What good is data without a good plot to tell the story? So, let's take what you've learned about Python Matplotlib and apply it to a real-world situation and dataset: 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.
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. 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. 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. 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.
- By looking at the bar chart, we can understand number of mice which is used for the test in Capomulin and Ramicane are more than others and Propriva has the least number.
- By looking at the pie chart, we can understand 50.6% of samples are male and 49.4% are female which is an equal number of each sex in the study.
- By looking at the boxplot, we can understand Capomulin treatment and Ramicane treatment have a better effect to reduce the tumor volume
- By looking at the scatter plot, we can understand the strong positive relationship between weight (g) and average tumor volume (mm3) in Capomulin treatment, which means by increasing weight the tumor volume grows as well. The r-squared in this model is 0.70 which means 70% of samples fitted for this model.