How to optimize my code for better performance in Jupyter Notebook?

Step 1: Use Built-in Functions
Python has a lot of built-in functions that are optimized for speed. Whenever possible, use these functions instead of writing your own.

Step 2: Use Vectorized Operations
If you're using libraries like NumPy or Pandas, take advantage of their vectorized operations. These operations are performed on entire arrays, which can be much faster than looping over individual elements.

Step 3: Use List Comprehensions
List comprehensions are faster than for loops in Python. If you're creating a new list based on an existing one, consider using a list comprehension.

Step 4: Use Local Variables
Accessing local variables is faster than accessing global variables in Python. If you're using a variable frequently inside a function, consider making it a local variable.

Step 5: Use the %timeit Magic Command
Jupyter Notebook has a magic command called %timeit that can time how long a particular line of code takes to run. Use this command to identify slow parts of your code.

Step 6: Use Profiling Tools
There are several Python libraries that can help you profile your code, such as cProfile, line_profiler, and memory_profiler. These tools can give you detailed information about how long each function in your code takes to run and how much memory it uses.

Step 7: Use Efficient Data Structures
Different data structures have different strengths and weaknesses. For example, lists are good for storing collections of items, but if you need to frequently check if an item is in the list, a set would be faster.

Step 8: Avoid Unnecessary Computations
If a computation is expensive and its result is used multiple times, consider storing the result in a variable instead of recomputing it each time.

Step 9: Use the Latest Version of Python and Libraries
Newer versions of Python and its libraries often include performance improvements. Make sure you're using the latest versions.

Step 10: Consider Using Cython or Numba
If you've tried everything else and your code is still too slow, you might want to consider using Cython or Numba. These tools can compile your Python code to C or machine code, which can significantly speed up your code. However, they also require more work to set up and use, so they should be a last resort.