NumPy code is much cleaner than straight Python code and it tries to accomplish the same tasks. There are fewer loops required because operations work directly on arrays and matrices. The many convenience and mathematical functions make life easier as well. The underlying algorithms have stood the test of time and have been designed with high performance in mind.

NumPy's arrays are stored more efficiently than an equivalent data structure in base Python, such as a list of lists. Array IO is significantly faster too. The improvement in performance scales with the number of elements of the array. For large arrays, it really pays off to use NumPy. Files as large as several terabytes can be memory-mapped to arrays, leading to optimal reading and writing of data.

The drawback of NumPy arrays is that they are more specialized than plain lists. Outside the context of numerical computations, NumPy arrays are less useful. The technical details of NumPy arrays will be discussed in later chapters.

Large portions of NumPy are written in C. This makes NumPy faster than pure Python code. A NumPy C API exists as well, and it allows further extension of functionality with the help of the C language. The C API falls outside the scope of the book. Finally, since NumPy is open source, you get all the related advantages. The price is the lowest possible—as free as a beer. You don't have to worry about licenses every time somebody joins your team or you need an upgrade of the software. The source code is available for everyone. This of course is beneficial to code quality.