NumPy Tutoria

NumPy Tutorial:

 https://www.i2tutorials.com/numpy-tutorial/

 

Numpy is one of the libraries available for Python programming language. This library or module provides numerical and mathematical functions which are pre-compiled.

Numpy is designed to used for multidimensional arrays and for scientific computing which are memory efficient.

Here we have 2 packages

1. Numpy – This provides basic calculations with multi-dimensional arrays and matrices of numeric data.

2. Scipy – This package provides functionality of Numpy with added algorithms like , regression, minimization, Fourier transforms, statistical operations, random simulation and applied mathematical techniques.

 

python numpy.isin - implement of matlab's ismember(A, B)

https://docs.scipy.org/doc/numpy/reference/generated/numpy.isin.html?highlight=numpy%20isin#numpy.isin

mask = np.isin(A, B)

idx=np.nonzere(mask) # return index of element A which is in element B

 

numpy.isin(element, test_elements, assume_unique=False, invert=False)[source]

Calculates element in test_elements, broadcasting over element only. Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise.

Parameters:

element : array_like Input array. test_elements : array_like The values against which to test each value of element. This argument is flattened if it is an array or array_like. See notes for behavior with non-array-like parameters. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. invert : bool, optional If True, the values in the returned array are inverted, as if calculating element not in test_elements. Default is False. np.isin(a, b, invert=True) is equivalent to (but faster than) np.invert(np.isin(a, b)).

Returns:

isin : ndarray, bool Has the same shape as element. The values element[isin] are in test_elements.