jackatopolis / Matplotlib-challenge

Statistical analysis of drug efficacy on mouse tumors using Pandas and Matplotlib

Statistical Analysis of Drugs Efficacy on Tumors

By: Jack Cohen

Pymaceuticals Inc.

Analysis

Observation from the data:

1. Average tumor volume is positively correlated with weight of the mouse for the mice under the Capomulin regimen. As the weight of the mouse increases, we can expect the tumor size to increase as well.
2. Infubinol is the only drug regimen that contains a potential outlier. The data from the other drug regimens all falls within 1.5 x IQR.
3. For Capomulin treatment of mouse l509, the tumor volume consistently grew until day 20 of treatment, then sharply declined. This suggests that Capomulin becomes effective 20 days after the first treatment.

Background

While your data companions rushed off to jobs in finance and government, you remained adamant that science was the way for you. Staying true to your mission, you've joined Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.

As a senior data analyst at the company, you've been given access to the complete data from their most recent animal study. In this study, 249 mice identified with SCC tumor growth were treated through a variety of drug regimens. Over the course of 45 days, tumor development was observed and measured. The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. You have been tasked by the executive team to generate all of the tables and figures needed for the technical report of the study. The executive team also has asked for a top-level summary of the study results.

Outputs

The final report includes each of the following:

Setup

• Before beginning the analysis, check the data for any mouse ID with duplicate time points and remove any data associated with that mouse ID.

• Use the cleaned data for the remaining steps.

Summary Statistics

• Generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen.

Bar and Pie Charts

• Generate a bar plot using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the number of total mice for each treatment regimen throughout the course of the study.

• Generate a pie plot using both Pandas's DataFrame.plot() and Matplotlib's pyplot that shows the distribution of female or male mice in the study.

Quartiles, Outliers, and Boxplots

• Calculate the final tumor volume of each mouse across four of the most promising treatment regimens: Capomulin, Ramicane, Infubinol, and Ceftamin. Calculate the quartiles and IQR and quantitatively determine if there are any potential outliers across all four treatment regimens.

• Using Matplotlib, generate a box and whisker plot of the final tumor volume for all four treatment regimens and highlight any potential outliers in the plot by changing their color and style.

Line and Scatter Plots

• Select a mouse that was treated with Capomulin and generate a line plot of tumor volume vs. time point for that mouse.

• Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin treatment regimen.

Correlation and Regression

• Calculate the correlation coefficient and linear regression model between mouse weight and average tumor volume for the Capomulin treatment. Plot the linear regression model on top of the previous scatter plot.