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 initializeNumPy arrays from nested Python lists and access it elements. A Numpy array on a structural level is made up of a combination of:

• TheDatapointer indicates the memory address of the first byte in the array.
• TheData type ordtype pointer describes the kind of elements that are contained within the array.
• Theshape indicates the shape of the array.
• Thestrides 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 arrayimportnumpyasnp# Initializing the arrayarr1=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))

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 arrayimportnumpyasnparr=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))

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 arrayimportnumpyasnparr=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))

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 arrayimportnumpyasnparr=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))

Output:

First array:[ 5 15 20]Second array:[2 5 9]Applying mod() function:[1 0 2]Applying remainder() function:[1 0 2]

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