text: Introduction
  Numpy is a python library that provides computation on large array elements and matrices. Numpy provides fast and efficient processing on n-dimensional arrays.
  Array elements in numpy are stored in contiguous memory locations so that the processing of elements is fast and efficient, unlike in a list where the elements are stored in a random memory location.
  Installation of numpy
  Make sure that your system has python and PIP installed already
  $ pip install numpy
  Creation of numpy array
  First of all, we’ve to import the numpy module
  import numpy as np
  Here, the numpy module is imported as np means numpy is shortened to np.
  One dimensional array
  Let’s create a numpy array
  arr = np.array([1,3,5,6,8,9]) print("Array Created: ", arr)
  Output
  Array Created: [1 3 5 6 8 9]
  Now, let’s see the type of arr we just created
  print("Type of arr is: ", type(arr))
  Output
  Type of arr is: <class 'numpy.ndarray'>
  We can create one-dimensional array using arange() function
  arr = np.arange(10) print("Array created: ", arr)
  Output
  Array created: [0 1 2 3 4 5 6 7 8 9]
  Note: arange() function in numpy is similar to range() function in python
  Now, let’s use a list to create an array using numpy
  a = [1,2,3,4,5] arr = np.array(a) print("Array created from list: ", arr)
  Output
  Array created from list: [1 2 3 4 5]
  Two dimensional array
  Previously we’ve seen how a one-dimensional array is created using the numpy module in python. Now, we’re going to see how a two-dimensional array is created using numpy
  a = [1,2,3,4,5] b = [6,7,8,9,10] arr = np.array([a, b]) print( arr)
  Output
  [[ 1 2 3 4 5] [ 6 7 8 9 10]]
  This is how we can create a two-dimensional array in python using a list
  arr = np.array([[1,2,3,4,5],[6,7,8,9,10]]) print( arr)
  Output
  [[ 1 2 3 4 5] [ 6 7 8 9 10]]
  This is another method where we can give elements directly to create two dimensional array
  The shape of an array
  We can determine the shape of an array using shape() function
  arr = np.array([[1,2,3,4,5],[6,7,8,9,10]]) print("Shape of array is: ", arr.shape)
  Output
  Shape of array is: (2, 5)
  The shape function returns the no of rows and columns present in an array. In this case, there are 2 rows and 5 columns
  Dimension of an array
  we can find the dimension of a numpy array
  arr = np.array([[1,2,3,4,5],[6,7,8,9,10]]) print("Dimension of array is: ", arr.ndim)
  Output
  Dimension of array is: 2
  Let’s see another example
  arr = np.array([1,2,3,4,5]) print("Dimension of array is: ", arr.ndim)
  Output
  Dimension of array is: 1
  arr = np.array([[[1,2,3,4,5], [1,2,3,4,5]], [[1,2,3,4,5], [1,2,3,4,5]]]) print(arr) print("Dimension of array is: ", arr.ndim)
  Output
  [[[1 2 3 4 5] [1 2 3 4 5]] [[1 2 3 4 5] [1 2 3 4 5]]] Dimension of array is: 3
  Size of an array
  we can also check the size of a numpy array
  a = [1,3,5,6,8,9,6,8] arr = np.array(a) print("size of arr is: ", arr.size)
  Output
  size of arr is: 8
  The size function returns the number of elements in an array
  arr = np.array([[[1,2,3,4,5], [1,2,3,4,5]], [[1,2,3,4,5], [1,2,3,4,5]]]) print("size of array is: ", arr.size)
  Output
  size of array is: 20
  Accessing elements of an array
  Array elements can be accessed using index same as list and tuple
  a = [1,2,3,4,5] arr = np.array(a) print(arr[0]) print(arr[2]) print(arr[4])
  Output
  1 3 5
  Elements can also be accessed using a loop
  for ele in arr: print(ele)
  Output
  1 2 3 4 5
  Let’s see an example of a 2-D array
  arr = np.array([[1,2,3,4,5], [23, 45, 67 ,98, 100]]) print(arr[0][1]) print(arr[0][0]) print(arr[1][3]) print(arr[1][0])
  Output
  2 1 98 23
  Explanation
  In the above example, we passed two lists inside a single list where the first list index is 0 and another one index is 1 to make a 2-dimensional array with 2 rows and 5 columns.
