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
Use the data to complete the following tasks in medical_data_visualizer.py:
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 the 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.
Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's catplot(). The dataset should be split by Cardio so there is one chart for each cardio value.
Clean the data. Filter 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
Create a correlation matrix using the dataset. Plot the correlation matrix using seaborn's heatmap(). Mask the upper triangle. The chart should look like examples/Figure_2.png.
Any time a variable is set to None, make sure to set it to the correct code.
Unit tests are written for you under test_module.py.
Instructions
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
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
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:
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.