A NumPy n-dimensional array (ndarray) is a collection of items of the same data type. It is defined by two primary characteristics: its shape and the data type of its elements. Because all items share the same data type, each element occupies an equal-sized block of memory, allowing the blocks to be interpreted uniformly and efficiently.

University of Minnesota - Twin Cities

Google

In NumPy, there are two objects that are crucial to understanding how this library works. Those are:

1. N-dimensional array object (ndarray)

2. Universal function object (ufunc)

However, keep in mind that there are other objects that are built upon these that can be incorporated into whatever you're doing.

Fundamental Objects in NumPy

Unlike the arrays supported by NumPy, the columns in a DataFrame can be of all different types. You can have one column holding string values, another column holding a date, another column holding a floating point number and so on.

Pandas Dataframe vs. numpy.array

Consider the two following random arrays "a" and "b" below: what will be the shape of "c"?

Essentially, there are NumPy functions that operate on ndarrays. All (ufuncs) wrap a core function that takes scalar inputs and produces scalar outputs.

Why would this be useful?
- Say for example a user wants to sum individual elements in a list with the same corresponding elements in another list. One could iterate through both lists and add them as it goes through the iteration. Or, if the user uses a ufunc called "add(x,y)"  then it does the same thing - but saves times.

Learn Before

Related