Book Image

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
Book Image

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (15 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Survival data analysis on cancer

Since we've spent a significant amount of time discussing death rate, let us conclude this chapter with one final analysis of two cancer datasets. We have obtained the de-identified clinical dataset of breast cancer and brain tumor from; our goal is to see what the overall survival outcome looks like, and whether the two cancers are having statistically different survival outcomes. The datasets are being explored only for research purposes:

# The clinical dataset are in tsv format
# We can use the .read_csv() method and add an argument sep='\t'
# to construct the dataframe
gbm_df = pd.read_csv('
gbm_primary_df = gbm_df[gbm_df['Sample Type']=='Primary Tumor']
.dropna(subset=['Overall Survival (Months)'])

brca_df = pd.read_csv('