Matplotlib Homework - The Power of Plots

Observations and Insights

# Dependencies and Setup
# Study data files
# Read the mouse data and the study results
# Combine the data into a single dataset
# Display the data table for preview

# Checking the number of mice.

# Getting the duplicate mice by ID number that shows up for Mouse ID and Timepoint.

# Create a clean DataFrame by dropping the duplicate mouse by its ID
# Combine the data into a single dataset

# Check the number of mice in the clean DataFrame.

Summary Statistics

# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen
# Use groupby and summary statistical methods to calculate the following properties of each drug regimen: 
    # mean, median, variance, standard deviation, and SEM of the tumor volume. 
# Assemble the resulting series into a single summary dataframe.

# Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen
# Using the aggregation method, produce the same summary statistics in a single line

Bar and Pie Charts

# Generate a bar plot showing the total number of unique mice tested on each drug regimen using pandas.
# Use the 'Drug Regimen' group by above to get a count of the unique mice for each drug regimen. 
# Create the bar plot

# Generate a bar plot showing the total number of unique mice tested on each drug regimen using pyplot.

# Generate a pie plot showing the distribution of female versus male mice using pandas
# Groupby sex
# Get the count for each sex
# Reindex it
# Create the plot

# Generate a pie plot showing the distribution of female versus male mice using pyplot
# Create axes which are equal so we have a perfect circle

Quartiles, Outliers and Boxplots

# Put treatments into a list for for loop (and later for plot labels)
# Create empty list to fill with tumor vol data (for plotting)
# Calculate the IQR and quantitatively determine if there are any potential outliers.
# Locate the rows which contain mice on each drug and get the tumor volumes
# add subset
# Determine outliers using upper and lower bounds
# Print the outliers for each drug

# Generate a box plot of the final tumor volume of each mouse across four regimens of interest

Line and Scatter Plots

# Generate a line plot of tumor volume vs. time point for a mouse treated with Capomulin
# Create a dataframe for just Capomulin
# Create a dataframe for just mouse s185
# Create a line plot

# Generate a scatter plot of average tumor volume vs. mouse weight for the Capomulin regimen
# Create a groupby dataframe by 'Mouse ID'
# Create a list for the mean of 'Tumor Volume (mm3)'
# Create a list for the mean of 'Weight (g)'
# Create a scatter plot

Correlation and Regression

# Calculate the correlation coefficient and linear regression model 
    # for mouse weight and average tumor volume for the Capomulin regimen
# Create a scatter plot
# Get the regression data
# Print the correlation coefficient
# Create the line for the regression data and plot them