Scientific Computing with SciPy 🔬

Welcome to Scientific Computing with SciPy – a collection of Jupyter Notebooks that explore various components of the SciPy library. This series is designed to guide learners through key functionalities in scientific and technical computing using Python.

Each section focuses on a different topic, making it easy to learn in small, digestible modules.

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• A hands-on collection of Jupyter Notebooks exploring key features of the SciPy library – from constants and optimization to sparse data and graph algorithms. Perfect for students, researchers, and anyone diving into scientific computing with Python.

📂 Table of Contents

1. Introduction to SciPy
2. SciPy Constants
3. Optimization in SciPy
4. Sparse Data Handling
5. Graph Theory and Spatial Algorithms

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1. Introduction to SciPy

This section provides a foundational overview of the SciPy library. It covers its structure, key submodules, and how it complements NumPy for scientific computing. You'll get hands-on experience with core mathematical operations and special functions.

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2. SciPy Constants

This section introduces the scipy.constants module, which provides access to a wide collection of physical constants and unit conversions used in scientific computations.

🔹 What You’ll Learn:

• Accessing standard constants: Learn how to use predefined constants like:

  • Speed of light (c)
  • Planck’s constant (h)
  • Gravitational constant (G)
  • Avogadro’s number (N_A)
  • Boltzmann constant (k)

• Working with physical units: Use helpful unit definitions such as:

  • minute, hour, day, week
  • inch, foot, mile
  • gram, pound, ton
  • atm, bar, psi

• Unit conversions: Perform unit conversions using tools like:

  • scipy.constants.convert_temperature(value, original_unit, target_unit)
  • Converting between Celsius, Fahrenheit, and Kelvin

• Constants database: Access a full list of constants using:

  • scipy.constants.physical_constants
  • scipy.constants.codata

  Learn how to extract values, units, and uncertainties from the dictionary.

🧪 Example Use Cases:

• Calculate the energy of a photon given its frequency using Planck’s constant.
• Convert inches to meters or psi to pascals.
• Look up the value of the fine-structure constant and its uncertainty.
• Use the Boltzmann constant in thermodynamic equations.

This section makes it easy to incorporate real-world scientific constants into your Python calculations with accuracy and convenience.

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3. Optimization in SciPy

Dive into mathematical optimization using the optimize module. This section includes:

• Function minimization techniques
• Solving equations
• Curve fitting with real-world data

Visual examples and plots are included for better understanding.

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4. Sparse Data Handling

Understand how to work efficiently with large datasets that contain mostly zeroes. This topic covers:

• Creating and manipulating sparse matrices
• Storage formats (CSR, CSC, COO)
• Performing matrix operations and comparisons with dense representations

Learn how sparse matrices can greatly optimize memory and computation in scientific applications.

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5. Graph Theory and Spatial Algorithms

Explore graph-based algorithms and spatial data structures. Topics include:

• Representing graphs using sparse adjacency matrices
• Finding shortest paths and minimum spanning trees
• Working with KD-Trees and computing distances in multidimensional space

These tools are especially useful in areas such as network analysis, logistics, and spatial data science.

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🛠️ Requirements

Make sure you have the following Python packages installed:

pip install numpy scipy matplotlib jupyter


Scipy

git add .

git commit -m "Optimizers file added" 

git push origin main