The example below executed with the standard CPython implementation ran in 20.6 seconds. 

```python
total = 0
for i in range(1, 10000):
    for j in range(1, 10000):
        total += i + j
```

Running the script with ```pypy3 script.py``` takes 0.22 seconds. As we can see in this instance Pypy is over 93x faster.

Pypy Example

Pypy is an alternative implementation of Python that uses a Just-In-Time compiler instead of an interpreter. Pypy consumes less memory and and is compatible with many python libraries. On average, Pypy is 4.2x faster than the standard CPython implementation.

California State University, San Bernardino

Python code can usually be optimized in few ways:

* Re-factoring code with optimizations that are specific to python.
* Using a Just-In-Time compiler (JIT) such as Numba or Pypy.
* Using an optimizing compiler such as Cython.

Speeding Up Python Code

Numba is a Just-In-Time (JIT) compiler that translates python code into machine code at runtime via the LLVM compiler. The performance of Numba-optimized algorithms comes very close to the speeds of native C execution. Numba also has support for parallelization as well as GPU acceleration. 

Numba Module

Cython is an optimizing compiler that allows computationally expensive Python code to be translated into C and compiled for use within a python program. It contains a Python-like syntax as well as static typing. The main performance gain comes from Cython being able to bypass the CPython API (Where python code is interpreted from). 

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