NumPy is a library built around the notion of numeric arrays—multidimensional, index-based (like a list) collection of data, which (unlike a list) guarantees the type of the stored values to stay consistent and predefined—say, a 2-dimensional array of integers or 1-dimensional array of floats. It is based on the C code and allows us to boost computation by a few orders of magnitude, compared to base Python. The gap in performance is staggering even on relatively small datasets and grows exponentially for large datasets and complex algorithms. NumPy is capable of handling a few million rows of data and is primarily bounded by the operational memory—not the CPU.
Let's illustrate this staggering difference in performance with an example. Imagine that we need to summarize three lists of values, pairwise. In pure Python, the code will be similar...
