Generate scatter plot with missingness

In this exercise you'll create a scatter plot consisting of both missing and non-missing values. You will utilize the function fill_dummy_values() which you created in the previous exercise for filling in dummy values in the DataFrame diabetes_dummy.

The nullity of a column is calculated using the .isnull() method. The nullity returns a Series (pd.Series) of True or False.

For setting different colors to the missing and non-missing values, you can simply combine the nullity using OR(|) operation on the columns that you are plotting, resulting in:

True \(\rightarrow\) Either col1 or col2 or both values are missing.
False \(\rightarrow\) Neither of col1 and col2 values are missing.

The DataFrame diabetes and the function fill_dummy_values() have been loaded for your usage.

This exercise is part of the course

Dealing with Missing Data in Python

View Course

Exercise instructions

Use OR operation to combine nullity of Skin_Fold and BMI.
Fill dummy values in diabetes_dummy using the function fill_dummy_values().
Create a scatter plot of 'BMI' versus 'Skin_Fold'; note Y versus X implies Y-axis against X-axis or Y as a function of X.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Use OR operation to combine Skin_Fold and BMI nullity
nullity = ___

# Fill dummy values in diabetes_dummy
diabetes_dummy = ___

# Create a scatter plot of BMI versus Skin_Fold
diabetes_dummy.plot(x=___, y=___, kind='___', alpha=0.5,                     
                    # Set color to nullity of BMI and Skin_Fold
                    c=___, 
                    cmap='rainbow')

plt.show()

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