How To Create Numpy Array Python

NumPy Arrays

Introduction to NumPy Arrays

Numpy arrays are a very good substitute for python lists. They are better than python lists as they provide better speed and takes less memory space. For those who are unaware of what numpy arrays are, let's begin with its definition. These are a special kind of data structure. They are basically multi-dimensional matrices or lists of fixed size with similar kind of elements.

1D-Array

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2D-Array

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A typical array function looks something like this:

numpy.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)

Here, all attributes other than objects are optional. So, do not worry even if you do not understand a lot about other parameters.

Object: Specify the object for which you want an array
Dtype: Specify the desired data type of the array
Copy: Specify if you want the array to be copied or not
Order: Specify the order of memory creation
Subok: Specify if you want a sub-class or a base-class type array
Ndmin: Specify the dimensions of an array

Attributes of an Array

An array has the following six main attributes:

Size: The total number of elements in an array
Shape: The shape of an array
Dimension: The dimension or rank of an array
Dtype: Data type of an array
Itemsize: Size of each element of an array in bytes
Nbytes: Total size of an array in bytes

Example of NumPy Arrays

Now, we will take the help of an example to understand the different attributes of an array.

Example #1 – To Illustrate the Attributes of an Array

Code:

import numpy as np #creating an array to understand its attributes A = np.array([[1,2,3],[1,2,3],[1,2,3]]) print("Array A is:\n",A) #type of array print("Type:", type(A)) #Shape of array print("Shape:", A.shape) #no. of dimensions print("Rank:", A.ndim) #size of array print("Size:", A.size) #type of each element in the array print("Element type:", A.dtype)

Output:

Attributes

How to Create an Array in NumPy?

Numpy provides us with several built-in functions to create and work with arrays from scratch. An array can be created using the following functions:

ndarray(shape, type): Creates an array of the given shape with random numbers
array(array_object): Creates an array of the given shape from the list or tuple
zeros(shape): Creates an array of the given shape with all zeros
ones(shape): Creates an array of the given shape with all ones
full(shape,array_object, dtype): Create an array of the given shape with complex numbers
arange(range): Creates an array with the specified range

Example #2 – Creation of a NumPy Array

Code:

import numpy as np #creating array using ndarray A = np.ndarray(shape=(2,2), dtype=float) print("Array with random values:\n", A) # Creating array from list B = np.array([[1, 2, 3], [4, 5, 6]]) print ("Array created with list:\n", B) # Creating array from tuple C = np.array((1 , 2, 3)) print ("Array created with tuple:\n", C)

Output:

Creation

Code:

# Creating array with all ones D = np.ones((3, 3)) print ("Array with all ones:\n", D) # Creating array with all zeros E = np.zeros((3, 3)) print ("Array with all zeroes:\n",E) # Creating an array with complex data type F = np.full((3, 3), 1, dtype = 'complex') print ("Array of complex data type:\n", F) #creating an array with buffer G = np.ndarray((2,), buffer=np.array([1,2,3]),dtype=int) print ("Array with buffer specified:\n", G) #creating an array with range H = np.arange(10) print ("Array with range specified:\n", H)

Output:

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How to Access Array Elements in NumPy?

We can access elements of an array by using their indices. We can take the help of the following examples to understand it better.

Example #3 – Element Accessing in a 2D Array

Code:

import numpy as np #creating an array to understand indexing A = np.array([[1,2,1],[7,5,3],[9,4,8]]) print("Array A is:\n",A) #accessing elements at any given indices B = A[[0, 1, 2], [0, 1, 2]] print ("Elements at indices (0, 0),(1, 1), (2, 2) are : \n",B) #changing the value of elements at a given index A[0,0] = 12 A[1,1] = 4 A[2,2] = 7 print("Array A after change is:\n", A)

Output:

Element Accessing

Example #4 – Array Indices in a 3D Array

Code:

import numpy as np #creating a 3d array to understand indexing in a 3D array I = np.array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) print("3D Array is:\n", I) print("Elements at index (0,0,1):\n", I[0,0,1]) print("Elements at index (1,0,1):\n", I[1,0,1]) #changing the value of elements at a given index I[1,0,2] = 31 print("3D Array after change is:\n", I)

Output:

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Array Operation in NumPy

The example of an array operation in NumPy explained below:

Example

Following is an example to Illustrate Element-Wise Sum and Multiplication in an Array

Code:

import numpy as np A = np.array([[1, 2, 3], [4,5,6],[7,8,9]]) B = np.array([[1, 2, 3], [4,5,6],[7,8,9]]) # adding arrays A and B print ("Element wise sum of array A and B is :\n", A + B) # multiplying arrays A and B print ("Elementwise multiplication of array A and B:\n", A*B)

Output:

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Conclusion

A NumPy array is a multidimensional list of the same type of objects. It is immensely helpful in scientific and mathematical computing. As such, they find applications in data science and machine learning.