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Numpy Array advantage over a Nested List
In this article, we will learn about the advantages of a Numpy array with a Nested List in Python. The Numpy array definitely has advantages over a Nested. Let’s see the reasons −
- The array in Numpy executes faster than a Nested List.
- A Nested List consumes more memory than a Nested List.
Numpy Array
NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.
Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object
Create a Numpy Array
Example
The basic Numpy Array is created using an array() function in NumPy:
import numpy as np # Create a Numpy Array arr = np.array([5, 10, 15, 20, 25]) print("Array = ",arr)
Output
Array = [ 5 10 15 20 25]
Create a Matrix with Numpy
Output
In this example, we will create a matrix using the numpy library −
import numpy as np # Create a Numpy Matrix 2x3 a = np.array([[5, 10, 15], [20, 25, 30]]) # Display the array with more than one dimension print("Array = ",a)
Output
Array = [[ 5 10 15] [20 25 30]]
Nested Lists
A nested list as the name suggests is a list of lists. They can be used to create a matrix as well.
Create a Matrix with Nested Lists
With Nested Lists, you can easily create a matrix in Python. In a Nested List.
- Every element of the nested list i.e., a matrix has rows and columns.
- Number of elements in the nested lists = The number of rows of the matrix.
- Length of the lists inside the nested list = number of columns.
Example
Let us see an example −
# create a matrix 3x3 mat = [[5, 10, 15], [50, 100, 150], [100, 150, 200]] # number of rows rows = len(mat) print("Number of rows = ", rows) # number of columns = length of sublist cols = len(mat[0]) print("Number of columns = ", cols) # Display the matrix (nested list) print("\nMatrix = ") for i in range(0, rows): print(mat[i])
Output
Number of rows = 3 Number of columns = 3 Matrix = [5, 10, 15] [50, 100, 150] [100, 150, 200]
The above example displays nested list −
- Related Articles
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- Pack the elements of a binary-valued Numpy array into bits in a uint8 array over negative axis
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- Expand the shape of an array over tuple of axis in Numpy
- Create a Series from a List, Numpy Array, and Dictionary in Pandas
