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10. Storing clean data: Finally, the cleaned data is saved in a secure and organised location, such as a database,
spreadsheet, or data warehouse, for analysis, visualization, or use in decision-making systems.
By following these steps, data scientists ensure that the data is neat, accurate, and ready for use. Clean data helps
build better AI systems that give more reliable and useful results.

Introduction to NumPy

NumPy is the short form of Numerical Python. It is a fundamental library in Python that is used for performing numerical
computation. It provides support for arrays, matrices, and a variety of mathematical functions to operate on these
data structures efficiently. Its array-based data structures and operations execution make it very useful for various
applications, such as data analysis, machine learning, scientific computing, etc.
In NumPy, there are several types of arrays, which are as follows:

u One-dimensional Arrays (1D Arrays): These arrays contain elements arranged in a single row or column.
One-dimensional arrays are created using the numpy.array() function with a Python list or tuple as input.

Program 1: To demonstrate the use of 1D array

import numpy as np
arr_1d = np.array([1, 2, 3, 4, 5])

print(arr_1d)
Output:
[1 2 3 4 5]
u Two-dimensional Arrays (2D Arrays): Two-dimensional arrays are arranged in rows and columns, forming a
grid-like structure. Two-dimensional arrays are created using nested lists or by reshaping a one-dimensional array.

Program 2: To demonstrate the use of 2D array
import numpy as np
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print(arr_2d)
Output:

[[1 2 3]
[4 5 6]

[7 8 9]]
u Multi-dimensional Arrays (nD Arrays): These arrays have more than two dimensions, which helps in complex data
representations. Multi-dimensional arrays can be created using nested lists or by reshaping existing arrays.

Program 3: To demonstrate the use of nD array

import numpy as np
# Creating a 3x3x3 ndarray
arr_nd = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
[[10, 11, 12], [13, 14, 15], [16, 17, 18]],

[[19, 20, 21], [22, 23, 24], [25, 26, 27]]])
print(arr_nd)

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