**ValueError: operands might not it is in broadcast in addition to shapes (2,2) (2,3) **This error occurs as soon as you attempt to carry out matrix multiplication using a multiplication sign (*****) in Python rather of the **numpy.dot()** function.

You are watching: Valueerror: operands could not be broadcast together with shapes

The following examples shows just how to settle this error in every scenario.

**How come Reproduce the Error**

Suppose we have actually a 2×2 matrix C, which has 2 rows and 2 columns:

Suppose we also have a 2×3 matrix D, which has actually 2 rows and 3 columns:

Here is exactly how to multiply matrix C by procession D:

This results in the adhering to matrix:

Suppose we attempt to execute this procession multiplication in Python utilizing a multiplication sign (*) as follows:

**import numpy together np#define matricesC = np.array(<7, 5, 6, 3>).reshape(2, 2)D = np.array(<2, 1, 4, 5, 1, 2>).reshape(2, 3)#print matricesprint(C)<<7 5> <6 3>>print(D)<<2 1 4> <5 1 2>>#attempt to multiply 2 matrices togetherC*DValueError: operands can not be broadcast along with shapes (2,2) (2,3) **We get a **ValueError**. We can refer come the NumPy documentation to understand why we obtained this error:

When operating on two arrays, NumPy compares their shapes element-wise. The starts v the trailing (i.e. Rightmost) dimensions and works its method left. 2 dimensions space compatible when

they room equal, orone of castle is 1If these conditions are not met, a **ValueError: operands could not be broadcast together** exemption is thrown, indicating that the arrays have actually incompatible shapes.

Since our two matrices perform not have actually the very same value for their trailing size (matrix C has actually a trailing dimension of 2 and matrix D has a trailing measurement of 3), we receive an error.

**How to solve the Error**

The easiest means to fix this error is to just using the **numpy.dot()** duty to do the matrix multiplication:

**import numpy as np#define matricesC = np.array(<7, 5, 6, 3>).reshape(2, 2)D = np.array(<2, 1, 4, 5, 1, 2>).reshape(2, 3)#perform matrix multiplicationC.dot(D)array(<<39, 12, 38>, <27, 9, 30>>)**Notice the we prevent a **ValueError** and also we’re able to efficiently multiply the two matrices.

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Also keep in mind that the results enhance the outcomes that us calculated by hand earlier.

**Additional Resources**

The following tutorials describe how to solve other typical errors in Python:

How to Fix: columns overlap yet no suffix specifiedHow come Fix: ‘numpy.ndarray’ object has actually no attribute ‘append’How come Fix: if utilizing all scalar values, you need to pass one indexHow come Fix: ValueError: cannot transform float NaN come integer