Python Workshop Assignments and Netflix Data Exploration

This repository documents hands-on work completed during the ISTE-approved Joy of Programming using Python workshop organized by the Department of Computer Engineering at C.K. Pithawala College of Engineering & Technology, Surat, from April 25 to May 1, 2020.

Motivation

The workshop used short, testable exercises to build practical confidence with Python before moving into object-oriented programming, iterators, NumPy, Pandas, and exploratory data analysis. The repository preserves both the learning sequence and the completed solutions.

Learning goals

• Strengthen core Python syntax, control flow, functions, and collection handling.
• Practice object-oriented design, inheritance, encapsulation, and custom iterators.
• Apply NumPy and Pandas to structured data.
• Explore a real dataset through filtering, aggregation, visualization, and interpretation.
• Use assertions and notebooks to verify solutions as they develop.

Repository contents

File	Focus
assignment1.ipynb	Conditions, loops, functions, list operations, slicing, and type filtering
assignment 2.ipynb	Comprehensions, sets, dictionaries, higher-order functions, and introductory OOP
assignment-3 solved.ipynb	Inheritance, private identifiers, iterators, zip, NumPy, and Pandas
assignment 4.ipynb	Pandas indexing, slicing, grouping, column operations, and capstone planning
Playing with Netflix dataset.ipynb	Exploratory analysis of Netflix titles
Netflix.csv	Dataset used by the exploratory notebook

Accomplishments

• Completed progressive Python exercises covering procedural and object-oriented programming.
• Used assertions to check expected behavior across multiple assignments.
• Analyzed a Netflix dataset containing 6,234 records and 12 columns.
• Identified missing-data patterns, compared movie and television-show counts, examined content categories, and filtered documentary releases by year.
• Used Pandas, NumPy, Matplotlib, and Seaborn to move from raw data to interpretable summaries and charts.

Getting started

Requirements

• Python 3
• Jupyter Notebook or JupyterLab
• Pandas
• NumPy
• Matplotlib
• Seaborn

Run the notebooks

git clone https://github.com/sajeda1311/python-workshop-ckpcet.git
cd python-workshop-ckpcet
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install jupyter pandas numpy matplotlib seaborn
jupyter notebook

Open any notebook and run the cells in order. Keep Netflix.csv in the repository root when running the Netflix exploration.

Current status

This repository represents completed workshop and practice work from 2020. Some notebook cells reflect the instructional style and library versions used at that time, so newer environments may show deprecation warnings.

Next goals

• Replace deprecated plotting calls with current Seaborn APIs.
• Add a reproducible requirements.txt.
• Clean incomplete exercise cells and standardize notebook outputs.
• Extend the Netflix analysis with country, rating, release-year, and duration trends.

Author

Sajeda Patel