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Scientific Python Lectures - Home

Getting started with Python for Science

  • Introduction to getting started
  • Python scientific computing ecosystem
  • The Python language
    • First steps
    • Basic types
    • Control Flow
    • Defining functions
    • Reusing code: scripts and modules
    • Input and Output
    • Standard Library
    • Exception handling in Python
    • Object-oriented programming (OOP)
  • NumPy: creating and manipulating numerical data
    • The NumPy array object
    • Numerical operations on arrays
    • More elaborate arrays
    • Advanced operations
    • Some exercises
  • Matplotlib: plotting
  • SciPy: high-level scientific computing
  • Getting help and finding documentation

Advanced topics

  • Introduction to advanced topics
  • Advanced Python Constructs
  • Advanced NumPy
  • Debugging code
  • Optimizing code
  • Scipy sparse arrays
    • Storage Schemes
    • Linear System Solvers
    • Other Interesting Packages
  • Image manipulation and processing using NumPy and SciPy
  • Mathematical optimization: finding minima of functions
  • Interfacing with C

Packages and applications

  • Introduction to packages and applications
  • Statistics in Python
  • sympy : Symbolic Mathematics in Python
  • scikit-image: image processing
  • scikit-learn: machine learning in Python

About

  • About the Scientific Python Lecture notes
  • Repository
  • Open issue

Index

D | E | I | M | P | S

D

  • diff, [1]
  • differentiation
  • dsolve

E

  • equations
    • algebraic
    • differential

I

  • integration

M

  • Matrix

P

  • Python Enhancement Proposals
    • PEP 255
    • PEP 3118
    • PEP 3129
    • PEP 318, [1]
    • PEP 342
    • PEP 343
    • PEP 380
    • PEP 380#id13
    • PEP 8

S

  • solve

By Scientific Python developers

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