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### NumPy operators and their corresponding NumPy universal functions (ufuncs)

NumPy operators and their corresponding NumPy universal functions (ufuncs)

This table was compiled from the 3 tables shown on pages 53, 72, 75 of the book Python Data Science Handbook by Jake VanderPlas. I put it here just for my own reference.

 Arithmetic Operator Equivalent ufunc Description + np.add Addition (e.g., 1 + 1 = 2) - np.subtract Subtraction (e.g., 3 - 2 = 1) - np.negative Unary negation (e.g., -2) * np.multiply Multiplication (e.g., 2 * 3 = 6) / np.divide Division (e.g., 3 / 2 = 1.5) // np.floor_divide Floor division (e.g., 3 // 2 = 1) ** np.power Exponentiation (e.g., 2 ** 3 = 8) % np.mod Modulus/remainder (e.g., 9 % 4 = 1) Comparison Operator Equivalent ufunc == np.equal != np.not_equal < np.less <= np.less_equal > np.greater >= np.greater_equal Logical Operator Equivalent ufunc See also & np.bitwise_and np.logical_and | np.bitwise_or np.logical_or ^ np.bitwise_xor np.logical_xor ~ np.bitwise_not np.logical_not

These rules are listed in the book Python Data Science Handbook by Jake VanderPlas (see page 65).

Broadcasting in NumPy is simply a set of rules for applying binary universal functions (like addition, subtraction, multiplication, etc.) on NumPy arrays of different sizes.

Here are the NumPy broadcasting rules.

• Rule 1: If the two arrays differ in their number of dimensions, the shape of the
one with fewer dimensions is