💻 PROJECT #2.3: Medical Data Viz with Seaborn

Overview & Setup

In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas. The dataset values were collected during medical examinations.

Go to the CS3 Project 2.3 assignment on Blackbaud and follow the provided GitHub Classroom link.

📁 Clicking the link generates a private repository for your project with the appropriate starter code. Note that projects are stored within the BWL-CS Organization, so you cannot access it from the “Your Repositories” page!
Open the repository in a Codespace whenever you spend time working on the program, in class or at home.

⚠️ Always remember to commit changes after every coding session!
When your project is complete, submit the link to your repository in the CS3 Project 2.3 assignment on Blackbaud.

STARTER CODE

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Import data
df = None

# Add 'overweight' column
df['overweight'] = None

# Normalize data by making 0 always good and 1 always bad. If the value of 'cholesterol' or 'gluc' is 1, make the value 0. If the value is more than 1, make the value 1.


# Function to draw Categorical Plot
def draw_cat_plot():
  # Create DataFrame for cat plot using `pd.melt` using just the values from 'cholesterol', 'gluc', 'smoke', 'alco', 'active', and 'overweight'.
  df_cat = None

  # Group and reformat the data to split it by 'cardio'. Show the counts of each feature. You will have to rename one of the columns for the catplot to work correctly.
  df_cat = None

  # Draw the catplot with 'sns.catplot()'

  # Get the figure for the output
  fig = None

  # Do not modify the next two lines
  fig.savefig('catplot.png')
  return fig


# Function to draw Heat Map
def draw_heat_map():
  # Clean the data
  df_heat = None

  # Calculate the correlation matrix
  corr = None

  # Generate a mask for the upper triangle
  mask = None

  # Set up the matplotlib figure
  fig, ax = None

  # Draw the heatmap with 'sns.heatmap()'

  # Do not modify the next two lines
  fig.savefig('heatmap.png')
  return fig


# RUN FUNCTIONS
draw_cat_plot()
draw_heat_map()

Data description

The rows in the dataset represent patients and the columns represent information like body measurements, results from various blood tests, and lifestyle choices. You will use the dataset to explore the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices.

File name: medical_examination.csv

Feature	Variable Type	Variable	Value Type
Age	Objective Feature	`age`	int (days)
Height	Objective Feature	`height`	int (cm)
Weight	Objective Feature	`weight`	float (kg)
Gender	Objective Feature	`gender`	categorical code
Systolic blood pressure	Examination Feature	`ap_hi`	int
Diastolic blood pressure	Examination Feature	`ap_lo`	int
Cholesterol	Examination Feature	`cholesterol`	1: normal, 2: above normal, 3: well above normal
Glucose	Examination Feature	`gluc`	1: normal, 2: above normal, 3: well above normal
Smoking	Subjective Feature	`smoke`	binary
Alcohol intake	Subjective Feature	`alco`	binary
Physical activity	Subjective Feature	`active`	binary
Presence or absence of cardiovascular disease	Target Variable	`cardio`	binary

Instructions

Part A

Create a chart where we show the counts of good and bad outcomes for the cholesterol, gluc, alco, active, and smoke variables for patients with cardio=1 and cardio=0 in different panels. Complete the following tasks in main.py:

Import the data from medical_examination.csv and assign it to the df variable
Create the overweight column in the df variable
Normalize data by making 0 always good and 1 always bad. If the value of cholesterol or gluc is 1, set the value to 0. If the value is more than 1, set the value to 1.
Draw the Categorical Plot in the draw_cat_plot function
Create a DataFrame for the cat plot using pd.melt with values from cholesterol, gluc, smoke, alco, active, and overweight in the df_cat variable.
Group and reformat the data in df_cat to split it by cardio. Show the counts of each feature. You will have to rename one of the columns for the catplot to work correctly.
Convert the data into long format and create a chart that shows the value counts of the categorical features using the following method provided by the seaborn library import : sns.catplot()
Get the figure for the output and store it in the fig variable

Your plot should look like this:

Part B

Draw the Heat Map in the draw_heat_map function:

Clean the data in the df_heat variable by filtering out the following patient segments that represent incorrect data:
- height is less than the 2.5th percentile (Keep the correct data with (df['height'] >= df['height'].quantile(0.025)))
- height is more than the 97.5th percentile
- weight is less than the 2.5th percentile
- weight is more than the 97.5th percentile
Calculate the correlation matrix and store it in the corr variable
Generate a mask for the upper triangle and store it in the mask variable
Set up the matplotlib figure
Plot the correlation matrix using the method provided by the seaborn library import: sns.heatmap()
- Mask the upper triangle.

Your plot should look like this:

Acknowledgement

Content on this page is adapted from FreeCodeCamp.