Matplotlib - The Power of Plots

The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The tasked by the executive team is 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.

Instructions

Your tasks are to do the following:

• 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.

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

• 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.

  • NOTE: These plots should look identical.

• 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.

  • NOTE: These plots should look identical.

• 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.

  Hint: All four box plots should be within the same figure. Use this Matplotlib documentation page for help with changing the style of the outliers.

• 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.

• 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.

• Look across all previously generated figures and tables and write at least three observations or inferences that can be made from the data. Include these observations at the top of notebook.

Here are some final considerations:

• You must use proper labeling of your plots, to include properties such as: plot titles, axis labels, legend labels, x-axis and y-axis limits, etc.

• See the starter workbook for help on what modules to import and expected format of the notebook.

Observations and Insights

• Overall,all the drug regimen started with a same number of mice and the number of data points on drug regimens Ramicane and Capomulin suggests that more mice reached a timepoint.
• The sex distribution in almost equally split between male and female.
• The Drug Ramicane shows the greatest reduction of tumor volume based on mean and final tumor volume.
• Ketapril on the other hand shows the least tumor reduction and least consistent results.
• Across the four regimens of interest, Infubinol has an outlier with an average tumor downwards.
• Moreover,weight of mouse and average tumor increses in the correlation and linear regreession model.