  Thus, if we have to access elements from the first row two indices must be passed i.e. the first index for the selection of row and the second index for selecting column. The first ‘print statement’ prints the value of first row and second column which is 2. Like as, third ‘print statement’ prints elements from the second row and fourth column which is 98.
  Slicing array
  Syntax
  array_name[start : end : step]
  Let’s see slicing of one dimensional array
  a = [1,2,3,4,5] arr = np.array(a) print(arr[0:4])
  Output
  array([1, 2, 3, 4])
  Index of array starts with 0 and end with one less than length of an array. Here in this example index starts from 0 and end with 4. We know the end index or upper bound is exclusive the above example retrieves the elements indexing 0 to 3.
  print(arr[-5:-1])
  Output
  array([1, 2, 3, 4])
  Indexing is assigned in negative as well. The negative indexing starts from -1 which is assigned to last element of array and ends with a negative length of an array.
  Let’s see slicing of two dimensional array
  Syntax
  array_name[start_row : end_row: step_row, start_column : end_column: step_column]
  arr = np.array([[1,2,3],[4,5,6],[7,8,9]]) print(arr)
  Output
  [[1 2 3] [4 5 6] [7 8 9]]
  Now, let’s take elements from first two rows and last two columns
  print(arr[0:2, 1:])
  Output
  [[2 3] [5 6]]
  Lets take out last element of array
  print(arr[2:, 2:])
  Output
  [[9]]
  Let’s take out last two elements from the second row
  print(arr[1:2, 1:])
  Output
  [[5 6]]
  Reshaping an array
  Using reshape() function, we can define a new array from a previously defined array
  arr1 = np.array([1,2,3,4,5,6]) arr2 = arr1.reshape(3,2) print(arr2)
  Output
  [[1 2] [3 4] [5 6]]
  arr1 = np.array([1,2,3,4,5,6]) print("shape of arr1: ", arr1.shape, "\n") arr2 = arr1.reshape(3,2) print(arr2, "\n") print("shape of arr2: ", arr2.shape)
  Output
  shape of arr1: (6,) [[1 2] [3 4] [5 6]] shape of arr2: (3, 2)
  Here we’ve changed the shape of arr1. All we have to care about during reshaping is that the no of elements must be the same in both the new and previous array
  arr2 = arr1.reshape(2,2) print(arr2)
  Output
  --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-70-a75024147a88> in <module> 1 arr1 = np.array([1,2,3,4,5,6]) ----> 2 arr2 = arr1.reshape(2,2) 3 print(arr2) ValueError: cannot reshape array of size 6 into shape (2,2)
  Here, we got ValueError because we are trying to reshape an array having 6 elements to an array with 4 elements in it.
  arr1 = np.array([[1,2,3,4,5,6], [7,8,9,10,11,12]]) arr2 = arr1.reshape(12,1) print(arr2)
  Output
  [[ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [10] [11] [12]]
  In both arrays, the number of elements is same just the shape is changed.
