Introduction
NumPy Stands for Numerical Python
Fundamental package for numerical computations in Python
General-purpose and array processing package.
why do we use NumPy
it provides high performance and works with a multidimensional array
In Python, we have lists that serve the purpose of arrays, but they are slow to process.
NumPy provides an array object that is up to 50 times faster than traditional Python lists.
Why is NumPy Faster Than Lists?
in NumPy, all elements of an array are placed next to each other in memory whereas in the list each element is a separate object with its memory address in memory
To install NumPy
pip install NumPy
To import NumPy
import NumPy as np
To create a NumPy array
Syntax: NumPy. Array(object)
import numpy as np
X=numpy.array([2,5,6]
print(type(X))
import numpy as np
# Create a 1D NumPy array from a Python list
arr1d = np.array([1, 2, 3, 4, 5])
print("1D array:")
print(arr1d)
# Create a 2D NumPy array from a Python list of lists
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
print("\n2D array:")
print(arr2d)
# Create a 3D NumPy array from a Python list of lists of lists
arr3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print("\n3D array:")
print(arr3d)
---OUTPUT----
1D array:
[1 2 3 4 5]
2D array:
[[1 2 3]
[4 5 6]]
3D array:
[[[1 2]
[3 4]]
arr1dis a 1D NumPy array created from a Python list[1, 2, 3, 4, 5].arr2dis a 2D NumPy array created from a Python list of lists[[1, 2, 3], [4, 5, 6]].arr3dis a 3D NumPy array created from a Python list of lists of lists[[[1, 2], [3, 4]], [[5, 6], [7, 8]]].