A 'MemoryError' in Python typically indicates that your program is trying to use more memory than is available. This can happen with large data structures. Consider optimizing your code or using generators to reduce memory usage.

Encountering a 'MemoryError' in Python can be alarming, as it indicates that your program is attempting to allocate more memory than is available. This often occurs when dealing with large data structures, such as lists, dictionaries, or arrays, especially when the data set is significantly large or when many large objects are created simultaneously. To troubleshoot and resolve MemoryErrors, start by analyzing your code to identify any large variables or data structures that may be consuming excessive memory. Consider whether you can optimize these structures by using more efficient data types or algorithms. For example, using a generator instead of a list can significantly reduce memory usage since generators yield items one at a time instead of storing the entire dataset in memory. If you are working with data processing tasks, libraries such as NumPy and pandas offer optimized structures that are more memory-efficient for numerical computations and data manipulation. Finally, monitoring your program's memory usage can provide insights into where memory is being consumed, allowing you to make informed decisions about optimization. By taking these steps, you can mitigate MemoryErrors and enhance the performance of your Python applications.