Python Numpy methods to get output from array

We can use these methods to get different outputs from array.

`all`	Test all elements in given axis is True or not
any	Test all elements in given axis is True or not
argmax	Position of Max value along any axis
argmin	Position of Min value along any axis
argsort	Return the indices after sorting
choose	Select elements based on position
clip	Set a maximum and minimum values for elements
compress	Selected elements based on condition
copy	Copy one array to another array
cumprod	Cumulative product of elements
cumsum	Cumulative sum of elements
diagonal	Cumulative sum of elements
imag	Imaginary numbers from main array
amax	Highest number in the array
mean	Arithmetic mean along an axis
min	Arithmetic min along an axis
nonzero	Returns array of position of Non zero elements
partition	Elements sorted in order based on nth position element

all

Check if all elements are True in a given axis ( optional )

import numpy as my_np 
my_array=my_np.array([1,4,5]) # change the elements to see result. 
print(my_np.all(my_array)) # True

axis Try two dimensional array, we will use axis to specify AND operation in different axis.

my_array = my_np.array([[1,2],[3,False],[4,True],[5,False]])
print(my_np.all(my_array,axis=0))

Output

[ True False]

out We can place the result in another array.
keepdims If keepdims=True, the output will have the same number of dimensions as the input, maintaining the original structure but with size 1 along the reduced axis. This can be useful for broadcasting when performing further operations on the result.

my_array = my_np.array([[1,2],[3,False],[4,True],[5,False]])
print(my_np.all(my_array,axis=0,keepdims=True))

Output is here

[[ True False]]

any

Check whether array elements evaluates to an array. Returns True or false.

import numpy as my_np 
my_array = my_np.array([[1,2],[3,False],[4,True],[5,False]])
print(my_np.any(my_array))

Output is here

True

axis

print(my_np.any(my_array,axis=0))

Output

[ True  True]

keepdims

print(my_np.any(my_array,axis=0,keepdims=True))

Output

[[ True  True]]

argmax

Indices of the maximum value along with an axis ( optional )

import numpy as my_np 
my_array = my_np.array([4,3,7,0,1])
print(my_np.argmax(my_array))  # 2 
my_array = my_np.array([[1,5],[3,4],[9,True],[5,False]])
print(my_np.argmax(my_array))  # 4

axis Optional

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,True],[5,False]])
print(my_np.argmax(my_array,axis=0)) # [3 0]

out Output should be stored in a defined array of matching shape and dtype.

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,True],[5,False]])
my_array2=my_np.array([0,0])
my_np.argmax(my_array,axis=0,out=my_array2)
print(my_array2) # [3 0]

Using a three dimensional array

import numpy as my_np 
my_array = my_np.array([[[ 30,  1,  2],[ 3,  50,  5],[ 6,  7,  8]],
                        [[ 9, 10, 11],[32, 13, 48],[15, 16, 17]],
                        [[18, 19, 20],[21, 47, 23],[36, 25, 26]]])
print(my_array.argmax()) 
print(my_array.argmax(axis=0))

Output is here

4
[[0 2 2]
 [1 0 1]
 [2 2 2]]

argmin

Indices of the minimum value along with an axis ( optional )

import numpy as my_np 
my_array = my_np.array([4,3,7,0,1])
print(my_np.argmin(my_array))  # 3 
my_array = my_np.array([[1,5],[3,4],[9,True],[5,False]])
print(my_np.argmin(my_array))  # 7

axis Optional

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,True],[5,False]])
print(my_np.argmin(my_array,axis=0)) # [0 3]

out Optional, Result will be stored in this array. If given it must have the same shape and dtype.

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,True],[5,False]])
my_array2=my_np.array([0,0])
my_np.argmin(my_array,axis=0,out=my_array2)
print(my_array2) # [0 3]

argsort

Sort in given axis ( optional ) and returns an array of indices after sorting.

import numpy as my_np 
my_array = my_np.array([6,4,5,8,3])
print(my_np.argsort(my_array)) # [4 1 2 0 3]

import numpy as my_np 
my_array = my_np.array([[6,4],[5,8],[3,7]])
print(my_np.argsort(my_array))

output

[[1 0]
 [0 1]
 [0 1]]

axis(optional)

import numpy as my_np 
my_array = my_np.array([[6,4],[5,8],[3,7]])
print(my_np.argsort(my_array,axis=0))

Output

[[2 0]
 [1 2]
 [0 1]]

kind ( optional ) type of sort algorithm, values are 'quicksort', 'mergesort', 'heapsort'

import numpy as my_np 
my_array = my_np.array([[6,4],[5,8],[3,7]])
print(my_np.argsort(my_array,kind='heapsort'))

order (Optional), When fields are defined then given field is considered first for sorting. After sorting input field, rest of the fields are used.

