CODEWITHSUNDEEP
pythonpandasnumpy
james1395
james1395

Reputation: 239

Understand np.mean in python

I am trying to understand how np.mean is working in the following example: 

n = np.array([1,7,5,4])
m = pd.Series(data = [1,2,3,4],index = [1,2,3,4])

print(np.mean(n!=m))
## returns 0.5
print(np.mean(n[n!=m]))
## returns 4.0

could someone explain how the first output is working out the value 0.5 and how np.mean is treating the boolean series n!=m? I understand what the second part is doing

Upvotes: 1

Views: 4134

Answers (1)

Thiru kumaran
Thiru kumaran

Reputation: 1342

n !=m returns a boolean array .True and False values are treated as 1,0 respectively. In this case (0+1+1,0)/4 yields 0.5

>>> n != m
1    False
2     True
3     True
4    False

dtype: bool

n[n!=m] array([1, 7, 7, 1])

it means applying boolean selection on 'n'. it returns the [1,7,7,1]/4 yields 4

>>> condition = [n !=m]
>>> condition
[1    False
2     True
3     True
4    False
dtype: bool]
>>> n[condition]
array([1, 7, 7, 1])

Upvotes: 4

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