFs: @xw.arg('x', empty='NA')
  • transpose

    This works for reading and writing and allows us to e.g. write a list in column orientation to Excel:

    • Range: sht.range('A1').options(transpose=True).value = [1, 2, 3]

    • UDFs:

      @xw.arg('x', transpose=True)
      @xw.ret(transpose=True)
      def myfunction(x):
          # x will be returned unchanged as transposed both when reading and writing
          return x
      
  • expand

    This works the same as the Range properties table, vertical and horizontal but is only evaluated when getting the values of a Range:

    >>> import xlwings as xw
    >>> sht = xw.Book().sheets[0]
    >>> sht.range('A1').value = [[1,2], [3,4]]
    >>> rng1 = sht.range('A1').expand()
    >>> rng2 = sht.range('A1').options(expand='table')
    >>> rng1.value
    [[1.0, 2.0], [3.0, 4.0]]
    >>> rng2.value
    [[1.0, 2.0], [3.0, 4.0]]
    >>> sht.range('A3').value = [5, 6]
    >>> rng1.value
    [[1.0, 2.0], [3.0, 4.0]]
    >>> rng2.value
    [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]
    

    Note

    The expand method is only available on Range objects as UDFs only allow to manipulate the calling cells.

Built-in Converters

xlwings offers several built-in converters that perform type conversion to dictionaries, NumPy arrays, Pandas Series and DataFrames. These build on top of the default converter, so in most cases the options described above can be used in this context, too (unless they are meaningless, for example the ndim in the case of a dictionary).

It is also possible to write and register custom converter for additional types, see below.

The samples below can be used with both xlwings.Range objects and UDFs even though only one version may be shown.

Dictionary converter

The dictionary converter turns two Excel columns into a dictionary. If the data is in row orientation, use transpose:

_images/dict_converter.png
>>> sht = xw.sheets.active
>>> sht.range('A1:B2').options(dict).value
{'a': 1.0, 'b': 2.0}
>>> sht.range('A4:B5').options(dict, transpose=True).value
{'a': 1.0, 'b': 2.0}

Numpy array converter

options: dtype=None, copy=True, order=None, ndim=None

The first 3 options behave the same as when using np.array() directly. Also, ndim works the same as shown above for lists (under default converter) and hence returns either numpy scalars, 1d arrays or 2d arrays.

Example:

>>> import numpy as np
>>> sht = xw.Book().sheets[0]
>>> sht.range('A1').options(transpose=True).value = np.array([1, 2, 3])
>>> sht.range('A1:A3').options(np.array, ndim=2).value
array([[ 1.],
       [ 2.],
       [ 3.]])

Pandas Series converter

options: dtype=None, copy=False, index=1, header=True

The first 2 options behave the same as when using pd.Series() directly. ndim doesn’t have an effect on Pandas series as they are always expected and returned in column orientation.

index: int or Boolean
When reading, it expects the number of index columns shown in Excel.
When writing, include or exclude the index by setting it to True or False.
header: Boolean
When reading, set it to False if Excel doesn’t show either index or series names.
When writing, include or exclude the index and series names by setting it to True or False.

For index and header, 1 and True may be used interchangeably.

Example:

_images/series_conv.png
>>> sht = xw.Book().sheets[0]
>>> s = sht.range('A1').options(pd.Series, expand='table').value
>>> s
date
2001-01-01    1
2001-01-02    2
2001-01-03    3
2001-01-04    4
2001-01-05    5
2001-01-06    6
Name: series name, dtype: float64
>>> sht.range('D1', header=False).value = s

Pandas DataFrame converter

options: dtype=None, copy=False, index=1, header=1

The first 2 options behave the same as when using pd.DataFrame() directly. ndim doesn’t have an effect on Pandas DataFrames as they are automatically read in with ndim=2.

index: int or Boolean
When reading, it expects the number of index columns shown in Excel.
When writing, include or exclude the index by setting it to True or False.
header: int or Boolean
When reading, it expects the number of column headers shown in Excel.
When writing, include or exclude the index and series names by setting it to True or False.

For index and header, 1 and True may be used interchangeably.

Example:

_images/df_converter.png
>>> sht = xw.Book().sheets[0]
>>> df = sht.range('A1:D5').options(pd.DataFrame, header=2).value
>>> df
    a     b
    c  d  e
ix
10  1  2  3
20  4  5  6
30  7  8  9

# Writing back using the defaults:
>>> sht.range('A1').value = df

# Writing back and changing some of the options, e.g. getting rid of the index:
>>> sht.range('B7').options(index=False).value = df

The same sample for UDF (starting in Range('A13') on screenshot) looks like this:

@xw.func
@xw.arg('x', pd.DataFrame, header=2)
@xw.ret(index=False)
def myfunction(x):
   # x is a DataFrame, do something with it
   return x

xw.Range and ‘raw’ converters

Technically speaking, these are “no-converters”.

