コンバーターおよびオプション

コンバーターはv0.7.0で導入された機能で、Excelのセル範囲やその値の 読み込み 、書き込み 時の変換方法を定義するものです。この機能は、xlwings.Range オブジェクトと User Defined Functions (UDF)の両方で同じように利用できます。

Converters are explicitly 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.

以下、全てのサンプル コードは次のインポートを前提としています:

>>> import xlwings as xw

シンタックス:

Action	Range objects	UDFs
読み込み	myrange.options(convert=None, **kwargs).value	@arg('x', convert=None, **kwargs)
書き込み	myrange.options(convert=None, **kwargs).value = myvalue	@ret(convert=None, **kwargs)

注釈

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::

myrange.options(pd.DataFrame, index=False, numbers=int).value

デフォルト コンバーター

If no options are set, the following default conversions are applied when accessing Range.value:

• Numbers -> floats

• Text -> str

• Date and/or time -> datetime

• TRUE or FALSE -> bool

• Empty cell -> None

• Windows only: Currency -> Decimal, truncated to 4 decimals

Columns/rows are read in as lists, e.g. [None, 1.0, 'a string'] and 2d cell ranges are read in as list of lists, e.g. [[None, 1.0, 'a string'], [None, 2.0, 'another string']].

以下のオプションを設定できます:

ndim

セル範囲の形に関わらず、強制的に値の次元を1か2にします::

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

To preserve the vertical orientation of columns, use ndim="natural". This returns scalars for single cells, 1D lists for horizontal ranges, and 2D lists for vertical or multi-row ranges:

>>> sheet['A1'].value = 1
>>> sheet['A1'].options(ndim="natural").value
1.0
>>> sheet['A1'].value = ["Industry", "Country", "Employees", "Revenue"]
>>> sheet['A1:D1'].options(ndim="natural").value
['Industry', 'Country', 'Employees', 'Revenue']
>>> sheet['A1'].value = [["3M"], ["AbbVie"], ["Apple"]]
>>> sheet['A1:A3'].options(ndim="natural").value  # Key difference to default
[['3M'], ['AbbVie'], ['Apple']]
>>> sheet['A1'].value = [[1, 2, 3], [4, 5, 6]]
>>> sheet['A1:C2'].options(ndim="natural").value
[[1, 2, 3], [4, 5, 6]]

numbers

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

>>> sheet['A1'].value = 1
>>> sheet['A1'].value
1.0
>>> sheet['A1'].options(numbers=int).value
1

それ以外にも、1つのfloat引数を受け取る関数や型を設定することもできます。

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

注釈

Excel delivers all numbers as floats in the interactive mode, which is the reason why the int converter rounds numbers first before turning them into integers. Otherwise it could happen that e.g., 5 might be returned as 4 in case it is represented as a floating point number that is slightly smaller than 5. Should you require Python's original int in your converter, use raw int` instead.

dates

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

• Range:

>>> import datetime as dt
>>> sheet['A1'].options(dates=dt.date).value

• UDFs (decorator):

@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)
>>> sheet['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:

>>> sheet['A1'].options(empty='NA').value

• UDFs (decorator):

@xw.arg('x', empty='NA')

transpose

transposeは読み込み/書き込み時に機能し、例えばリストを列方向に書き込めます:

• Range: sheet['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
>>> sheet = xw.Book().sheets[0]
>>> sheet['A1'].value = [[1,2], [3,4]]
>>> range1 = sheet['A1'].expand()
>>> range2 = sheet['A1'].options(expand='table')
>>> range1.value
[[1.0, 2.0], [3.0, 4.0]]
>>> range2.value
[[1.0, 2.0], [3.0, 4.0]]
>>> sheet['A3'].value = [5, 6]
>>> range1.value
[[1.0, 2.0], [3.0, 4.0]]
>>> range2.value
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]

注釈

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

chunksize

When you read and write from or to big ranges, you may have to chunk them or you will hit a timeout or a memory error. The ideal chunksize will depend on your system and size of the array, so you will have to try out a few different chunksizes to find one that works well:

import pandas as pd
import numpy as np
sheet = xw.Book().sheets[0]
data = np.arange(75_000 * 20).reshape(75_000, 20)
df = pd.DataFrame(data=data)
sheet['A1'].options(chunksize=10_000).value = df

And the same for reading:

# As DataFrame
df = sheet['A1'].expand().options(pd.DataFrame, chunksize=10_000).value
# As list of list
df = sheet['A1'].expand().options(chunksize=10_000).value

err_to_str

Added in version 0.28.0.

If True, will include cell errors such as #N/A as strings. By default, they will be converted to None.

formatter

Added in version 0.28.1.

注釈

You can't use formatters with Excel tables.

