matplotlib-challenge

Module 5 Challenge (Matplotlib / Pandas / Jupyter Notebook) - Wassim Deen

Summary of Challenge

• Create a Jupyter script solution to do the following:

  1. Prepare the Data

     • Merge datasets from separate csv files into one 'Complete' DataFrame
     • Display No. mice shown from the merged DataFrame
     • Find all duplicate mice based on the Mouse ID and Timepoint
     • Clean the DataFrame by dropping The mice with duplicates
     • Display No. mice shown from the cleaned DataFrame

  2. Generate Summary Statistics (using Groupby())

     • Calculate the mean of the tumor volume for each Drug Regimen
     • Calculate the median of the tumor volume for each Drug Regimen
     • Calculate the variance of the tumor volume for each Drug Regimen
     • Calculate the standard deviation of the tumor volume for each Drug Regimen
     • Calculate the standard error of mean (SEM) of the tumor volume for each Drug Regimen
     • Store all summary statistics in a new DataFrame

  3. Create Bar Charts and Pie Charts

     • Generate a bar plot showing total no. of timepoints for all mice tested per drug regimen (using Pandas)
     • Generate a bar plot showing total no. of timepoints for all mice tested per drug regimen (using Pyplot)
     • Generate a pie plot showing the distribution of female vs. male mice using Pandas (using Pandas)
     • Generate a pie plot showing the distribution of female vs. male mice using Pandas (using Pyplot)

  4. Calculate Quartiles, Identify Outliers, and Create Box Plot

     • Using Groupby(), create a new DataFrame that has the last (max) Timepoint for each mouse ID; index in DataFrame is also reset
     • Retrieve the maximum Timepoint value for each Mouse ID
     • Create a list to store four treatment groups: Capomulin, Ramicane, Infubinol, and Ceftamin
     • Use 'for' loop to display the quartiles (Q1, Q2, Q3), interquartile range (IQR) and the outliers for each treatment group
     • Generate a box plot showing the distribution of the final tumor volume for all the mice in each treatment group. (using Pyplot)

  5. Create Line Plot and Scatter Plot

     • Generate a line plot that shows tumor volume vs. time point for a selected mouse ID treated with Capomulin (using Pyplot)
     • Generate a scatter plot that shows average tumor volume vs. mouse weight for the Capomulin regimen (using Pyplot)

  6. Calculate Correlation and Regression

     • Calculate Pearson Correlation Coefficient and Linear Regression model for mouse weight and average tumor volume for the Capomulin regimen (using SciPy)
     • Plot the Regression line on the same scatter plot generated from Part #5 (using Pyplot)

Contents

1. 'Resources' Folder

   • 'mouse_metadata.csv' (reference dataset)
   • 'study_results.csv' (reference dataset)

2. 'main.ipynb' Jupyter Solution Script

3. README File