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In Chapter 6, we looked at the analysis of one-dimensional arrays of data. Along the way, we introduced the for loop and the branching statement. Looping enables us to make some set of calculations repeatedly. Branching enables us to ask questions of our data. In this chapter, we extend our discussion to two-dimensional arrays.
One of the neat features of Python is that it works very well as a “glue” language. That is, it enables us to manage many, if not all, parts of a scientific or engineering workflow, within a single computing environment. In earlier chapters, we saw how Python can be used for reading in data, doing calculations on the data, and making plots of the results. We have also seen that Python can be used to generate “data,” in the form of model or simulation results, which can also be analyzed and graphed. In the past, scientists and engineers used separate programs to work on each of these steps in their workflow, and these separate programs did not communicate with each other except through files.
In the previous chapters, the lists and arrays of data we have looked at have been pretty small. Partly, this is because it is easier to understand small datasets, which helps us when we are learning a new tool. However, what makes using a programming language (as opposed to Excel) a better tool to analyze data is the ease with which a program written in a programming language can be scaled up to handle a large dataset. In Excel, it is not so easy to go from, say, 500 rows of data to 500 million rows of data.