Summary Statistics and Central Values
In order to find out what our data really looks like, we use a technique known as data profiling. This is defined as the process of examining the data available from an existing information source (for example, a database or a file) and collecting statistics or informative summaries about that data. The goal is to make sure that you understand your data well and are able to identify any challenges that the data may pose early on in the project, which is done by summarizing the dataset and assessing its structure, content, and quality.
Data profiling includes collecting descriptive statistics and data types. Common data profile commands include those you have seen previously, including data.describe(), data.head(), and data.tail(). You can also use data.info(), which tells you how many non-null values there are in each column, along with the data type of the values (non-numeric types are represented as object types).
