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For example, if you want to use a specific function like sqrt(), pow(), abs(), or sin() in Python, you have to tell
Python that you want to use the math library where this function is stored. You do this by adding the line 'import math'
at the beginning of your program. This lets you access and use all the helpful tools that the math library offers, including
the ‘sqrt()’ function.
Python offers a wide range of libraries, such as NumPy, Pandas, Matpoltlib, Scikit-Learn, etc. for various purposes that
make Python a versatile language. With these libraries, you can do various tasks, such as web development, data analysis,
machine learning, scientific computing, etc.
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:
• • 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 29: 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]
• • 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 30: 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]]
• • 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 31: 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)
202 Touchpad Artificial Intelligence (Ver. 3.0)-XI

