دانلود کتاب کار pandas – نسخه 11

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. Deep understanding of series can be useful in other ways, too: Series are often used to retrieve selected elements of another series, or even of an entire data frame, using the “boolean index” or “mask index” functionality that’s commonly used in pandas. Given how often we want to retrieve specific parts of a data frame, knowing how best to use this functionality is important. Finally, one of the most important and powerful tools we have as pandas users is the index. We’ll look at indexes in greater depth in later chapters, but knowing how to set and modify an index, as well as retrieve values using unique and non-unique values, comes in handy just about every time you use pandas. This chapter will thus help you to better understand how to use indexes effectively.