Matplotlib Sample

Dependencies

• Import the necessary dependencies, including matplotlib, pandas, scipy, and numpy.

Reading in Data

• Create filepaths to access the data files.
• Read in the CSV data using the pandas function.
• Examine the first few rows of the data using the .head() function to display a preview.

Merging and Unique IDs

• Merge DataFrames on the "Mouse ID" column.
• Use a pandas function to determine the number of unique "Mouse ID" values.
• Sort the DataFrame by "Mouse ID" and "Timepoint" in ascending order.
• Use the duplicated function to identify duplicate "Mouse ID" values and retrieve their indexes.
• Use the loc function to display the rows with duplicate "Mouse ID" values.
• Remove the duplicate "Mouse ID" using the loc function.
• Display the new unique values in the "Mouse ID" column.

Drug Regimen

Regimen Stats

• Group the DataFrame by "Drug Regimen."
• Use the agg function to calculate summary statistics for each regimen.

Regimen Timepoints

• Group the DataFrame by "Drug Regimen" again, using the agg function to count the number of timepoints for each regimen.
• Use pandas and matplotlib to create a bar graph showing the number of timepoints for each drug regimen.

Male vs. Female

• Extract the "Sex" column from the unique DataFrame.
• Use the value_counts function to count the occurrences of each gender.
• Create a pie chart using matplotlib and pandas to visualize the gender distribution.

Calculate Quartiles, Find Outliers, and Create a Box Plot

• Group the mice by "Mouse ID" and select the maximum timepoint for each mouse.
• Merge this data back into the unique DataFrame using an inner join to keep only the final tumor volume data.
• Create a list to store tumor volume data for the four specific treatment names: 'Capomulin', 'Ramicane', 'Infubinol', and 'Ceftamin'.
• Append all final tumor volumes for each treatment to the list.
• Calculate potential outliers using the upper and lower bounds (1.5 times the interquartile range) for each treatment.
• Create a box plot for the final tumor volumes, highlighting any potential outliers in red.

Capomulin

• Narrow down the unique DataFrame to include only subjects treated with Capomulin.
• Select the first mouse treated with Capomulin and create a line plot of "Tumor Volume" vs. "Timepoint."
• For the entire Capomulin subset, group the data by "Mouse ID" and use the agg function to calculate the mean of "Tumor Volume" and the weight of the mice.
• Create a scatter plot of average tumor volume vs. weight for mice treated with Capomulin.

Linear Regression

• Use the scipy library's linregress function to calculate the correlation coefficient (r), slope (m), and y-intercept (b).
• Create a linear regression line using the formula y = mx + b.
• Plot the linear regression line over the scatter plot from the Capomulin section to visualize the relationship between tumor volume and weight.