🐍 Semester IV Python Notebooks

Comprehensive Python learning journey covering fundamentals, control flow, numerical computing, and data science. 15 classes with hands-on assignments and real-world datasets.

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📋 Quick Overview

Metric	Count
Classes	15 (Feb 25 - Apr 26, 2026)
Assignments	6 (Classes 3, 4, 5, 9, 10, 15)
Datasets	11 files (CSV, XLSX, TXT, PKL, Parquet)
Core Libraries	NumPy, Pandas, Matplotlib/Seaborn, Requests

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📚 Curriculum Structure

Phase 1: Python Fundamentals (Classes 2-5)

Building core programming skills

Class	Date	Topics	Libraries	Assignment
Class 2	25-01-26	Python setup, variables, data types	-	-
Class 3	01-02-26	Lists, tuples, strings, indexing	str methods	✅ Class 3
Class 4	07-02-26	Dictionaries, sets, type operations	Built-ins	✅ Class 4
Class 5	08-02-26	Conditionals, control flow	if/elif/else	✅ Class 5

Key Concepts: Data structures, string manipulation, basic logic

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Phase 2: Control Flow & Iteration (Classes 6-7)

Mastering loops and efficient coding patterns

Class	Date	Topics	Key Skills
Class 6	01-03-26	Loops (for, while), iterations	Loop mechanics, nested loops
Class 7	14-03-26	List comprehensions, lambda functions	Functional programming, concise syntax

Key Concepts: Loop optimization, functional approach to data manipulation

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Phase 3: Numerical Computing (Classes 8-10)

Introduction to scientific Python

Class	Date	Topics	Libraries	Assignment
Class 8	14-03-26	NumPy arrays, creation, shape	numpy	-
Class 9	23-03-26	Array indexing, slicing, filtering	np.where(), boolean indexing	✅ Class 9
Class 10	04-04-26	Statistical operations, aggregations	np.mean(), np.sort(), descriptive stats	✅ Class 10

Key Concepts: Vectorized operations, numerical arrays, filtering & statistics

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Phase 4: Data Science Fundamentals (Classes 11-12)

From data ingestion to cleaning

Class	Date	Topics	Libraries	Focus
Class 11	05-04-26	Pandas basics, DataFrames, file I/O	pandas	Data ingestion from CSV/Excel
Class 12	18-04-26	Data cleaning, handling missing values	pandas	Removing duplicates, type casting, null handling

Key Concepts: DataFrame operations, data inspection, cleaning pipelines

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Phase 5: Advanced Data Science & Visualization (Classes 13-15)

Building stronger EDA and pandas skills

Class	Date	Topics	Libraries	Focus
Class 13	19-04-26	Data cleaning review, outlier detection, typos, duplicate handling	pandas, numpy, seaborn	Robust data quality checks
Class 14	25-04-26	Advanced filtering, string operations, sorting, groupby aggregation	pandas	Structured dataset exploration
Class 15	26-04-26	Data visualization, correlation analysis, concat, web scraping	pandas, seaborn, requests, bs4	EDA, data combination, and CSV/text integration

Key Concepts: Visual analytics, groupby aggregation, concatenation, scraping supplemental data

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📊 Datasets

Located in /data/:

File	Format	Purpose	Used In
retail_2016_2017.csv	CSV	Retail sales analysis	Classes 11-15
oil.csv	CSV	Oil price/supply data	Class 12
Student Grades.xlsx	Excel	Student performance data	Class 13
Run Times.xlsx	Excel	Exercise performance data	Class 12
Groceries.xlsx	Excel	Transaction data	Class 14
groceries_with_new_columns.pkl	Pickle	Enriched grocery dataset	Classes 14-15
student_data.csv	CSV	Student survey grades	Classes 14-15
happiness_survey_data.csv	CSV	Global happiness scores	Classes 15, Assignment 15
happiness_data_*.txt	TXT	Country happiness data slices	Class 15
final_student_dataset.parquet	Parquet	Processed student data archive	Course reference

Additional dataset formats support exercises in file I/O, concatenation, and text parsing.

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📁 Directory Structure

Sem IV Python/
├── Class ipynb Notebook/        # Main course content
│   ├── class_02 to class_15     # 15 classroom sessions
│   └── Organized by date
├── Class Assignment/            # Assessments & exercises
│   ├── assignment_class_03-15   # Problem sets
│   └── Class_XX_Assignment.docx # Assignment details
├── data/                        # Datasets for practice
│   ├── *.csv, *.txt, *.xlsx, *.pkl, *.parquet
└── README.md                    # This file

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🚀 Getting Started

Environment Setup

# Option 1: Using Anaconda (Recommended)
# 1. Install Anaconda from https://www.anaconda.com
# 2. Launch Jupyter Notebook from Anaconda Navigator
# 3. Navigate to this repository folder

# Option 2: Using pip
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install jupyter pandas numpy
jupyter notebook

View Notebooks

1. Open Jupyter Notebook
2. Navigate to the repository folder
3. Select a class notebook to explore

Run Assignments

• Open corresponding assignment notebook
• Complete the exercises
• Run cells to validate your solutions

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🎓 Learning Path

Recommended Order:

1. ✅ Start with Phase 1 (Classes 2-5) - Foundation
2. ✅ Practice Phase 2 (Classes 6-7) - Iteration & Efficiency
3. ✅ Learn Phase 3 (Classes 8-10) - Numerical Computing
4. ✅ Master Phase 4 (Classes 11-12) - Data Science
5. ✅ Explore Phase 5 (Classes 13-15) - Advanced Data Science & Visualization

Each phase builds on previous concepts. Assignments reinforce learning.

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📝 Key Topics Covered

Data Types & Structures

• Lists, Tuples, Dictionaries, Sets
• String manipulation and methods
• Type casting and conversion

Control Structures

• Conditional statements (if/elif/else)
• Loops (for, while)
• List comprehensions

Numerical Computing

• NumPy arrays and vectorization
• Advanced indexing and slicing
• Statistical operations

Data Science

• Pandas DataFrames and Series
• File I/O (CSV, Excel)
• Data cleaning and preprocessing
• Handling missing/duplicate values

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💡 Tips

• Run cells sequentially - Variables defined earlier are needed
• Modify code - Try different parameters to understand behavior
• Use assignments - Apply learning through problem-solving
• Refer to documentation - Check pandas/numpy docs when needed

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📖 Resources

• Python Official Docs
• Pandas Documentation
• NumPy Documentation
• Jupyter Notebook Guide

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👨‍💻 Author

Shreyit

• GitHub: @Shreyit