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Python für Naturwissenschaftler


  • Advanced Python
    • Sets
    • The collections module
    • List comprehensions
    • Generators and iterators
    • Decorators
    • Exceptions
    • Context with the with-statement
  • NumPy
    • Python lists and matrices
    • NumPy arrays
    • Creating NumPy arrays
    • Addressing NumPy arrays
    • Universal functions
    • Linear algebra
    • Simple applications
  • Version control with Git
    • Basic steps
    • Branching and merging
    • Working with remote repositories
  • Profiling
    • Pitfalls in profiling
    • The timeit module
    • The cProfile module
    • Line-oriented profiling
  • Aspects of parallel computing
    • Threads, processes, and the GIL
    • Parallel computing with Python
    • Numba