Numba explained in 30 minutes the python and numpy compiler

sure! numba is a just-in-time (jit) compiler that translates a subset of python and numpy code into fast machine code. it’s especially useful for numerical computations that involve loops and heavy use of numpy arrays. below is a structured tutorial that can be covered in about 30 minutes.

### introduction to numba

#### what is numba?
- a jit compiler designed to speed up numerical python functions.
- works by compiling python functions into machine code at runtime.
- allows for the use of numpy arrays and supports many numpy operations.

#### key features
- **speed**: numba can significantly reduce execution time for numerical computations.
- **compatibility**: works well with numpy and scipy.
- **ease of use**: requires minimal code changes to achieve speed improvements.

### getting started

#### installation
you can install numba using pip:

#### basic usage

### example 1: basic numba function

let’s start with a straightforward example of using numba to accelerate a simple function that computes the sum of squares.

### explanation of the example
- a large numpy array is created to demonstrate the performance difference.
- we measure the execution time for both functions.

### example 2: using numba with numpy functions

numpy functions can also be optimized with numba.

### explanation
- the `array_operations` function computes the sum, mean, and standard deviation of the array using numba.
- it utilizes numpy functions that are supported by numba for efficient computation.

### example 3: numba with parallel processing

numba can leverage parallel processing to further improve performance. this is particularly useful for large datasets.

### explanation
- the `parallel=true` argument allows numba t ...

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