Numpy&Pandas 002: Basic Operation in NumPy

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We make two arrays first.

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a = np.array([[2,2,2],[3,3,3],[4,4,4]])
b = np.ones((3,3))

#array([[2, 2, 2],
#       [3, 3, 3],
#       [4, 4, 4]])
#array([[1., 1., 1.],
#       [1., 1., 1.],
#       [1., 1., 1.]])

Arithmetic Operation

As numbers, it is possible to add, minus, multiple, divide…

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#add
a+b

#array([[3., 3., 3.],
#       [4., 4., 4.],
#       [5., 5., 5.]])

#minus
a-b

#array([[1., 1., 1.],
#       [2., 2., 2.],
#       [3., 3., 3.]])

#mutiple
a*b

#array([[2., 2., 2.],
#       [3., 3., 3.],
#       [4., 4., 4.]])

#divide
b/a

#array([[0.5       , 0.5       , 0.5       ],
#       [0.33333333, 0.33333333, 0.33333333],
#       [0.25      , 0.25      , 0.25      ]])\

#pow
a**2

#array([[ 4,  4,  4],
#      [ 9,  9,  9],
#       [16, 16, 16]], dtype=int32)

For all of them, it executes corresponding element in the array.

Functional Operation

Besides, it can also do for Trigonometric function.

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np.sin(a)

#array([[ 0.90929743,  0.90929743,  0.90929743],
#       [ 0.14112001,  0.14112001,  0.14112001],
#       [-0.7568025 , -0.7568025 , -0.7568025 ]])

For the cos and tan or others is same.

We can do some conditions, but the output is boolean value

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a>3

#array([[False, False, False],
#       [False, False, False],
#       [ True,  True,  True]]


a==2

#array([[ True,  True,  True],
#       [False, False, False],
#       [False, False, False]])

If we wanna compute the value of multiplication of the matrix, we need a method of the numpy

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np.dot(a,b)

#array([[ 6.,  6.,  6.],
#       [ 9.,  9.,  9.],
#       [12., 12., 12.]])

For an array, we can use some inner functions to compute it conviently

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np.sum(a)	#27
np.max(a)  	#4
np.min(a)	#2

Specially, we can add one more parameter and it will execute on every rows or columns

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np.sum(a, axis=0)	#column
#array([9, 9, 9])

np.sum(a, axis=1)	#row
#array([ 6,  9, 12])

It is available for min and max as well.

Besides, we can output the index of the maximum number or minimum

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# make a new array
A = np.arange(12,0, -1).reshape((3,4))

#array([[12, 11, 10,  9],
#       [ 8,  7,  6,  5],
#       [ 4,  3,  2,  1]])

np.argmax(A)	#0
np.argmin(A)	#11

Computing the value of average

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np.mean(A)	#6.5
A.mean()	#6.5

np.average(A)	#6.5

And median

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np.median(A)	#6.5

There is a method, it makes an increase array and the element is summed by last element 

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np.cumsum(A)
#array([12, 23, 33, 42, 50, 57, 63, 68, 72, 75, 77, 78], dtype=int32)

Similarly, a minus method. But there are some differences. This method is still keep the type of the original array and the number of the element in every row will decrease one.

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np.diff(A)
#array([[-1, -1, -1],
#       [-1, -1, -1],
#       [-1, -1, -1]])

A detection method to check whether it is zero or not, if it is not zero, the position of it will be written down. e.g. (0,0), (0,1), (0,2)…

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np.nonzero(A) 
#(array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int64),
# array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int64))

#it means (0,1) (0,1)... of the original array are not zero

It also has sort method, differently, it sorts every row, not all elements

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np.sort(A)
#array([[ 9, 10, 11, 12],
#       [ 5,  6,  7,  8],
#       [ 1,  2,  3,  4]])

It is possible to transpose matrix

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np.transpose(A)
# or
A.T

#array([[12,  8,  4],
#       [11,  7,  3],
#       [10,  6,  2],
#       [ 9,  5,  1]])

And there is a method called clip, it has three parameters, first is the array, second is the minimal value, last is the maximal value and it will return another array.

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np.clip(A, 3, 9)

#array([[9, 9, 9, 9],
#       [8, 7, 6, 5],
#       [4, 3, 3, 3]])

As we see, the element bigger than you set, they will be the set one and for minimal value is also.

Lastly, let us see random method.

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r = np.random.random((2,2))

#array([[0.38912605, 0.55904679],
#       [0.11785658, 0.20156226]])

It will generate the number between 0 to 1.

Thanks for your reading, I hope it is useful for you.

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