In the last chapter, we learned that the libraries that are most commonly used for data science work with Python. Although they are not big data libraries per se, the libraries of the Python Data Science Stack (NumPy, Jupyter, IPython, Pandas, and Matplotlib) are important in big data analysis.
As we will demonstrate in this chapter, no analysis is complete without visualizations, even with big datasets, so knowing how to generate images and graphs from data in Python is relevant for our goal of big data analysis. In the subsequent chapters, we will demonstrate how to process large volumes of data and aggregate it to visualize it using Python tools.
There are several visualization libraries for Python, such as Plotly, Bokeh, and others. But one of the oldest, most flexible, and most used is Matplotlib. But before going through the details of creating a graph with Matplotlib, let's first understand what kinds of graphs are relevant for analysis.