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### Why We Need to Forget 'For-Loop' for Data Science Code And Embrace Vectorization

- ndarray, a fast and space-efficient multidimensional array providing vectorized arithmetic operations and sophisticated broadcasting capabilities
- Standard mathematical functions for fast operations on entire arrays of data without having to write loops

You will often come across this assertion in the data science, machine learning, and Python community that Numpy is much faster due to its vectorized implementation and due to the fact that many of its core routines are written in C (based on CPython framework).

- Create a list of a moderately large number of floating point numbers, preferably drawn from a continuous statistical distribution like a Gaussian or Uniform random. I chose 1 million for the demo.
- Create a ndarray object out of that list i.e. vectorize.
- Write short code blocks to iterate over the list and use a mathematical operation on the list say taking logarithm of base 10. Use for-loop, map-function, and list-comprehension. Each time use time.time() function to determine how much time it takes in total to process the 1 million records.

- Do the same operation using Numpy's built-in mathematical method (np.log10) over the ndarray object. Time it.

- Store the execution times in a list and plot a bar chart showing the comparative difference.