Pandas deals with the following three data structures −
These data structures are built on top of the Numpy array, which means they are fast.
Dimension & Description
The best way to think of these data structures is that the higher dimensional data structure is a container of its lower dimensional data structure. For example, DataFrame is a container of Series, Panel is a container of DataFrame.
Building and handling two or more dimensional arrays is a tedious task, burden is placed on the user to consider the orientation of the data set when writing functions. But using Pandas data structures, the mental effort of the user is reduced.
For example, with tabular data (DataFrame) it is more semantically helpful to think of the index (the rows) and the columns rather than axis 0 and axis 1.
All Pandas data structures are valued mutable (can be changed) and except Series, all are size mutable. The Series is size immutable.
Note − DataFrame is widely used and one of the most important data structures. Panel is used much less.
Series is a one-dimensional array-like structure with homogeneous data. For example, the following series is a collection of integers 10, 23, 56, …
Values of Data Mutable
DataFrame is a two-dimensional array with heterogeneous data.
The table represents the data of a sales team of an organization with their overall performance rating. The data is represented in rows and columns. Each column represents an attribute and each row represents a person.
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