import numpy as np
x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])
v = np.array([9,10])
w = np.array([11, 12])
# Inner product of vectors; both produce 219
print(v.dot(w))
print(np.dot(v, w))
# Matrix / vector product; both produce the rank 1 array [29 67]
print(x.dot(v))
print(np.dot(x, v))
# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
# [43 50]]
print(x.dot(y))
print(np.dot(x, y))
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You can also compute the sum and prod product:
import numpy as np
x = np.array([[1,2],[3,4]])
print(np.sum(x)) # Compute sum of all elements; prints "10"
print(np.sum(x, axis=0)) # Compute sum of each column; prints "[4 6]"
print(np.sum(x, axis=1)) # Compute sum of each row; prints "[3 7]"
print(np.prod(x)) # multiply all the elements
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There are a wide range of useful functions, below demonstrates a few common ones:
import numpy as np
a = np.array([2, 1, 9], float)
print( a.mean() ) # mean
print( a.var() ) # variance
print( a.min(), a.max() ) #min and max
print( a.argmin(), a.argmax() ) #min and max indices
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In addition to the mean, var, and std functions, NumPy supplies several other methods for
returning statistical features of arrays:
import numpy as np
a = np.array([1, 4, 3, 8, 9, 2, 3], float)
print( np.median(a) ) #median
print( np.corrcoef(a) ) # correlation coefficient
print( np.cov(a) ) #covariance
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