In this chapter, we discussed visualizing data with Python by plotting. We used matplotlib, pandas, and Plot.ly. We covered box plots, scatter plots, bubble charts, logarithmic plots, autocorrelation plots, lag plots, three-dimensional plots, legends, and annotations.

Logarithmic plots (or log plots) are plots that use a logarithmic scale. The semi-log plots use linear scaling on one axis and logarithmic scaling on the other axis. Scatter plots plot two variables against each other. A bubble chart is a special type of scatter plot. In a bubble chart, the value of a third variable is relatively represented by the size of the bubble surrounding a data point. Autocorrelation plots graph autocorrelations of time series data for different lags. A box plot visualizes data based on the data's quartiles.

The next chapter, Chapter 7, Signal Processing and Time Series is about a special type of data: time series. Time series are ordered data points that have been timestamped. A lot of the physical world data that we measure is in the form of a time series and can be considered a signal, for instance, sound, light, or electrical signals. You will learn how to filter signals and model time series.