NumPy is a Python package which means ‘Numerical Python’. It is the library for logical computing, which contains a powerful n-dimensional array object, gives tools to integrate C, C++ and so on. It is likewise helpful in linear based math, arbitrary number capacity and so on. NumPy exhibits can likewise be utilized as an effective multi-dimensional compartment for generic data. NumPy Array: Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can initialize NumPy arrays from nested Python lists and access it elements. A Numpy array on a structural level is made up of a combination of:
- The Data pointer indicates the memory address of the first byte in the array.
- The Data type or dtype pointer describes the kind of elements that are contained within the array.
- The shape indicates the shape of the array.
- The strides are the number of bytes that should be skipped in memory to go to the next element.
Operations on Numpy Array
Arithmetic Operations:
# Python code to perform arithmetic # operations on NumPy array import numpy as np
# Initializing the array arr1 = np.arange( 4 , dtype = np.float_).reshape( 2 , 2 )
print ( 'First array:' )
print (arr1)
print ( '\nSecond array:' )
arr2 = np.array([ 12 , 12 ])
print (arr2)
print ( '\nAdding the two arrays:' )
print (np.add(arr1, arr2))
print ( '\nSubtracting the two arrays:' )
print (np.subtract(arr1, arr2))
print ( '\nMultiplying the two arrays:' )
print (np.multiply(arr1, arr2))
print ( '\nDividing the two arrays:' )
print (np.divide(arr1, arr2))
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Output:
First array: [[ 0. 1.] [ 2. 3.]] Second array: [12 12] Adding the two arrays: [[ 12. 13.] [ 14. 15.]] Subtracting the two arrays: [[-12. -11.] [-10. -9.]] Multiplying the two arrays: [[ 0. 12.] [ 24. 36.]] Dividing the two arrays: [[ 0. 0.08333333] [ 0.16666667 0.25 ]]
numpy.reciprocal() This function returns the reciprocal of argument, element-wise. For elements with absolute values larger than 1, the result is always 0 and for integer 0, overflow warning is issued. Example:
# Python code to perform reciprocal operation # on NumPy array import numpy as np
arr = np.array([ 25 , 1.33 , 1 , 1 , 100 ])
print ( 'Our array is:' )
print (arr)
print ( '\nAfter applying reciprocal function:' )
print (np.reciprocal(arr))
arr2 = np.array([ 25 ], dtype = int )
print ( '\nThe second array is:' )
print (arr2)
print ( '\nAfter applying reciprocal function:' )
print (np.reciprocal(arr2))
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Output
Our array is: [ 25. 1.33 1. 1. 100. ] After applying reciprocal function: [ 0.04 0.7518797 1. 1. 0.01 ] The second array is: [25] After applying reciprocal function: [0]
numpy.power() This function treats elements in the first input array as the base and returns it raised to the power of the corresponding element in the second input array.
# Python code to perform power operation # on NumPy array import numpy as np
arr = np.array([ 5 , 10 , 15 ])
print ( 'First array is:' )
print (arr)
print ( '\nApplying power function:' )
print (np.power(arr, 2 ))
print ( '\nSecond array is:' )
arr1 = np.array([ 1 , 2 , 3 ])
print (arr1)
print ( '\nApplying power function again:' )
print (np.power(arr, arr1))
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Output:
First array is: [ 5 10 15] Applying power function: [ 25 100 225] Second array is: [1 2 3] Applying power function again: [ 5 100 3375]
numpy.mod() This function returns the remainder of division of the corresponding elements in the input array. The function numpy.remainder() also produces the same result.
# Python code to perform mod function # on NumPy array import numpy as np
arr = np.array([ 5 , 15 , 20 ])
arr1 = np.array([ 2 , 5 , 9 ])
print ( 'First array:' )
print (arr)
print ( '\nSecond array:' )
print (arr1)
print ( '\nApplying mod() function:' )
print (np.mod(arr, arr1))
print ( '\nApplying remainder() function:' )
print (np.remainder(arr, arr1))
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Output:
First array: [ 5 15 20] Second array: [2 5 9] Applying mod() function: [1 0 2] Applying remainder() function: [1 0 2]