Matplotlib Homework

The Power of Plots

Challenge Taken!

As a senior data anaylist who works for a pharmaceutical company called Pymaceuticals, I've been given access to data from a drug regimen animal study. Pymaceuticals, specializes in anti-cancer pharmaceuticals. The scientists in the company attempt to develope a drug that will treat a type of skin cancer called squamous cell carcinoma (SCC).

There were 249 mice that were tested for 10 types of drug regimens in a 45 day period.

As a data analyist I was tasked to collect the data, generate tables and charts for a technical report of the study. This report, also needed to have a high-level summary of the results. Link to my Jupter Notebook results

Tasks:

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

• I used the cleaned data for the remaining steps.

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

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

• Generated 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.

• Calculated 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, I generated 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.

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

• Generated 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.

• Wrote at least three observations that could be made from the data. Included these observations at the top of notebook. Jupyter Notebook Link