import numpy as my_np 
my_array = my_np.array([('Alex',80,50.6),('Ronal',50,60.3),('Jack',55,72.3)],
                       dtype=[('name', 'U10'), ('mark', 'i4'), ('average', 'f4')])
print(my_np.argsort(my_array,order='name')) # [0 2 1]
print(my_np.argsort(my_array,order='mark')) # [1 2 0]
print(my_np.argsort(my_array,order='average')) # [0 1 2]

choose

Select elements based on input positions. choices array and it must be broadcastable

import numpy as my_np 
my_array = [[0, 5,7, 3], [19, 15, 12, 14],[27, 23, 21, 29], [39, 33, 38, 34]]
ch=[2,3,1,0]
print(my_np.choose(ch, my_array)) # [27 33 12 3]

mode (Optional ) value can be raise( default ), clip,wrap
clip : If value is more than n-1 then it is changed to n-1.
wrap : Math module is used to keep the value in range of n-1.
Check the example to know how mode is used

import numpy as my_np 
my_array = [[0, 5,7, 3], [19, 15, 12, 14],[27, 23, 21, 29], [39, 33, 38, 34]]
ch=[2,3,1,0]
print(my_np.choose(ch, my_array)) # [27 33 12 3]
ch=[2,4,1,0]
print(my_np.choose(ch, my_array,mode='clip')) # [27 33 12 3]
print(my_np.choose(ch, my_array,mode='wrap')) # [27 5 12 3] # 4 mod 4 is 0 
ch=[2,5,1,0]
print(my_np.choose(ch, my_array,mode='wrap')) # [27 15 12 3] # 5 mod 4 is 1

clip

Elements are restricted to a maximum and minimum value.
Here minimum value is given as 10 , no maximum value ( None )

import numpy as my_np 
my_array = [5,12,6,9,17]
print(my_np.clip(my_array,10,None)) # [10 12 10 10 17]

Maximum value is there, no minimum value

import numpy as my_np 
my_array = [5,12,6,9,17]
print(my_np.clip(my_array,None,10)) # [ 5 10  6  9 10]

Both Maximum and Minimum value is given

import numpy as my_np 
my_array = [5,12,6,9,17]
print(my_np.clip(my_array,10,12)) # [10 12 10 10 12]

out Optional : Matching array to store the result ( output )

import numpy as my_np 
my_array = [5,12,6,9,17]
my_out=my_np.array([0,0,0,0,0])
my_np.clip(my_array,10,12,out=my_out)
print(my_out) # [10 12 10 10 12]

compress

Returns an array with elements matching to the given pattern.
Here axis is not given so the condition is applied on the flattened array.

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,3],[5,7]])
print(my_np.compress([0,1,1,1,0,1],my_array)) # [5 3 4 3]

axis Optional along the axis

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,3],[5,7]])
print(my_np.compress([0,1,1],my_array,axis=0))

[[3 4]
 [4 3]]

out Optional : Matching array to store the output ( result )

import numpy as my_np 
my_array = my_np.array([[1,5],[3,4],[4,3],[5,7]])
my_out=my_np.array([[0,0],[0,0]])
my_np.compress([0,1,1],my_array,axis=0,out=my_out)
print(my_out)

Output is here

[[3 4]
 [4 3]]

copy

copy array

import numpy as my_np 
my_array = my_np.array([1,2,3,4])
my_array2=my_array.copy()
print(my_array2) # [1 2 3 4]

cumprod

Cumulative product of elements of array.

import numpy as my_np 
my_array = my_np.array([1,2,3,4])
print(my_np.cumprod(my_array)) # [ 1  2  6 24]

axis : Optional, Cumulative product based on the specified axis. If no axis is given ( default) then flattened array is used ( above code ). cumprod with axis=0

import numpy as my_np 
my_array = my_np.array([[1,2],
                       [3,4],
                        [5,6]])
print(my_np.cumprod(my_array,axis=0))

Output is here

[[ 1  2]
 [ 3  8]
 [15 48]]

axis=1

import numpy as my_np 
my_array = my_np.array([[1,2],
                        [3,4],
                        [5,6]])
print(my_np.cumprod(my_array,axis=1))

Output is here

[[ 1  2]
 [ 3 12]
 [ 5 30]]

out Optional , output can be stored in the array

import numpy as my_np 
my_array = my_np.array([1,2,3,4])
my_out=my_np.array([0,0,0,0])
my_np.cumprod(my_array,out=my_out) 
print(my_out) # [ 1  2  6 24]

cumsum

Cumulative sum of elements of array.

import numpy as my_np 
my_array = my_np.array([1,2,3,4])
print(my_np.cumsum(my_array)) # [ 1  3  6 10]

axis : Optional, Cumulative sum based on the specified axis. If no axis is given ( default) then flattened array is used ( above code ). cumsum with axis=0

import numpy as my_np 
my_array = my_np.array([[1,2],
                        [3,4],
                        [5,6]])
print(my_np.cumsum(my_array,axis=0))

Output is here

[[ 1  2]
 [ 4  6]
 [ 9 12]]

axis=1

import numpy as my_np 
my_array = my_np.array([[1,2],
                        [3,4],
                        [5,6]])
print(my_np.cumsum(my_array,axis=1))

