Working With Dataframes Using pandas
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
pandas, the standard Python package for working with
dataframes. Below is an example of a dataframe that holds information about
popular dog breeds:
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
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.
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
Series classes. Then, we show how to use
perform common data manipulation tasks, like slicing, filtering, sorting,
grouping, and joining.