  arr2 = arr1.reshape(4, 3) print(arr2)
  Output
  [[ 1 2 3] [ 4 5 6] [ 7 8 9] [10 11 12]]
  Appending & Inserting rows and columns in the array
  Using append() function
  Row wise appending
  Syntax
  np.append(previous_array, [array_to_be_add], axis =0)
  a = np.array([20,21,22]) np.append(arr2,[a],axis=0)
  Output
  array([[ 1, 2, 3], [ 4, 5, 6], [ 7, 8, 9], [10, 11, 12], [20, 21, 22]])
  Column wise appending
  Syntax
  np.append(previous_array, array_to_be_add, axis =1)
  a = np.array([20,21,22,23]) b= a.reshape(4,-1) np.append(arr2,b,axis=1)
  Output
  array([[ 1, 2, 3, 20], [ 4, 5, 6, 21], [ 7, 8, 9, 22], [10, 11, 12, 23]])
  Using insert()function
  Row wise inserting
  Syntax
  np.insert(previous_array, inserting_index, array_tobe_inserted, axis=0)
  arr = np.array([[1,2,3],[4,5,6],[7,8,9]]) inserting_arr = np.array([11,12,13]) print("Before insertion: ") print(arr) print("after insertion at index 2: ") print(np.insert(arr, 2, inserting_arr, axis=0))
  Output
  Before insertion: [[1 2 3] [4 5 6] [7 8 9]] after insertion at index 2: [[ 1 2 3] [ 4 5 6] [11 12 13] [ 7 8 9]]
  Column wise inserting
  Syntax
  np.insert(previous_array, inserting_index, array_tobe_inserted, axis=1)
  arr = np.array([[1,2,3],[4,5,6],[7,8,9]]) inserting_arr = np.array([11,12,13]) print("Before insertion: ") print(arr) print("after insertion at index 1: ") print(np.insert(arr, 1, inserting_arr, axis=1))
  Output
  Before insertion: [[1 2 3] [4 5 6] [7 8 9]] after insertion at index 1: [[ 1 11 2 3] [ 4 12 5 6] [ 7 13 8 9]]
  Matrix generation using numpy
  We can generate matrices having elements all one and zero using the ones() and zeros() function
  zero_matrix = np.zeros([3,3], dtype=int) print(zero_matrix)
  Output
  [[0 0 0] [0 0 0] [0 0 0]]
  Let’s take another example
  ones_matrix = np.ones([4,5], dtype=float) print(ones_matrix)
  Output
  [[1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.] [1. 1. 1. 1. 1.]]
  Using random, we can generate matrices too
  Syntax
  np.random.rand(size of array)
  arr = np.random.rand(3,5) print(arr) print(arr.shape)
  Output
  [[0.74206847 0.44733595 0.10237527 0.34372174 0.87838503] [0.48042584 0.46966427 0.318181 0.88341896 0.46838867] [0.02591508 0.58777176 0.07273747 0.80669176 0.69172011]] (3, 5)
  Element wise operation of an array
  One dimensional array
  arr1 = np.array([1,2,3,4]) arr2 = np.array([4,3,2,1]) print("sum: ", arr1+arr2) print("Difference: ", arr1-arr2) print("Multiplication: ", arr1*arr2) print("Division: ", arr1/arr2)
  Output
  sum: [5 5 5 5] Difference: [-3 -1 1 3] Multiplication: [4 6 6 4] Division: [0.25 0.66666667 1.5 4. ]
  Two-dimensional array
  arr1 = np.array([[1,2,3,4], [4,3,2,1]]) arr2 = np.array([[4,3,2,1], [4,3,2,1]]) print("sum:\n ", arr1+arr2) print("Difference:\n ", arr1-arr2) print("Multiplication:\n ", arr1*arr2) print("Division:\n ", arr1/arr2)
  Output
  sum: [[5 5 5 5] [8 6 4 2]] Difference: [[-3 -1 1 3] [ 0 0 0 0]] Multiplication: [[ 4 6 6 4] [16 9 4 1]] Division: [[0.25 0.66666667 1.5 4. ] [1. 1. 1. 1. ]]
  Conclusion
  Numpy is a powerful library that provides fast computation on a large array of elements and matrices. Elements are stored in contiguous memory locations so the processing of array elements is faster than in the list.
  Numpy has a use case in image processing too as OpenCV sees images as an array of 1’s and 0’s. Numpy can be used with the matplotlib library to plot various bar charts, histograms, etc. So numpy is the powerful and most useable library in python.
  Happy Learning 🙂
  Reference