**Diagram**

To **fix** the **ValueError: operands could not be broadcast together with shapes **error in Python, use the **“numpy.reshape()”** method to ensure that the shapes of the arrays are compatible.

**Python** raises **ValueError: operands could not be broadcast together with shapes **error when operating on two NumPy arrays with incompatible shapes. **Broadcasting** refers to the ability of NumPy* ***to handle arrays of different shapes during arithmetic operations.**

**Solution 1**

**Reproducing the error**

```
import numpy as np
x = np.array([1, 2, 3, 4])
y = np.array([[1, 2], [3, 4]])
result = x + y
print(result)
```

**Output**

`ValueError: operands could not be broadcast together with shapes (3,) (2,2)`

In this example, array a has shape (3,), and array b has shape (2, 2). The shapes are incompatible for broadcasting, and the addition operation raises a **ValueError**.

**How to fix it?**

Use the **np.reshape()** function to make both arrays compatible.

```
import numpy as np
x = np.array([1, 2, 3, 4])
y = np.array([[1, 2], [3, 4]])
x = x.reshape(2, 2)
result = x + y
print(result)
```

**Output**

```
[[2 4]
[6 8]]
```

We reshaped the x array to have the same shape as the y array and then performed the addition operation.

Since both x and y arrays have the same shape (2, 2), the addition operation can be performed element-wise, resulting in a new (2, 2) array with the summed values.

**Solution 2**

The **ValueError: operands could not be broadcast together with shapes** also occurs when you try to perform matrix multiplication using a multiplication sign (*) in Python instead of the **numpy.dot()** function.

**Reproducing the error**

```
import numpy as np
#define matrices
C = np.array([1, 2, 3, 4]).reshape(2, 2)
D = np.array([21, 19, 46, 52, 18, 23]).reshape(2, 3)
print(C*D)
```

**Output**

`ValueError: operands could not be broadcast together with shapes (2,2) (2,3)`

**How to Fix it?**

You can use the **“np.dot()”** method to fix the error.

```
import numpy as np
C = np.array([1, 2, 3, 4]).reshape(2, 2)
D = np.array([21, 19, 46, 52, 18, 23]).reshape(2, 3)
print(C.dot(D))
```

**Output**

```
[[125 55 92]
[271 129 230]]
```

That’s it.

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.