<jupyter_start><jupyter_text>Pymaceuticals Inc.--- Analysis* Overall, it is clear that Capomulin is a viable drug regimen to reduce tumor growth.* Capomulin had the most number of mice complete the study, with the exception of Remicane, all other regimens observed a number of mice deaths across the duration of the study. * There is a strong correlation between mouse weight and tumor volume, indicating that mouse weight may be contributing to the effectiveness of any drug regimen.* There was one potential outlier within the Infubinol regimen. While most mice showed tumor volume increase, there was one mouse that had a reduction in tumor growth in the study.<jupyter_code># Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st

# Study data files
mouse_metadata_path = "data/Mouse_metadata.csv"
study_results_path = "data/Study_results.csv"

# Read the mouse data and the study results
mouse_metadata = pd.read_csv(mouse_metadata_path)
study_results = pd.read_csv(study_results_path)

# Combine the data into a single dataset

# Display the data table for preview<jupyter_output><empty_output><jupyter_text>Summary Statistics<jupyter_code># Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen

# This method is the most straighforward, creating multiple series and putting them all together at the end.

# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen
    
# This method produces everything in a single groupby function<jupyter_output><empty_output><jupyter_text>Bar and Pie Charts<jupyter_code># Generate a bar plot showing number of data points for each treatment regimen using pandas
# Generate a bar plot showing number of data points for each treatment regimen using pyplot
# Generate a pie plot showing the distribution of female versus male mice using pandas
# plt.pie(counts.values,labels=counts.index.values,autopct='%1.1f%%')
# Generate a pie plot showing the distribution of female versus male mice using pyplot<jupyter_output><empty_output><jupyter_text>Quartiles, Outliers and Boxplots<jupyter_code># Calculate the final tumor volume of each mouse across four of the most promising treatment regimens. 
# Calculate the IQR and quantitatively determine if there are any potential outliers. 

# Start by getting the last (greatest) timepoint for each mouse

# Merge this group df with the original dataframe to get the tumor volume at the last timepoint

# Quantitatively determine capomulin outliers

# Quantitatively determine ramicane outliers
# Quantitatively determine infubinol outliers
# Quantitatively determine ceftamin outliers
# Generate a box plot of the final tumor volume of each mouse across four regimens of interest<jupyter_output><empty_output><jupyter_text>Line and Scatter Plots<jupyter_code># Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin
# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen<jupyter_output><empty_output><jupyter_text>Correlation and Regression<jupyter_code># Calculate the correlation coefficient and linear regression model 
# for mouse weight and average tumor volume for the Capomulin regimen<jupyter_output>The correlation between mouse weight and average tumor volume is 0.84.
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