ML-LAB-VI-SEM

Exercise 02

Study of Python Libraries for ML Applications such as Pandas and Matplotlib

Aim

To explore and experiment with Python libraries Pandas and Matplotlib, which are commonly used in Machine Learning applications.

Procedure/Program

Pandas Example:

import pandas as pd

# sample dataset
data = {
    "Name": ["Alice", "Bob", "Charlie", "David"],
    "Age": [25, 30, 35, 40],
    "Salary": [50000, 60000, 70000, 80000]
}

# DataFrame
df = pd.DataFrame(data)

print("DataFrame:\n", df)

# basic operations
print("\nDescriptive Statistics:\n", df.describe())
print("\nSelecting Age Column:\n", df["Age"])
print("\nFiltering Rows where Age > 30:\n", df[df["Age"] > 30])

Matplotlib Example:

import matplotlib.pyplot as plt

# sample data
names = ["Alice", "Bob", "Charlie", "David"]
salaries = [50000, 60000, 70000, 80000]

# bar chart
plt.bar(names, salaries, color=['blue', 'orange', 'green', 'red'])
plt.xlabel("Employees")
plt.ylabel("Salary")
plt.title("Employee Salary Chart")
plt.show()

Output/Explanation

• Output:
  The program demonstrates basic functionalities of Pandas and Matplotlib. Expected outputs:

  Pandas Output:

  DataFrame:
        Name  Age  Salary
  0    Alice   25   50000
  1      Bob   30   60000
  2  Charlie   35   70000
  3    David   40   80000
  
  Descriptive Statistics:
              Age        Salary
  count   4.000000      4.000000
  mean   32.500000  65000.000000
  std     6.454972  12909.944487
  min    25.000000  50000.000000
  25%    28.750000  57500.000000
  50%    32.500000  65000.000000
  75%    36.250000  72500.000000
  max    40.000000  80000.000000
  
  Selecting Age Column:
  0    25
  1    30
  2    35
  3    40
  Name: Age, dtype: int64
  
  Filtering Rows where Age > 30:
         Name  Age  Salary
  2  Charlie   35   70000
  3    David   40   80000
  

  Matplotlib Output:
  

  • A bar chart displaying employee names on the x-axis and their salaries on the y-axis.

• Explanation:

  • Pandas provides functionalities for data manipulation and analysis.
  • Matplotlib is used for data visualization, helping to represent trends and patterns graphically.
  • The Pandas example demonstrates DataFrame creation, data selection, and basic analysis.
  • The Matplotlib example showcases how to create a simple bar chart.