Advanced Programming with Python

This repository contains a collection Python scripts developed. The projects progress from Object-Oriented Programming (OOP) principles to advanced data analysis, visualization, and real-time API integration.

📂 Repository Structure

The exercises are organized into 8 modules, each focusing on specific advanced Python concepts:

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🔹 1: Introduction to Pandas

• Focus: Basic DataFrame manipulation.
• Description: An analysis of a small retail dataset (products_sales.csv).
• Key Tasks:
  • Calculating total value per product ($Price \times Quantity$).
  • Grouping data to find average prices per category.
  • Sorting and identifying top-value products.

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🔹 2: Object-Oriented Student Manager

• Focus: Classes, Methods, and File I/O.
• Description: Implementation of a StudentManager system to handle student records.
• Key Features:
  • Student class with attributes for grades and biographical data.
  • Methods for calculating individual and class-wide averages.
  • CSV Import/Export functionality with error handling.
  • Bonus: Visualization of class grades using matplotlib.

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🔹 3: Sales Manager System

• Focus: Integrating OOP with Pandas.
• Description: A SalesManager class designed to track store transactions.
• Key Features:
  • Dynamic addition of sales via the add_sale() method.
  • Conversion of transaction objects into a Pandas DataFrame.
  • Analytics methods: total_sales_per_category() and top_selling_products().

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🔹 4: Real-World Data Analysis

• Focus: Data Cleaning and Exploratory Data Analysis (EDA).
• Description: Processing a raw e-commerce dataset (sales_data.csv).
• Key Tasks:
  • Data Cleaning: Handling missing values and converting date formats.
  • Feature Engineering: Creating new columns (e.g., Total Sales).
  • Visualization: Bar charts showing sales by category and country.

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🔹 5: Crypto Financial Analysis (API) 🪙

• Focus: REST APIs, Financial Metrics, and Correlation.
• Description: A financial tool that fetches historical data for 5 cryptocurrencies (Bitcoin, Ethereum, etc.) using the CoinGecko API.
• Key Features:
  • Fetching 30-day historical data and resampling time-series.
  • Calculating Daily Returns, Volatility, and Pearson Correlation.
  • Interactive visualizations using Plotly (Time series & Heatmaps).

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🔹 6: Flight Radar Analysis (API) ✈️

• Focus: Real-time Data, Geo-spatial Data, and Anomaly Detection.
• Description: Analysis of live aircraft data using the OpenSky Network API.
• Key Features:
  • Fetching and parsing real-time JSON data.
  • Z-Score Analysis: Detecting anomalies in aircraft velocity.
  • Geo-spatial filtering and Pivot Tables (Country vs. Velocity).
  • Scatter plots mapping aircraft positions (Longitude/Latitude).

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🔹 7: Text Analysis & NLP 📝

• Focus: Modular Programming, Regex, and Natural Language Processing.
• Description: A text mining project analyzing public sentiment and trends using a dataset of tweets (energy_tweets.csv).
• Key Features:
  • Modular Architecture: Code split into 5 specialized modules (Loader, Cleaner, Analyzer, Visualizer, Main).
  • Advanced Cleaning: Utilizing Regex to strip URLs, mentions, and noise.
  • Frequency Analysis: Extracting trending hashtags and calculating word distributions.
  • Visualization: Generating bar charts for top words and hashtags using matplotlib.

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🔹 8: Spotify Audio Analysis 🎧

• Focus: Descriptive Statistics, Correlation Analysis, and Visualization.
• Description: Analyzing a dataset of music tracks and their audio features (energy, danceability, tempo, etc.) using data.csv.
• Key Features:
  • Data Loading: Converting raw CSV data into a Pandas DataFrame.
  • Statistical Analysis: Using .describe() and .corr() to understand feature distributions.
  • Correlation Heatmap: Visualizing feature relationships using seaborn.
  • Feature Insights: Identifying which audio characteristics correlate most with high "Energy" tracks.
  • Visualization: Bar charts displaying the average intensity of each audio feature.

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🛠️ Prerequisites & Installation

To run these scripts, you will need Python 3.x and the following external libraries:

pip install pandas numpy matplotlib requests plotly

🚀 How to Run

Ensure you have Python installed (version 3.x is recommended).

1. Clone the repository.

git clone [https://github.com/yourusername/advanced-python-labs.git](https://github.com/yourusername/advanced-python-labs.git)

2. Navigate to the directory.

cd Advanced-Programming-with-Python

3. Run specific exercises (ensure required CSV files are in the same directory):

python main.py

📝 Key Learning Outcomes

Through these exercises, the following concepts are demonstrated:

• Object-Oriented Design: Building robust classes to manage complex data structures.
• Data Science Stack: Proficiency with pandas for data manipulation and numpy for calculations.
• API Integration: Handling HTTP requests, parsing JSON, and dealing with rate limits.
• Data Visualization: Creating static (matplotlib) and interactive (plotly) charts.
• Statistical Analysis: Implementing correlation matrices and anomaly detection algorithms.