Summary
Great! You now have a grasp of the ins and outs of NumPy and pandas. Basically, those libraries are the essential tool for data scientists in Python. By relying on optimized and compiled code, they allow you to load and manipulate large set of data in Python, without sacrificing performance. To allow this, they define fixed-type data structures, meaning each value in the dataset should be of the same type. This is what enables efficient memory consumption and fast computations.
Even though those basics should be enough for you to get started, we recommend that you spend some time on the official user guides and tinker with those a bit to discover all their aspects.
As we said in the introduction, NumPy and pandas are at the heart of most data science applications in Python. In the next chapter, we'll see how they will help us in machine learning tasks, along with the well-known machine learning library scikit-learn.
