The following are useful NumPy functions:
numpy.arange([start,] stop[, step,], dtype=None): This function creates a NumPy array with evenly spaced values within a specified range.numpy.argsort(a, axis=-1, kind='quicksort', order=None): This function returns the indices that will sort the input array.numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0): This function creates a NumPy array from an array-like sequence such as a Python list.numpy.dot(a, b, out=None):This function calculates the dot product of two arrays.numpy.eye(N, M=None, k=0, dtype=<type 'float'>): This function returns the identity matrix.numpy.load(file, mmap_mode=None): This function loads NumPy arrays or pickled objects from.npy,.npz, or pickles. A memory-mapped array is stored in the filesystem and doesn't have to be completely loaded in the memory. This is especially useful for large arrays.numpy.loadtxt(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0): This function loads data from a text file into a NumPy array.numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False): This function calculates the arithmetic mean along the given axis.numpy.median(a, axis=None, out=None, overwrite_input=False): This function calculates the median along the given axis.numpy.ones(shape, dtype=None, order='C'): This function creates a NumPy array of a specified shape and data type, containing ones.numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False): This function performs a least squares polynomial fit.numpy.reshape(a, newshape, order='C'): This function changes the shape of a NumPy array.numpy.save(file, arr): This function saves a NumPy array to a file in the NumPy.npyformat.numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline=' ', header='', footer='', comments='# '): This function saves a NumPy array to a text file.numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): This function returns the standard deviation along the given axis.numpy.where(condition, [x, y]): This function selects array elements from input arrays based on a Boolean condition.numpy.zeros(shape, dtype=float, order='C'): This function creates a NumPy array of a specified shape and data type, containing zeros.
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