Pymaceuticals Inc. Data Analysis

Overview

This script prefroms analysis for Pymaceuticals Inc. The analysis examines effectiveness of different drug regimens for treating tumors in mice, as well as development of those tumors in mice over time. With the help of data manipulations such as data cleaning and summary statistics, the script visualizes and explores correlations in data by providing various graphs (bar, pie, sctatter, box, and linear).

Observations

• After performing an analysis and graphing on the outputs there are three observations that could be made:

  1. Treatment Efficency: The bar graph "Mouse Timepoints Across Drug Regimens" suggests that Capomulin and Ramicane may have better efficency. This conclusion is based on the fact that mice treated with those drugs have more timeponts, or in other words may live longer.

  2. Weight vs. Tumor Volume Correlation: The scatter plot with correlation line "Correlation Between Mouse Weight and Average Tumor Volume" shows positive correlation between weight of the mice and the avarage tumor volume. It may suggests that the weight is contributing factor to the growth of the tumor in mice.

  3. Capomulin Treatment for Mice l509: Based on a linear graph "Capomulin Treatment of Mice l509" we can assume its effectiveness. We can observe a significant decrease in tumor volume starting after 20 days of treatment. However, it is important to note that this success may be due to genetic or other individual properties of the l509. The mouse l509 could be used for additional genetic examination to understand what factors contributed to that success.

Data

The dataset contains two CSV files:

• Mouse_metadata.csv with information about the mice (mouse ID, drug regimen, sex, age, and weight).

• Study_results.csv with the results of the study ( mouse ID, timepoint, tumor volume, and metastatic sites).

Dependancies

• matplotlib.pyplot
• pandas
• scipy.stats
• numpy
• linregress (from scipy.stats)*

Usage

Before running the script please ensure that all libraries mentioned in "Dependancies" is installed in Python. After installing all the libraries and importing linregress from spicy.stats, run the the script.

Visualizations

The sript creates various cahrts to help user understand the data, as well as relationships between some varibles. These charts include:

1) Bar chart with the number of mice's timepoints for drug regimens.
2) Pie charts showing distribution of mice's genders.
3) Box plots that show the distrubution of the tumor volume for each treatment group.
4) Line chart of tumor volume vs. time point for a single mouse (l509) treated with Capomulin.
5) Scatter plot with correlation line of mouse weight vs. the average observed tumor volume for the entire Capomulin regimen.

Conclusion

This analysis provides valuable insights on effectivness of different drug regimens for treating / decreasing tumor volume in mice. The script visualizes finding in form of different graphs appropriate for specific stages of the analysis process. Visualisations makes the analysis accessable for users with different levels of understanding of original data.