This is the code repository for Data Visualization Recipes in Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Visualization is a critical component in exploratory data analysis, as well as presentations and applications. If you are struggling in your day-to-day data analysis tasks, then this is the right course for you. This fast-pace guide follows a recipe-based approach, each video focusing on a commonly-faced issue.
This course covers advanced and powerful time series capabilities so you can dissect by any possible dimension of time. It introduces the Matplotlib library, which is responsible for all of the plotting in pandas, at the same time focusing on the pandas plot method and the Seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.
- Create beautiful and insightful visualizations through pandas' direct hooks to Matplotlib and Seaborn
- Utilize pandas' unparalleled time series functionality
- Split data into independent groups before applying aggregations and transformations to each group
- Prepare real-world messy datasets for machine learning
- Combine and merge data from different sources through pandas' SQL-like operations
To fully benefit from the coverage included in this course, you will need:
Fundamental knowledge of Python and Pandas.
It is assumed that the viewer is familiar with all the common built-in data containers in Python, such as lists, sets, dictionaries, and tuples.
This course has the following software requirements:
Anaconda 4.3
Jupyter Notebook
Python 3.x
Pandas 0.20.1