hi @Joseph_Lilleberg
I am not mentor either for this course but here is how you can debug your code (I am just explaining your error)
your error indicates an issue with performing an operation (like addition, subtraction, multiplication, etc.) between two arrays or array-like objects that have incompatible shapes for NumPy’s broadcasting rules.
Explanation of the shapes:
(0,): This represents a 1-dimensional array with zero elements. It’s essentially an empty array.
(8): This represents a 1-dimensional array with 8 elements.
Why broadcasting fails:
NumPy’s broadcasting rules dictate how operations are performed on arrays of different shapes.
For two arrays to be broadcastable, their dimensions must either be equal, or one of them must be 1 (when comparing from the trailing dimension forward).
So in your case:
You have an empty array (shape (0,)) and an array with 8 elements (shape (8,)).
When attempting an operation, NumPy tries to align the dimensions. The dimension 0 and 8 are neither equal nor is one of them 1. Therefore, broadcasting cannot occur.
Common causes and solutions:
This error often arises when:
1. An array is unexpectedly empty:
Cause: A previous operation, filtering, or data loading step resulted in an empty array when a non-empty one was expected.
Solution: Debug the code leading up to the operation to ensure the array intended to have elements actually contains them. Check for empty results from functions or conditional statements.
2.Incorrect indexing or slicing:
Cause: You might be trying to access elements of an array using an index or slice that results in an empty selection, and then attempting to use this empty selection in an operation with another array.
Solution: Verify your indexing and slicing operations to ensure they are selecting the intended elements and that the resulting shape is compatible with the other operand.
3.Lastly dimension mismatch in a more complex scenario:
Cause: While the example is simple, in more complex scenarios (e.g., image processing, machine learning), a dimension might be accidentally dropped or added, leading to an empty dimension in one array while the other has a fixed size.
Solution: Carefully examine the shapes of all arrays involved in the operation using .shape and ensure they align with the broadcasting rules. You might need to reshape, add np.newaxis, or remove/add dimensions to make them compatible.
For example
import numpy as np
# Original problematic scenario (assuming x became empty)
x = np.array([]) # shape (0,)
y = np.array([1, 2, 3, 4, 5, 6, 7, 8]) # shape (8,)
try:
result = x + y
except ValueError as e:
print(f"Error: {e}")
# Corrected scenario: Ensure x has a compatible shape or is a scalar
x_corrected = np.array([0]) # Or a scalar like 0
result_corrected = x_corrected + y
print(f"Corrected result: {result_corrected}")
From one of older threads mentions to exercise 4. or 5 where the two lists need to have same length.
Regards
DP