The formatter option accepts the name of a function. The function will be called after writing the values to Excel and allows you to easily style the range in a very flexible way. How it works is best shown with a little example:

import pandas as pd
import xlwings as xw

sheet = xw.Book().sheets[0]

def table(rng: xw.Range, df: pd.DataFrame):
    """This is the formatter function"""
    # Header
    rng[0, :].color = "#A9D08E"

    # Rows
    for ix, row in enumerate(rng.rows[1:]):
        if ix % 2 == 0:
            row.color = "#D0CECE"  # Even rows

    # Columns
    for ix, col in enumerate(df.columns):
        if "two" in col:
            rng[1:, ix].number_format = "0.0%"

df = pd.DataFrame(data={"one": [1, 2, 3, 4], "two": [5, 6, 7, 8]})
sheet["A1"].options(formatter=table, index=False).value = df

Running this code will format the DataFrame like this:

The formatter's signature is: def myformatter(myrange, myvalues) where myrange corresponds to the range where myvalues are written to. myvalues is simply what you assign to the value property in the last line of the example. Since we're using this with a DataFrame, it makes sense to name the argument accordingly and using type hints will help your editor with auto-completion. If you would use a nested list instead of a DataFrame, you would write something like this instead:

def table(rng: xw.Range, values: list[list]):

ビルトイン コンバーター

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).

これら以外の型に対応するカスタム コンバーターの作成や登録もできます（後述）。

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

辞書コンバーター

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

>>> sheet = xw.sheets.active
>>> sheet['A1:B2'].options(dict).value
{'a': 1.0, 'b': 2.0}
>>> sheet['A4:B5'].options(dict, transpose=True).value
{'a': 1.0, 'b': 2.0}

Note: instead of dict, you can also use OrderedDict from collections.

Tuple converter

Get the values as (nested) tuples instead of (nested) lists. This can be helpful in connection with caching, as tuples are immutable and hashable.

>>> sheet = xw.sheets.active
>>> sheet['A1:B2'].options(tuple).value
(('a', 1.0), ('b', 2.0))

JSON converter

Read and write values as JSON-formatted strings. This is especially useful to interact with LLMs.

>>> sheet = xw.sheets.active
>>> sheet['A1:C2'].options("json").value
'[["2024-01-01T00:00:00", "text", true], [null, 42.0, false]]'

Numpy array コンバーター

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

Pandas Series コンバーター

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.

例

>>> sheet = xw.Book().sheets[0]
>>> s = sheet['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

Pandas DataFrame コンバーター

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.

例

>>> sheet = xw.Book().sheets[0]
>>> df = sheet['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:
>>> sheet['A1'].value = df

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

The same sample for UDF (starting in cell 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

Polars DataFrame and Series converters

Polars DataFrames work almost the same as pandas DataFrames. But since polars DataFrames don't have an index and don't support MultiIndex headers, the index option isn't available and the header option only accepts True (default) or False.

Examples:

# This is a script example

import datetime as dt
import polars as pl
import xlwings as xw

df = pl.DataFrame(
    {
        "name": ["Alice Archer", "Ben Brown", "Chloe Cooper", "Daniel Donovan"],
        "birthdate": [
            dt.date(1997, 1, 10),
            dt.date(1985, 2, 15),
            dt.date(1983, 3, 22),
            dt.date(1981, 4, 30),
        ],
        "weight": [57.9, 72.5, 53.6, 83.1],
        "height": [1.56, 1.77, 1.65, 1.75],
    }
)

book = xw.Book()
sheet = book.sheets[0]
sheet["A1"].value = df  # writing
df_read = sheet["A1"].expand().options(pl.DataFrame).value  # reading

# This is a UDF example

import polars as pl

@xw.func
def myfunction(df: pl.DataFrame):
   # df is a polars DataFrame, do something with it
   return df

xw.Range コンバーターおよび 'raw' コンバーター

技術的には、これらは "コンバーターではありません"。

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

  @xw.func
  @xw.arg('x', '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:

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

カスタム コンバーター

自身で作成したコンバーターを実装する手順は以下のとおりです:

• 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 options method, e.g. when calling myrange.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: DictConverter, NumpyArrayConverter, PandasDataFrameConverter, PandasSeriesConverter

• Optional: コンバーターを登録します: (a) 型を登録すれば、その型を書き込む際ののデフォルト コンバーターになります。そして/または (b) エイリアスを登録すれば、正確なクラス名ではなく、名前でコンバーターを指定できるようになります。

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
sheet = xw.Book().sheets[0]
df = pd.DataFrame([[1.,10.],[2.,np.nan], [3., 30.]])

• Default converter for DataFrames:

# Write
sheet['A1'].value = df

# Read
sheet['A1:C4'].options(pd.DataFrame).value

• DataFrameDropna converter:

# Write
sheet['A7'].options(DataFrameDropna, dropna=True).value = df

# Read
sheet['A1:C4'].options(DataFrameDropna, dropna=True).value

• Register an alias (optional):

DataFrameDropna.register('df_dropna')

# Write
sheet['A12'].options('df_dropna', dropna=True).value = df

# Read
sheet['A1:C4'].options('df_dropna', dropna=True).value

• Register DataFrameDropna as default converter for DataFrames (optional):

DataFrameDropna.register(pd.DataFrame)

# Write
sheet['A13'].options(dropna=True).value = df

# Read
sheet['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

注釈

Pythonオブジェクトは、複数の変換パイプラインのステージを経て、Excelに書き出されます。反対方向、つまり、Excel/COM オブジェクトがPythonに読み込まれる際も同様です。

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 lists (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.