A 'ValueError' in NumPy often occurs when the input array does not match the expected shape or data type. Check your array dimensions and types to ensure they are compatible with the operation you are performing.

In NumPy, the 'ValueError' exception is frequently encountered when the input array does not conform to the expected shape or data type for a given operation. This can arise in various scenarios, such as attempting to perform mathematical operations on arrays of differing shapes or trying to reshape an array into incompatible dimensions. To resolve a ValueError in NumPy, start by carefully examining the operation that triggered the error. Use the .shape attribute of the NumPy array to inspect its dimensions and ensure they match the requirements of the function or method you are using. If reshaping an array, confirm that the total number of elements remains constant and that the new shape is compatible. Additionally, be mindful of the data types of your arrays; using the .dtype attribute can help you verify the types and ensure compatibility for operations. If necessary, you can convert data types using the astype() method. By methodically checking these factors, you can effectively diagnose and resolve ValueErrors in your NumPy applications, leading to more robust and efficient code.