Pandas is an open-source Python Library used for high-performance data manipulation and data analysis using its powerful data structures. Python with pandas is in use in a variety of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, organize, manipulate, model, and analyse the data.
Below are the some of the important features of Pandas which is used specifically for Data processing and Data analysis work.
Pandas deals with the following three data structures −
These data structures are built on top of Numpy array, making them fast and efficient.
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.
|Series||1||1D labeled homogeneous array, size-immutable.|
|Data Frames||2||General 2D labeled, size-mutable tabular structure with potentially heterogeneously typed columns.|
DataFrame is widely used and it is the most important data structures.
Series is a one-dimensional array like structure with homogeneous data. For example, the following series is a collection of integers 10, 23, 56, …
DataFrame is a two-dimensional array with heterogeneous data. For example,
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.
The data types of the four columns are as follows −
We will see lots of examples on using pandas library of python in Data science work in the next chapters.