# 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.range('A1').value = 1
>>> sheet.range('A1').value
1.0
>>> sheet.range('A2').value = 'Hello'
>>> sheet.range('A2').value
'Hello'
>>> sheet.range('A3').value is None
True
>>> sheet.range('A4').value = dt.datetime(2000, 1, 1)
>>> sheet.range('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.range('A1').value = [[1],[2],[3],[4],[5]] # Column orientation (nested list) >>> sheet.range('A1:A5').value [1.0, 2.0, 3.0, 4.0, 5.0] >>> sheet.range('A1').value = [1, 2, 3, 4, 5] >>> sheet.range('A1:E1').value [1.0, 2.0, 3.0, 4.0, 5.0]

To force a single cell to arrive as list, use:

>>> sheet.range('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.range('A1:A5').options(ndim=2).value [[1.0], [2.0], [3.0], [4.0], [5.0]] >>> sheet.range('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.range('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.range('A1').value = [[1,2], [3,4]]
>>> rng1 = sheet.range('A1').expand('table') # or just .expand()
>>> rng2 = sheet.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]]
>>> sheet.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]]
```

`'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.range('A1').value = np.eye(3)
>>> sheet.range('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.range('A1').value = df
>>> sheet.range('A1:C3').options(pd.DataFrame).value
one two
0 1.1 2.2
1 3.3 NaN
# options: work for reading and writing
>>> sheet.range('A5').options(index=False).value = df
>>> sheet.range('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.range('A1').value = s
>>> sheet.range('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.range('A1').value = np.eye(10)`

## Chunking: Read/Write big DataFrames etc.¶

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
```