ML-LAB-VI-SEM

Exercise 01

Study of Python Basic Libraries such as NumPy and SciPy

Aim

To understand and experiment with basic functionalities of Python libraries NumPy and SciPy.

Procedure/Program

NumPy Example

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
print("NumPy Array:\n", arr)

# basic operations
print("Shape of array:", arr.shape)
print("Sum of all elements:", np.sum(arr))
print("Mean of elements:", np.mean(arr))
print("Transpose of array:\n", arr.T)

SciPy Example

from scipy import stats
import numpy as np

data = [12, 15, 14, 10, 18, 14, 17, 16, 15, 15]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data)

print("Mean:", mean)
print("Median:", median)
print("Mode:", mode.mode)

Output/Explanation

• Output:

  The program demonstrates basic NumPy and SciPy functionalities, with an expected output similar to:

  NumPy Array:
   [[1 2 3]
   [4 5 6]]
  Shape of array: (2, 3)
  Sum of all elements: 21
  Mean of elements: 3.5
  Transpose of array:
   [[1 4]
   [2 5]
   [3 6]]
    
  Mean: 14.6
  Median: 15.0
  Mode: 15
  

• Explanation:

  • NumPy is used for array creation, mathematical operations, and reshaping.
  • SciPy’s stats module provides statistical functions such as mean, median, and mode.
  • The program showcases how these libraries simplify numerical computations.