These are harvest kg. Create a Series from [12, 3, 8, 6] with index ['rye', 'oat', 'bar', 'fig'] and assign it to s.

Take the max of s, assign the result to high, and print high.

Create a Series from [41, 50, 62] and assign it to s.

Keep the values of s that are greater than 50, assign them to high, and print high.

Write a function named rain_mm that takes a list of millimetres and returns a Series of the amounts greater than 0.

>>> rain_mm([6, 0, 2])
0    6
1    2
dtype: int64

Call rain_mm with [9, 0, 1, 4], assign the result to rain, and print rain.

Count the distinct values of s and assign the result to n.

Print n.

Create a Series of 4 numbers with index ['Miri', 'Vesa', 'Dori', 'Klea'], assign it to s, and print s.

Write a function named low_mask that takes a Series of weights s and a number n and returns the mask of weights less than n.

>>> s = pd.Series([9, 20, 4])
>>> low_mask(s, 10)
0     True
1    False
2     True
dtype: bool

Write a function named light_total that takes a Series of weights s and a number n and returns the total of the weights less than n. light_total must use low_mask.

>>> s = pd.Series([7, 15, 2])
>>> light_total(s, 10)
9

Write a function named depth_if_deep that takes a Series of depths s, a label label, and a number n, and returns the depth with that label if it is greater than n, or 0 if not.

>>> s = pd.Series([8, 1], index=["W1", "W2"])
>>> depth_if_deep(s, "W1", 5)
8

Call depth_if_deep with pd.Series([6, 2], index=['A', 'B']), B, and 1, assign the result to got, and print got.

Create a Series of 2 strings with index ['Fier', 'Vlora'], assign it to s, and print s.