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Presentation on theme: "Python Crash Course Numpy"— Presentation transcript: 1 Python Crash Course Numpy 2 Extra features required:Scientific Python? Extra features required: fast, multidimensional arrays libraries of reliable, tested scientific functions plotting tools NumPy is at the core of nearly every scientific Python application or module since it provides a fast N-d array datatype that can be manipulated in a vectorized form. 2 3 What is NumPy? NumPy is the fundamental package needed for scientific computing with Python. It contains: a powerful N-dimensional array object basic linear algebra functions basic Fourier transforms sophisticated random number capabilities tools for integrating Fortran code tools for integrating C/C++ code 4 Official documentation The NumPy book Example listNumPy documentation Official documentation The NumPy book Example list 5 Arrays – Numerical Python (Numpy)Lists ok for storing small amounts of one-dimensional data >>> a = [1,3,5,7,9] >>> print(a[2:4]) [5, 7] >>> b = [[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]] >>> print(b[0]) [1, 3, 5, 7, 9] >>> print(b[1][2:4]) [6, 8] >>> a = [1,3,5,7,9] >>> b = [3,5,6,7,9] >>> c = a + b >>> print c [1, 3, 5, 7, 9, 3, 5, 6, 7, 9] But, can’t use directly with arithmetical operators (+, -, *, /, …) Need efficient arrays with arithmetic and better multidimensional tools Numpy Similar to lists, but much more capable, except fixed size >>> import numpy 6 Numpy – N-dimensional Array manpulationsThe fundamental library needed for scientific computing with Python is called NumPy. This Open Source library contains: a powerful N-dimensional array object advanced array slicing methods (to select array elements) convenient array reshaping methods and it even contains 3 libraries with numerical routines: basic linear algebra functions basic Fourier transforms sophisticated random number capabilities NumPy can be extended with C-code for functions where performance is