APY-04 Libraries of Python

Introduction

• Libraries are pre-written code that can be imported and used to perform specific tasks.
• Libraries provide reusable functions, classes, and modules.
• Libraries simplify complex processes and make development faster.

NumPy

• Powerful library for numerical computation.
• Features:
  • Provides support for large, multi-dimensional arrays and matrices.
  • Mathematical functions to operate on these arrays.
  • Applications: Data manipulation, scientific computing, matrix operations.

import numpy as np
 
arr = np.array([1, 2, 3])
print(arr)

• Resources: Tutorial | Documentation

Pandas

• Library used for data manipulation and analysis.
• Features:
  • DataFrames for handling structured data.
  • Tools for reading/writing CSV, Excel, SQL, etc.
  • Handling missing data, data aggregation, and transformation.

import pandas as pd
 
data = {'Name': ['John', 'Anna', 'Peter'], 'Age': [28, 24, 35]}
df = pd.DataFrame(data)
 
print(df)

• Resources: Tutorial | Documentation

Matplotlib

• A plotting library for creating static, animated, and interactive visualizations.
• Features:
  • Line plots, scatter plots, histograms, etc.
  • Customization of plots (labels, legends, etc.)

import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
 
plt.plot(x, y)
 
plt.show()

• Resources: Tutorial | Documentation

PyGame

• A library for creating games and multimedia applications.
• Features:
  • Tools for handling images, sounds, and game mechanics.
  • Supports 2D game development.

import pygame
 
pygame.init()
screen = pygame.display.set_mode((400, 300))
pygame.display.set_caption('Pygame Example')
pygame.quit()

• Resources: Tutorial | Documentation

Statsmodels

• Used for statistical modeling and testing.
• Features:
  • Linear regression, time-series analysis, hypothesis testing.
  • Models for econometrics and statistical computations.

import statsmodels.api as sm
 
data = sm.datasets.get_rdataset('mtcars').data
model = sm.OLS(data['mpg'], sm.add_constant(data[['hp', 'wt']])).fit()
print(model.summary())

• Resources: Tutorial | Documentation

Scikit-learn

• A library for machine learning.
• Features:
  • Supports supervised and unsupervised learning algorithms.
  • Tools for model evaluation and selection.
  • Easy to use API for training and testing models.

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
 
iris = load_iris()
 
clf = RandomForestClassifier()
clf.fit(iris.data, iris.target)

• Resources: Tutorial | Documentation

SciPy

• A library used for scientific and technical computing.
• Features:
  • Optimization, integration, interpolation, eigenvalue problems, etc.
  • Built on top of NumPy for numerical computations.

from scipy import integrate
 
result = integrate.quad(lambda x: x**2, 0, 1)
print(result)

• Resources: Tutorial | Documentation

TensorFlow

• A popular open-source library for machine learning and AI.
• Features:
  • Used for deep learning and neural networks.
  • Provides tools for both research and production.

import tensorflow as tf
 
model = tf.keras.Sequential([tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)),
 
tf.keras.layers.Dense(10, activation='softmax')])

• Resources: Tutorial | Documentation

Questions

1. Explain the features and applications of the NumPy library in Python. Write a program to create a 2D array. (5)
2. What is Pandas? Write a Python program to create and manipulate a DataFrame. (5)
3. Explain how the Matplotlib library is used for data visualization in Python. Plot a line chart as an example. (4)
4. Discuss the role of TensorFlow in Python for deep learning applications. Write a basic program using TensorFlow. (5)
5. Write a Python program to calculate basic statistical metrics (mean, median, standard deviation) using NumPy. (4)
6. How is the SciPy library used for scientific computing? Write a program to solve a linear algebra problem using SciPy. (4)
7. Compare NumPy and Pandas. Provide examples where each library is more suitable. (3)
8. Write a Python program to visualize data using Matplotlib by creating a bar chart and pie chart. (5)
9. Explain the process of data manipulation using Pandas. Write a program to sort and filter data in a DataFrame. (5)
10. Write a Python program to perform polynomial fitting using NumPy and visualize the results with Matplotlib. (5)