データ構造¶

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.

これ以降のすべてのコードサンプルは次のインポートを前提としています:

>>> import xlwings as xw

単独セル¶

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)

リスト¶

1次元リスト: Excel上で行または列形式のRangeは、単純なリスト形式で読み込まれます。したがって、一度Pythonに読み込まれると元の形状（行であるか列であるか）がわからなくなります。このことが問題となるようなら、次の・で紹介するの方法をとりましょう:
```
>>> 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]
```
注釈

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次元のRangeは自動的に入れ子のリストとして読み込まれます。逆に（入れ子の）リストをExcelのRangeに代入するには、左上のセルを指定します。次の例では、値をPythonに読み込む時のインデックスの使い方も示しています:
```
>>> 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]]
```

注釈

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の拡張¶

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.

注釈

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

注釈

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)

チャンク(Chunking): 巨大なDataFrame等の読み書き¶

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

読み込みについても同様に:

# 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