Let's delve into Python's NumPy library and focus on the centerpiece of NumPy - arrays. NumPy, an acronym for 'Numerical Python', specializes in efficient computations on arrays. Arrays in NumPy are more efficient than typical Python data structures.
The power of NumPy lies in its fast computations on large data arrays, making it crucial in data analysis. Before we start, let's import it:
np is a commonly used representation for numpy.
NumPy arrays, like a sorted shopping list, allow for swift computations. Arrays offer quick access to elements. Let's create a simple one-dimensional NumPy array:
This code creates a five-element array.
We can create multi-dimensional arrays as much as we would with a multi-day shopping list. Here, each sublist [] forms a row in the final array:
