Python for data

Python has several built-in data structures, including lists, dictionaries, and sets, that we use to build customized objects. In addition, there are a number of internal libraries, such as collections and the math object, which allow us to create more advanced structures as well as perform calculations on those structures. Finally, there are the external libraries such as those found in the SciPy packages. These allow us to perform a range of advanced data tasks such as logistic and linear regression, visualization, and mathematical calculations such as operations on matrixes and vectors. External libraries can be very useful for an out-of-the-box solution. However, we must also be aware that there is often a performance penalty compared to building customized objects from the ground up. By learning how to code these objects ourselves, we can target them to specific tasks, making them more efficient. This is not to exclude the role of external libraries and we will look at this in Chapter 12, Design Techniques and Strategies.

To begin, we will take an overview of some of the key language features that make Python such a great choice for data programming.