Dido Grigorov outlines ten effective strategies for optimizing Python code performance. One key point is the use of sets for membership testing. Unlike lists, which require linear time complexity for lookups, sets enable constant-time (O(1)) checks due to their underlying hash table implementation. For example, checking membership in a list of one million items takes around 0.015 seconds, while the same check in a set takes only about 0.00002 seconds. This makes sets particularly useful for tasks involving large datasets, like filtering duplicates or cross-referencing collections. Another notable strategy is avoiding unnecessary copies of large objects. Each copy incurs significant overhead, particularly in tight loops. Grigorov emphasizes modifying objects in place whenever possible. In tests, modifying a list in place took about 0.0001 seconds, while creating a copy took around 0.0100 seconds. The article also introduces the use of `__slots__` in classes to reduce memory usage. By declaring a fixed set of attributes, you eliminate the need for a dynamic dictionary, leading to faster attribute access and lower memory overhead. For instance, creating one million instances of a class with `__slots__` took roughly 0.1200 seconds compared to 0.1500 seconds without it. Grigorov advises using functions from Python's `math` module instead of basic operators for mathematical calculations. Functions like `math.sqrt()` outperform the exponentiation operator, showing a time difference of about 0.2000 seconds versus 0.2500 seconds in a comparison involving ten million numbers. Pre-allocating memory in lists and arrays also enhances performance by avoiding the overhead of dynamic resizing. By initializing a list with a fixed size, Python manages memory more efficiently, which is crucial for performance-critical applications. Each of these hacks demonstrates how small, deliberate changes can lead to significant performance improvements in Python code.