  • If you need access to the xlwings.Range object directly, you can do:

    @xw.func
    @xw.arg('x', xw.Range)
    def myfunction(x):
       return x.formula
    

    This returns x as xlwings.Range object, i.e. without applying any converters or options.

  • The raw converter delivers the values unchanged from the underlying libraries (pywin32 on Windows and appscript on Mac), i.e. no sanitizing/cross-platform harmonizing of values are being made. This might be useful in a few cases for efficiency reasons. E.g:

    >>> sht.range('A1:B2').value
    [[1.0, 'text'], [datetime.datetime(2016, 2, 1, 0, 0), None]]
    
    >>> sht.range('A1:B2').options('raw').value  # or sht.range('A1:B2').raw_value
    ((1.0, 'text'), (pywintypes.datetime(2016, 2, 1, 0, 0, tzinfo=TimeZoneInfo('GMT Standard Time', True)), None))
    

Custom Converter

Here are the steps to implement your own converter:

  • Inherit from xlwings.conversion.Converter

  • Implement both a read_value and write_value method as static- or classmethod:

    • In read_value, value is what the base converter returns: hence, if no base has been specified it arrives in the format of the default converter.
    • In write_value, value is the original object being written to Excel. It must be returned in the format that the base converter expects. Again, if no base has been specified, this is the default converter.

    The options dictionary will contain all keyword arguments specified in the xw.Range.options method, e.g. when calling xw.Range('A1').options(myoption='some value') or as specified in the @arg and @ret decorator when using UDFs. Here is the basic structure:

    from xlwings.conversion import Converter
    
    class MyConverter(Converter):
    
        @staticmethod
        def read_value(value, options):
            myoption = options.get('myoption', default_value)
            return_value = value  # Implement your conversion here
            return return_value
    
        @staticmethod
        def write_value(value, options):
            myoption = options.get('myoption', default_value)
            return_value = value  # Implement your conversion here
            return return_value
    
  • Optional: set a base converter (base expects a class name) to build on top of an existing converter, e.g. for the built-in ones: DictCoverter, NumpyArrayConverter, PandasDataFrameConverter, PandasSeriesConverter

  • Optional: register the converter: you can (a) register a type so that your converter becomes the default for this type during write operations and/or (b) you can register an alias that will allow you to explicitly call your converter by name instead of just by class name

The following examples should make it much easier to follow - it defines a DataFrame converter that extends the built-in DataFrame converter to add support for dropping nan’s:

from xlwings.conversion import Converter, PandasDataFrameConverter

class DataFrameDropna(Converter):

    base = PandasDataFrameConverter

    @staticmethod
    def read_value(builtin_df, options):
        dropna = options.get('dropna', False)  # set default to False
        if dropna:
            converted_df = builtin_df.dropna()
        else:
            converted_df = builtin_df
        # This will arrive in Python when using the DataFrameDropna converter for reading
        return converted_df

    @staticmethod
    def write_value(df, options):
        dropna = options.get('dropna', False)
        if dropna:
            converted_df = df.dropna()
        else:
            converted_df = df
        # This will be passed to the built-in PandasDataFrameConverter when writing
        return converted_df

Now let’s see how the different converters can be applied:

# Fire up a Workbook and create a sample DataFrame
sht = xw.Book().sheets[0]
df = pd.DataFrame([[1.,10.],[2.,np.nan], [3., 30.]])
  • Default converter for DataFrames:

    # Write
    sht.range('A1').value = df
    
    # Read
    sht.range('A1:C4').options(pd.DataFrame).value
    
  • DataFrameDropna converter:

    # Write
    sht.range('A7').options(DataFrameDropna, dropna=True).value = df
    
    # Read
    sht.range('A1:C4').options(DataFrameDropna, dropna=True).value
    
  • Register an alias (optional):

    DataFrameDropna.register('df_dropna')
    
    # Write
    sht.range('A12').options('df_dropna', dropna=True).value = df
    
    # Read
    sht.range('A1:C4').options('df_dropna', dropna=True).value
    
  • Register DataFrameDropna as default converter for DataFrames (optional):

    DataFrameDropna.register(pd.DataFrame)
    
    # Write
    sht.range('A13').options(dropna=True).value = df
    
    # Read
    sht.range('A1:C4').options(pd.DataFrame, dropna=True).value
    

These samples all work the same with UDFs, e.g.:

@xw.func
@arg('x', DataFrameDropna, dropna=True)
@ret(DataFrameDropna, dropna=True)
def myfunction(x):
    # ...
    return x

Note

Python objects run through multiple stages of a transformation pipeline when they are being written to Excel. The same holds true in the other direction, when Excel/COM objects are being read into Python.

Pipelines are internally defined by Accessor classes. A Converter is just a special Accessor which converts to/from a particular type by adding an extra stage to the pipeline of the default Accessor. For example, the PandasDataFrameConverter defines how a list of list (as delivered by the default Accessor) should be turned into a Pandas DataFrame.

The Converter class provides basic scaffolding to make the task of writing a new Converter easier. If you need more control you can subclass Accessor directly, but this part requires more work and is currently undocumented.