You've just joined Pymaceuticals, Inc., a new pharmaceutical company that specializes in anti-cancer medications. Recently, 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 who were identified with SCC tumors received treatment with a range 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, against the other treatment regimens.
The executive team has tasked you with generating all of the tables and figures needed for the technical report of the clinical study. They have also asked you for a top-level summary of the study results.
1.) Ran the provided package dependency and data imports, and then merged the "mouse_metadata" and "study_results" DataFrames into a single DataFrame.
2.) Displayed the number of unique mice IDs in the data, and then checked for any mouse ID with duplicate time points. Displayed the data associated with that mouse ID, and then createed a new DataFrame where this data is removed. Used this cleaned DataFrame for the remaining steps.
- A column for each of the following statistics: mean, median, variance, standard deviation, and SEM of the tumor volume.
1.) Generated two bar charts. Both charts are identical and show the total total number of rows (Mouse ID/Timepoints) for each drug regimen throughout the study.
2.) Generated two pie charts. Both charts are identical and show the distribution of female versus male mice in the study.
1.) Calculated the final tumor volume of each mouse across four of the most promising treatment regimens: Capomulin, Ramicane, Infubinol, and Ceftamin. Then, calculated the quartiles and IQR, and determined if there was any potential outliers across all four treatment regimens.
- Created a grouped DataFrame that shows the last (greatest) time point for each mouse. Merged this grouped DataFrame with the original cleaned DataFrame.
- Created a list that holds the treatment names as well as a second, empty list to hold the tumor volume data.
- Looped through each drug in the treatment list, locating the rows in the merged DataFrame that corresponded to each treatment. Appended the resulting final tumor volumes for each drug to the empty list.
2.) Using Matplotlib, generated a box plot that shows the distribution of the final tumor volume for all the mice in each treatment group.
1.) Selected a single mouse that was treated with Capomulin, and generated a line plot of tumor volume versus time point for that mouse.
2.) Generated a scatter plot of mouse weight versus average observed tumor volume for the entire Capomulin treatment regimen.
1.) Calculated the correlation coefficient and linear regression model between mouse weight and average observed tumor volume for the entire Capomulin treatment regimen.
