🐍 Python Learning Libraries (Notebooks Collection)

This repository is a learning journey in Python and the most important libraries used in data analysis, visualization, file handling, and a beginner intro to web scraping.

All content is organized as Jupyter Notebooks (.ipynb), so you can read and run each topic step-by-step.

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📌 Included Notebooks (Exact Files)

✅ 1) Python Basics

• intro_to_python.ipynb
  • Python syntax
  • Variables and data types
  • Conditions & loops
  • Functions
  • Basic practice examples

✅ 2) NumPy

• Numpy.ipynb
  • Arrays and array operations
  • Indexing & slicing
  • Reshaping
  • Math operations

✅ 3) Pandas

• Pandas.ipynb
  • Series & DataFrames
  • Reading and working with data
  • Filtering, sorting, grouping

✅ 4) Matplotlib

• Matplotlib.ipynb
  • Building plots (line, scatter, bar, etc.)
  • Titles, labels, grid, figure size
  • Plot customization basics

✅ 5) Visualization using Matplotlib

• VisualizationUsingMatplot.ipynb
  • Practical plotting examples
  • More visualization practice using matplotlib

✅ 6) Visualization using Pandas

• visualizationUsingPandas.ipynb
  • Pandas built-in plotting
  • Quick visualization directly from DataFrames

✅ 7) Intro on Frontend Web Scraping

• intro_on_front_end_scrapping.ipynb
  • Basic understanding of web pages (frontend)
  • Intro-level scraping concepts and examples

✅ 8) Glob Library

• glob.ipynb
  • Finding files with patterns (e.g., *.csv, *.txt)
  • Automating file collection from folders

📄 Extra Data File

• data.txt (used for practice / examples)

🖼️ Graph Files

• Graph.png
• Graph.svg

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🖼️ Image Processing & Noise Analysis (Advanced Notebooks)

In addition to the core Python learning notebooks, this repository includes a practical image processing mini-project focused on analyzing and improving noisy and distorted images.

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📌 Additional Notebooks

✅ 9) Open Images Using Different Libraries

• Open images using different libraries.ipynb
  • Loading images using:
    • Pillow (PIL)
    • OpenCV
    • Matplotlib
  • Comparing how each library handles image data

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✅ 10) Pillow (PIL)

• Pillow.ipynb
  • Image loading and saving
  • Resizing
  • Cropping
  • Basic manipulation

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✅ 11) SciPy

• Scipy.ipynb
  • Applying filters
  • Image smoothing
  • Signal/image processing basics

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✅ 12) scikit-image

• skimage.ipynb
  • Edge detection
  • Filtering
  • Image transformations

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✅ 13) Solving a Noisy Image

• solve a noise image.ipynb
  • Working with a noisy image
  • Applying multiple techniques:
    • Noise reduction
    • Filtering
    • Enhancement
  • Attempting to recover hidden details

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🖼️ Image Files Used

• input.jpg → Original noisy image (house scene)
• resized_image.jpg → Processed version
• Additional generated outputs from notebooks

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🔍 What Happened in These Files?

This project represents a step-by-step attempt to analyze and improve a heavily noisy image.

🧪 Step 1 — The Problem

The original image contains:

• Heavy noise
• Low visibility
• Distorted details

👉 Goal: Enhance the image and recover useful information

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⚙️ Step 2 — Image Loading

Different libraries were used:

• Pillow
• OpenCV
• Matplotlib
• skimage

📌 Insight: Each library handles images differently (RGB vs BGR, scaling, etc.)

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🧼 Step 3 — Noise Reduction

Applied techniques:

• Gaussian filter
• Median filter
• Smoothing

📌 Result: Noise reduced partially, but not fully removed.

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🔎 Step 4 — Enhancement

Applied:

• Contrast adjustment
• Brightness tuning
• Edge detection

📌 Result: Some shapes became more visible.

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🌊 Step 5 — Observations

Some processed outputs look like:

• Abstract shapes
• Underwater-like patterns

📌 Reason: Noise patterns can sometimes appear as structures after filtering.

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🧠 Key Learnings

✔ Noise can significantly degrade image quality
✔ Different tools provide different perspectives
✔ Image recovery has limits
✔ Combining techniques improves results

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🚀 Technologies Used

• Python 🐍
• NumPy
• Pandas
• Matplotlib
• Pillow (PIL)
• OpenCV
• SciPy
• scikit-image (skimage)

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📌 Final Note

This repository shows a full journey: from Python basics → data analysis → visualization → real-world image processing.

It demonstrates practical skills useful in:

• Data Science
• Computer Vision
• Image Analysis

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