Search when a value in pandas.Series crosses / reaches a threshold

Question

Consider the following row

s = pd.Series([0,1,2,3,4,1,5,4,3,2,1])

Is there an easy way to find out how many times the value 2 is reached / crossed (without an obvious iterative solution)?

The expected result for the above example should be 4 (2 rows intersect or drop 4 times in a series).

Edit: updated case example

Answer 1

This is easy to understand using the method Series.shift. Since you only need to look to see if the number has crossed or not.

s = pd.Series([0,1,2,3,4,1,5,4,3,2,1])
df = pd.DataFrame({'s':s})
df['next_s'] = df.s.shift(-1)
line = 2

df
    s  next_s
0   0       1
1   1       2
2   2       3
3   3       4
4   4       1
5   1       5
6   5       4
7   4       3
8   3       2
9   2       1
10  1     NaN

Now you can use a simple vector conditional operator

df['cross'] = (
    ((df.s >= line) & (df.next_s < line)) |
    ((df.next_s > line) & (df.s <= line)) |
    (df.s == line))

df
    s  next_s  cross
0   0       1  False
1   1       2  False
2   2       3   True
3   3       4  False
4   4       1   True
5   1       5   True
6   5       4  False
7   4       3  False
8   3       2  False
9   2       1   True
10  1     NaN  False

Now it's pretty easy to sum booleans to get the score:

df.cross.sum()
4