Raw data, whether from experiments or logs, often appears as a large collection of numbers or categories. To make sense of it, we need methods to summarize its main characteristics. This chapter focuses on descriptive statistics, the techniques used to describe and summarize features of a dataset.

You will learn how to calculate and interpret:

• Measures of central tendency: the mean, median, and mode, which locate the center of your data.
• Measures of dispersion (or spread): the range, variance, and standard deviation, which describe how varied the data points are.
• Measures of position: percentiles and quartiles, which help understand the distribution of data.

We will also introduce basic data visualization techniques, such as histograms and box plots, as visual aids for understanding these summaries. Throughout the chapter, you'll see how to compute these statistics efficiently using Python's NumPy and Pandas libraries.