Output is here

[[ 1  3]
 [ 3  7]
 [ 5 11]]

out Optional , output can be stored in the array

import numpy as my_np 
my_array = my_np.array([1,2,3,4])
my_out=my_np.array([0,0,0,0])
my_np.cumsum(my_array,out=my_out) 
print(my_out) # [ 1  3  6 10]

diagonal

Returns the diagonal elements of the input array.

import numpy as my_np 
my_array = my_np.array([[1,2],[3,4],[5,6]])
print(my_np.diagonal(my_array)) # [1 4]

import numpy as my_np 
my_array = my_np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
print(my_np.diagonal(my_array)) # [1 5 9]

axis1 axis2 : Optional, default for first axis1 = 0 and for second it is axis2=1

import numpy as my_np 
my_array = my_np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
print(my_np.diagonal(my_array,axis1=0,axis2=1)) # [1 5 9]

offset int , optional , offset from main diagonal.

import numpy as my_np 
my_array = my_np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
print(my_np.diagonal(my_array,offset=1)) # [2 6]

imag

We can get imaginary parts of an array with complex numbers.

import numpy as my_np 
my_array = my_np.array([[1+2j,2-4j],[3+3j,4-8j]])
print(my_array.imag)

Output

[[ 2. -4.]
 [ 3. -8.]]

amax

Maximum value of the array

import numpy as my_np 
my_array = my_np.array([2,1,0,3]) 
print(my_np.amax(my_array)) # 3

axis Optional : Maximum value along the given axis.

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
print(my_np.amax(my_array)) # 7 
print(my_np.amax(my_array,axis=0)) # [6 7] 
print(my_np.amax(my_array,axis=1)) # [5 6 2 7]

out Optional, output can be stored in the given array.

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
my_out=my_np.array([0,0])
my_np.amax(my_array,axis=0,out=my_out)
print(my_out) # [6 7]

keepdims optional , bool

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
print(my_np.amax(my_array,axis=0,keepdims=True)) # [6 7]

mean

Arithmetic mean of the elements, if axis is not provided then average is taken over the flattened array.

import numpy as my_np 
my_array = my_np.array([[1,2],[3,4],[5,6]]) 
print(my_np.mean(my_array)) # 3.5

axis: optional, mean along the given axis

import numpy as my_np 
my_array = my_np.array([[1,2],[3,4],[5,6]]) 
print(my_np.mean(my_array,axis=0)) # [3. 4.]

import numpy as my_np 
my_array = my_np.array([[1,2],[3,4],[5,6]]) 
print(my_np.mean(my_array,axis=1)) # [1.5 3.5 5.5]

dtypeOptional , type to use while calculating mean.

import numpy as my_np 
my_array = my_np.array([[1.5,2.4],[3.6,4.8],[5.2,6.4]]) 
print((my_np.mean(my_array,dtype=my_np.int16))) # 3 
print((my_np.mean(my_array,dtype=my_np.float32))) # 3.9833333
print((my_np.mean(my_array,dtype=my_np.float64))) # 3.983333333333333

out: Optional output can be stored in the given array.

min

Minimum value of the array

import numpy as my_np 
my_array = my_np.array([2,1,3]) 
print(my_np.amin(my_array)) # 1

axis Optional : Minimum value along the given axis.

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
print(my_np.amin(my_array)) # 0 
print(my_np.amin(my_array,axis=0)) # [0 1] 
print(my_np.amin(my_array,axis=1)) # [4 3 1 0]

out Optional, output can be stored in the given array.

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
my_out=my_np.array([0,0])
my_np.amin(my_array,axis=0,out=my_out)
print(my_out) # [0 1]

keepdims optional , bool

import numpy as my_np 
my_array = my_np.array([[5,4],[6,3],[2,1],[0,7]]) 
print(my_np.amin(my_array,axis=0,keepdims=True)) # [0 1]

nonzero

Returns array of positions of non zero elements

import numpy as my_np 
my_array = my_np.array([2,1,0,3]) 
print(my_np.nonzero(my_array)) # (array([0, 1, 3], dtype=int64),)

import numpy as my_np 
my_array = my_np.array([[2,-11,3],[14,3,-5],[-15,6,7]]) 
print(my_np.nonzero(my_array>1))

Output

(array([0, 0, 1, 1, 2, 2], dtype=int64), array([0, 2, 0, 1, 1, 2], dtype=int64))

partition

Elements are sorted based on value at k^th position, smaller elements are placed before it and equal or higher elements behind it.

import numpy as my_np 
my_array = my_np.array([2,1,0,3]) 
print(my_np.partition(my_array,2)) # [0 1 2 3]

Numpy

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Numpy Array Methods

all

any

argmax

argmin

argsort

choose

clip

compress

copy

cumprod

cumsum

diagonal

imag

amax

mean

min

nonzero

partition

Subhendu Mohapatra