--description--

You will be <a href="https://app.ona.com/?autostart=true#https://github.com/freeCodeCamp/boilerplate-medical-data-visualizer/" target="_blank" rel="noopener noreferrer nofollow">working on this project with our Ona starter code</a>.

We are still developing the interactive instructional part of the Python curriculum. For now, here are some videos on the freeCodeCamp.org YouTube channel that will teach you everything you need to know to complete this project:

<a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">Python for Everybody Video Course</a> (14 hours)
<a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">How to Analyze Data with Python Pandas</a> (10 hours)

--instructions--

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.

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

Create a chart similar to examples/Figure_1.png, 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.

By each number in the medical_data_visualizer.py file, add the code from the associated instruction number below.

Import the data from medical_examination.csv and assign it to the df variable.
Add an overweight column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight.
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.
Do not modify the next two lines.
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:
- diastolic pressure is higher than systolic (Keep the correct data with (df['ap_lo'] <= df['ap_hi']))
- 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().
Do not modify the next two lines.

Development

Write your code in medical_data_visualizer.py. For development, you can use main.py to test your code.

Testing

The unit tests for this project are in test_module.py. We imported the tests from test_module.py to main.py for your convenience.

Submitting

Copy your project's URL and submit it to freeCodeCamp.

--hints--

It should pass all Python tests.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.

Medical Data Visualizer