NumPy (Numerical Python) is a powerful library for numerical computations inPython. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of theNumPy library and is optimized for numerical and scientific computation in Python.

In this article, we will exploreNumPy Array in Python.

Create NumPy Arrays

To start using NumPy, import it as follows:

import numpy as np

NumPy array’s objects allow us to work with arrays in Python. The array object is called ndarray. NumPy arrays are created using thearray()function

Example:

importnumpyasnp# Creating a 1D arrayx=np.array([1,2,3])# Creating a 2D arrayy=np.array([[1,2],[3,4]])# Creating a 3D arrayz=np.array([[[1,2],[3,4]],[[5,6],[7,8]]])print(x)print(y)print(z)

[1 2 3][[1 2] [3 4]][[[1 2]  [3 4]] [[5 6]  [7 8]]]

Key Attributes of NumPy Arrays

NumPy arrays have attributes that provide information about the array:

• shape: Returns the dimensions of the array.
• dtype: Returns the data type of the elements.
• ndim: Returns the number of dimensions.

Example:

importnumpyasnparr=np.array([[1,2,3],[4,5,6]])print(arr.shape)print(arr.dtype)print(arr.ndim)

(2, 3)int642

Operations on NumPy Arrays

NumPy supports element-wise and matrix operations, including addition, subtraction, multiplication, and division:

Example:

importnumpyasnp# Element-wise additionx=np.array([1,2,3])y=np.array([4,5,6])print(x+y)# Output: [5 7 9]# Matrix multiplicationa=np.array([[1,2],[3,4]])b=np.array([[5,6],[7,8]])print(np.dot(a,b))

[5 7 9][[19 22] [43 50]]

Dimensions in NumPy Arrays

NumPy arrays can have multiple dimensions, allowing users to store data in multilayered structures.

NumPy Arrays vs Python Lists

• Fixed Size: Arrays have a fixed size, while lists can dynamically grow.
• Homogeneous Data: Arrays require uniform data types; lists can store mixed types.
• Performance: Arrays are faster due to their optimized implementation.
• Memory Efficiency: Arrays use contiguous memory blocks, unlike lists.

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