6. Working With Dataframes Using pandas

Data scientists work with data stored in tables. This chapter introduces dataframes, one of the most widely used ways to represent data tables. We also introduce pandas, the standard Python package for working with dataframes. Below is an example of a dataframe that holds information about popular dog breeds:

	grooming	food_cost	kids	size
breed
Labrador Retriever	weekly	466.0	high	medium
German Shepherd	weekly	466.0	medium	large
Beagle	daily	324.0	high	small
Golden Retriever	weekly	466.0	high	medium
Yorkshire Terrier	daily	324.0	low	small
Bulldog	weekly	466.0	medium	medium
Boxer	weekly	466.0	high	medium

In a dataframe, each row represents a single record—in this case, a single dog breed. Each column represents a feature about the record—for example, the grooming column represents how often each dog breed needs to be groomed.

Dataframes have labels for both columns and rows. For instance, this dataframe has a column labeled grooming and a row labeled German Shepherd. The columns and rows of a dataframe are ordered—we can refer to the Labrador Retriever row as the first row of the dataframe.

Within a column, data have the same type. For instance, the food_cost column contains numbers, and the size column contains categories. But data types can be different within a row.

Because of these properties, dataframes enable all sorts of useful operations.

Note

Data scientists often find themselves working with people from different backgrounds who use different terms. For instance, computer scientists say that the columns of a dataframe represent features of the data, while statisticians call them variables instead.

Other times, people use the same term to refer to slightly different ideas. Data types in a programming sense refers to how a computer stores data internally. For instance, the size column has a string data type in Python. But from a statistical point of view, the type of the size column is ordered categorical data (ordinal data). We talk more about this specific distinction in the Exploratory Data Analysis chapter.

In this chapter, we introduce common dataframe operations using pandas. Data scientists use the pandas library when working with dataframes in Python. First, we explain the main objects that pandas provides: the DataFrame and Series classes. Then, we show how to use pandas to perform common data manipulation tasks, like slicing, filtering, sorting, grouping, and joining.