Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **Numpy ValueError: operands could not be broadcast together with shape ...**

**in python**. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How Numpy `ValueError: operands could not be broadcast together with shape ...`

Error Occurs?

Today I get the following error **Numpy ValueError: operands could not be broadcast together with shape ...**

**in python**.

## How To Solve Numpy `ValueError: operands could not be broadcast together with shape ...`

Error ?

**How To Solve Numpy**`ValueError: operands could not be broadcast together with shape ...`

Error ?To Solve Numpy

`ValueError: operands could not be broadcast together with shape ...`

Error When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when:**Numpy**`ValueError: operands could not be broadcast together with shape ...`

To Solve Numpy

`ValueError: operands could not be broadcast together with shape ...`

Error When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when:

## Solution 1

It’s possible that the error didn’t occur in the dot product, but after. For example try this

a = np.random.randn(12,1) b = np.random.randn(1,5) c = np.random.randn(5,12) d = np.dot(a,b) * c

`np.dot(a,b)`

will be fine; however `np.dot(a, b) * c`

is clearly wrong (`12x1 X 1x5 = 12x5`

which cannot element-wise multiply `5x12`

) but numpy will give you

ValueError: operands could not be broadcast together with shapes (12,1) (1,5)

The error is misleading; however there is an issue on that line.

## Solution 2

Per numpy docs:

When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when:

- they are equal, or
- one of them is 1

In other words, if you are trying to multiply two matrices (in the linear algebra sense) then you want `X.dot(y)`

but if you are trying to broadcast scalars from matrix `y`

onto `X`

then you need to perform `X * y.T`

.

**Example:**

>>> import numpy as np >>> >>> X = np.arange(8).reshape(4, 2) >>> y = np.arange(2).reshape(1, 2) # create a 1x2 matrix >>> X * y array([[0,1], [0,3], [0,5], [0,7]])

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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