1 Series
If you have any experience with Pandas, then you know that we typically work with data in two-dimensional tables, known as "data frames," with rows and columns. But each column in a data frame is built from a "series," a one-dimensional data structure, which means that you can think of a data frame as a collection of series.
This perspective is particularly useful once you learn what methods are available on a series, because most of those methods are also available on data frames—only instead of getting a single result, we’ll get one result for each column in the data frame. For example, the mean method, when applied to a series, returns the mean of the values in the series. If you invoke mean on a data frame, then Pandas will invoke the mean method on each column, returning a collection of mean values. Moreover, those values are themselves returned as a series, on which you can invoke further methods.
Figure 1.2. Invoking a series method (such as mean) on a data frame often returns one value for each column
Deep understanding of series can be useful in other ways, too. In particular, with a "boolean index" (also known as a "mask index"), we can retrieve selected rows and columns of a data frame. (If you aren’t familiar with boolean indexes, see the sidebar, "Selecting values with booleans," below.)