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

NumPy rules of broadcasting

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 padded with ones on its leading (left) side.

• Rule 2: If the shape of the two arrays does not match in any dimension, the array
with shape equal to 1 in that dimension is stretched to match the other shape.

• Rule 3: If in any dimension the sizes disagree and neither is equal to 1, an error is
raised.