Data Visualization
Estaimted time needed: 30 minutes
Objectives
After complting this lab you will be able to:
- Create Data Visualization with Python
- Use various Python libraries for visualization
Introduction
The aim of these labs is to introduce you to data visualization with Python as concrete and as consistent as possible.
Speaking of consistency, because there is no best data visualization library avaiblable for Python - up to creating these labs - we have to introduce different libraries and show their benefits when we are discussing new visualization concepts. Doing so, we hope to make students well-rounded with visualization libraries and concepts so that they are able to judge and decide on the best visualitzation technique and tool for a given problem and audience.
Please make sure that you have completed the prerequisites for this course, namely Python Basics for Data Science and Analyzing Data with Python.
Note: The majority of the plots and visualizations will be generated using data stored in pandas dataframes. Therefore, in this lab, we provide a brief crash course on pandas. However, if you are interested in learning more about the pandas library, detailed description and explanation of how to use it and how to clean, munge, and process data stored in a pandas dataframe are provided in our course Analyzing Data with Python.
Exploring Datasets with pandas
pandas is an essential data analysis toolkit for Python. From their website:
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
The course heavily relies on pandas for data wrangling, analysis, and visualization. We encourage you to spend some time and familizare yourself with the pandas API Reference: http://pandas.pydata.org/pandas-docs/stable/api.html.