Summary - Performance

Combine timeit, cProfile and __slots__ to write an optimized handler.

Recap: Performance

In this group you learned Python performance tools. You measured with timeit and profiled with cProfile.

You measured memory with sys.getsizeof. You saved memory with __slots__. You applied vectorization with numpy. Now you will combine these into an optimized handler: a memory-friendly class with __slots__ and fast computation with numpy together.

Exercise Write an optimized handler

Import the module with import numpy as np. Write a Point class with __slots__ = ['data']. In __init__(self, data) set self.data = np.array(data). Add a squared method returning self.data ** 2. Create a Point([1, 2, 3]) instance and print .squared().

Show example solution

import numpy as np

class Point:
    __slots__ = ["data"]
    def __init__(self, data):
        self.data = np.array(data)
    def squared(self):
        return self.data ** 2

p = Point([1, 2, 3])
print(p.squared())

What you learned

In this group you learned Python performance optimization. You measured with timeit and profiled with cProfile. You measured memory with sys.getsizeof.

You saved memory with __slots__ and applied vectorization with numpy. In the summary you combined these into an optimized handler. This knowledge is critical for large-data and high-performance applications.

Test yourself

Which tool for which job?
- timeit to measure memory
- getsizeof for vectorization
- cProfile for time profiling
- numpy for profiling
Show answer

cProfile for time profiling — cProfile profiles function times. The other matches are wrong.
Match all performance concepts

timeit

Measures time
cProfile

Profiles
getsizeof

Measures memory
numpy

Vectorizes

Final blank

import numpy as ____
a = np.array([1, 2, 3])

Show answer

import numpy as np
a = np.array([1, 2, 3])

Next lesson: Project design

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