Python Pandas: pivot table with count as aggfunc gives different result than value_counts

I am working with some data and end up with a situation where I want to cut a series like this:

df = pd.DataFrame({'A': 10000*[1], 'B': np.random.randint(0, 1001, 10000)})
df['level'] = pd.cut(df.B, bins = [0, 200, 400, 600, 800, 1000], labels = ['i', 'ii', 'iii', 'iv', 'v'])

Then, to count the number of values in each level, I find two different answers when I do one of the following:

df.level.value_counts(sort = False)

i      1934
ii     1994
iii    2055
iv     2056
v      1952
Name: level, dtype: int64

or

df.pivot_table(index = 'A', columns = 'level', values = 'B', aggfunc = 'count').loc[1]

level
i      1994
ii     2056
iii    1934
iv     1952
v      2055
Name: 1, dtype: int64

Shouldn't both methods give equal results?

from Python Pandas: pivot table with count as aggfunc gives different result than value_counts