In this chapter, we covered exploratory data analysis by using the NumPy, SciPy, matplotlib, and Seaborn packages. At the start, we learned how to load and save files and explore our dataset. Then, we explained and calculated important statistical central moments, such as the mean, variance, skewness, and kurtosis. Four important visualizations were used for the graphical representation of univariate and variate analysis, respectively; these were the histogram, box plot, scatter plot, and heatmap. The importance of data trimming was also emphasized using examples.
In the next chapter, we will go one step further and start predicting housing prices using linear regression.
