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:
Python3
# Python code to perform arithmetic# operations on NumPy arrayimport numpy as np# 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:
Python3
# Python code to perform reciprocal operation# on NumPy arrayimport numpy as nparr = 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.
Python3
# Python code to perform power operation# on NumPy arrayimport numpy as nparr = 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.
Python3
# Python code to perform mod function# on NumPy arrayimport numpy as nparr = 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]
