Edit multiple values with df.at()

Question

Why does

>>> offset = 2
>>> data = {'Value': [7, 9, 21, 22, 23, 100]}
>>> df = pd.DataFrame(data=data)
>>> df.at[:offset, "Value"] = 99
>>> df
Value
0   99
1   99
2   99
3   22
4   23
5   100

change values in indices [0, 1, 2]? I would expect them only to be changed in [0, 1] to be conform with regular slicing.

Like when I do

>>> arr = [0, 1, 2, 3, 4]
>>> arr[0:2]
[0, 1]

Answer 1

.at behaves like .loc, in that it selects rows/columns by label. Label slicing in pandas is inclusive. Note that .iloc, which performs slicing on the integer positions, behaves like you would expect. See this good answer for a motivation.

Also note that the pandas documentation suggests to use .at only when selecting/setting single values. Instead, use .loc.

Answer 2

On line 4, when you say :2, it means all rows from 0 to 2 or 0:2. If you want to change only the 3rd row, you should change it to 2:2