# 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 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. All code samples below depend on the following import: ```pycon >>> import xlwings as xw ``` **Syntax:** | Action | **Range objects** | **UDFs** | |-------------|-----------------------------------------------------------|-------------------------------------| | **reading** | `myrange.options(convert=None, **kwargs).value` | `@arg('x', convert=None, **kwargs)` | | **writing** | `myrange.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:: ```none myrange.options(pd.DataFrame, index=False, numbers=int).value ``` ## Default Converter 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']]`. The following options can be set: ### ndim Force the value to have either 1 or 2 dimensions regardless of the shape of the range: ```pycon >>> 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: ```pycon >>> 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`: ```python >>> sheet['A1'].value = 1 >>> sheet['A1'].value 1.0 >>> sheet['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: ```python @xw.func @xw.arg('x', numbers=int) def myfunction(x): # all numbers in x arrive as int return x ``` #### NOTE 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: ```python >>> import datetime as dt >>> sheet['A1'].options(dates=dt.date).value ``` - UDFs (decorator): ```python @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: ```pycon >>> 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: ```pycon >>> sheet['A1'].options(empty='NA').value ``` - UDFs (decorator): ```python @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: `sheet['A1'].options(transpose=True).value = [1, 2, 3]` - UDFs: ```python @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: ```python >>> 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]] ``` #### NOTE 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, xlwings splits the transfer into row chunks automatically: reads above 4,000,000 cells (1,500,000 on macOS, where a bigger AppleScript reply fails) and writes above 100,000 cells are chunked on desktop Excel (Windows and macOS) and the remote engines (xlwings Lite and xlwings Server). This reduces timeout and memory pressure on desktop Excel. For xlwings Lite’s on-demand `await myrange.get_value()` reads, automatic chunking keeps each Office.js read below its all-platform 5,000,000-cell limit. xlwings Server and synchronous xlwings Lite books still load cell values eagerly before Python conversion; Python-side chunking does not protect that initial transfer from the limit. Use an async book in xlwings Lite to avoid that eager transfer. xlwings Reader (`mode="r"`) reads unchunked by default. Chunking applies to the normal value pipeline, including DataFrames, NumPy arrays, lists and scalar fills; `raw_value` is not chunked. Set `chunksize` explicitly to tune the number of rows per chunk in either direction, or set `chunksize=None` to disable chunking. Both defaults count cells, not bytes, so you may still need a much smaller explicit `chunksize` if your cells hold long strings—Excel on the web additionally caps each request and response at 5 MB—or if you hit a timeout or a memory error. Note that a chunked write that fails partway through leaves the earlier chunks written. ```python 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: ```python # 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 #### Versionadded 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 #### Versionadded Added in version 0.28.1. #### NOTE 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: ```python 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: ![./images/formatter.png](images/formatter.png) 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: ```python def table(rng: xw.Range, values: list[list]): ``` ## 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 a 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`: ![image](images/dict_converter.png) ```pycon >>> 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. ```pycon >>> 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. ```pycon >>> sheet = xw.sheets.active >>> sheet['A1:C2'].options("json").value '[["2024-01-01T00:00:00", "text", true], [null, 42.0, false]]' ``` ### 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** ```pycon >>> 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 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:** ![image](images/series_conv.png) ```pycon >>> 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 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:** ![image](images/df_converter.png) ```python >>> 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: ```python @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:** ```python # 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 ``` ```python # 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 and ‘raw’ converters Technically speaking, these are “no-converters”. * If you need access to the `xlwings.Range` object directly, you can do: ```python @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: ```pycon >>> 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)) ``` ## 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 `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: ```python 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: 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: ```python 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: ```python # 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: ```python # Write sheet['A1'].value = df # Read sheet['A1:C4'].options(pd.DataFrame).value ``` * DataFrameDropna converter: ```python # Write sheet['A7'].options(DataFrameDropna, dropna=True).value = df # Read sheet['A1:C4'].options(DataFrameDropna, dropna=True).value ``` * Register an alias (optional): ```python 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): ```python 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.: ```python @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 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.