Data Structures Tutorial

This tutorial gives you a quick introduction to the most common use cases and default behaviour of xlwings when reading and writing values. For an in-depth documentation of how to control the behavior using the options method, have a look at Converters and Options.

All code samples below depend on the following import:

>>> import xlwings as xw

Single Cells

Single cells are by default returned either as float, unicode, None or datetime objects, depending on whether the cell contains a number, a string, is empty or represents a date:

>>> import datetime as dt
>>> sheet = xw.Book().sheets[0]
>>> sheet['A1'].value = 1
>>> sheet['A1'].value
1.0
>>> sheet['A2'].value = 'Hello'
>>> sheet['A2'].value
'Hello'
>>> sheet['A3'].value is None
True
>>> sheet['A4'].value = dt.datetime(2000, 1, 1)
>>> sheet['A4'].value
datetime.datetime(2000, 1, 1, 0, 0)

Lists

1d lists: Ranges that represent rows or columns in Excel are returned as simple lists, which means that once they are in Python, you’ve lost the information about the orientation. If that is an issue, the next point shows you how to preserve this info:
```
>>> sheet = xw.Book().sheets[0]
>>> sheet['A1'].value = [[1],[2],[3],[4],[5]]  # Column orientation (nested list)
>>> sheet['A1:A5'].value
[1.0, 2.0, 3.0, 4.0, 5.0]
>>> sheet['A1'].value = [1, 2, 3, 4, 5]
>>> sheet['A1:E1'].value
[1.0, 2.0, 3.0, 4.0, 5.0]
```
To force a single cell to arrive as list, use:
```
>>> sheet['A1'].options(ndim=1).value
[1.0]
```
Note

To write a list in column orientation to Excel, use transpose: sheet.range('A1').options(transpose=True).value = [1,2,3,4]
2d lists: If the row or column orientation has to be preserved, set ndim in the Range options. This will return the Ranges as nested lists (“2d lists”):
```
>>> sheet['A1:A5'].options(ndim=2).value
[[1.0], [2.0], [3.0], [4.0], [5.0]]
>>> sheet['A1:E1'].options(ndim=2).value
[[1.0, 2.0, 3.0, 4.0, 5.0]]
```
2 dimensional Ranges are automatically returned as nested lists. When assigning (nested) lists to a Range in Excel, it’s enough to just specify the top left cell as target address. This sample also makes use of index notation to read the values back into Python:
```
>>> sheet['A10'].value = [['Foo 1', 'Foo 2', 'Foo 3'], [10, 20, 30]]
>>> sheet.range((10,1),(11,3)).value
[['Foo 1', 'Foo 2', 'Foo 3'], [10.0, 20.0, 30.0]]
```

Note

Try to minimize the number of interactions with Excel. It is always more efficient to do sheet.range('A1').value = [[1,2],[3,4]] than sheet.range('A1').value = [1, 2] and sheet.range('A2').value = [3, 4].

Range expanding

You can get the dimensions of Excel Ranges dynamically through either the method expand or through the expand keyword in the options method. While expand gives back an expanded Range object, options are only evaluated when accessing the values of a Range. The difference is best explained with an example:

>>> sheet = xw.Book().sheets[0]
>>> sheet['A1'].value = [[1,2], [3,4]]
>>> range1 = sheet['A1'].expand('table')  # or just .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]]

'table' expands to 'down' and 'right', the other available options which can be used for column or row only expansion, respectively.

Note

Using expand() together with a named Range as top left cell gives you a flexible setup in Excel: You can move around the table and change its size without having to adjust your code, e.g. by using something like sheet.range('NamedRange').expand().value.

NumPy arrays

NumPy arrays work similar to nested lists. However, empty cells are represented by nan instead of None. If you want to read in a Range as array, set convert=np.array in the options method:

>>> import numpy as np
>>> sheet = xw.Book().sheets[0]
>>> sheet['A1'].value = np.eye(3)
>>> sheet['A1'].options(np.array, expand='table').value
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

Pandas DataFrames

>>> sheet = xw.Book().sheets[0]
>>> df = pd.DataFrame([[1.1, 2.2], [3.3, None]], columns=['one', 'two'])
>>> df
   one  two
0  1.1  2.2
1  3.3  NaN
>>> sheet['A1'].value = df
>>> sheet['A1:C3'].options(pd.DataFrame).value
   one  two
0  1.1  2.2
1  3.3  NaN
# options: work for reading and writing
>>> sheet['A5'].options(index=False).value = df
>>> sheet['A9'].options(index=False, header=False).value = df

Pandas Series

>>> import pandas as pd
>>> import numpy as np
>>> sheet = xw.Book().sheets[0]
>>> s = pd.Series([1.1, 3.3, 5., np.nan, 6., 8.], name='myseries')
>>> s
0    1.1
1    3.3
2    5.0
3    NaN
4    6.0
5    8.0
Name: myseries, dtype: float64
>>> sheet['A1'].value = s
>>> sheet['A1:B7'].options(pd.Series).value
0    1.1
1    3.3
2    5.0
3    NaN
4    6.0
5    8.0
Name: myseries, dtype: float64

Note

You only need to specify the top left cell when writing a list, a NumPy array or a Pandas DataFrame to Excel, e.g.: sheet['A1'].value = np.eye(10)

Chunking: Read/Write big DataFrames etc.

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 with classic xlwings (Windows and macOS) and the remote engines (xlwings Lite and xlwings Server). This reduces timeout and memory pressure with classic xlwings. For xlwings Lite’s asynchronous 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, scalar fills, and formula writes; raw_value is not chunked. With classic xlwings, scalar fills are never chunked, even with an explicit chunksize: Excel fills a scalar in a single call, so a formula like =A1*2 keeps adjusting its relative references across the whole range.

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.

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