Replacing hidden missing values
In the previous two exercises, you worked on identifying and handling missing values while importing a dataset. In this exercise, you will work on identifying hidden missing values in your data and handling them. You'll use the diabetes
dataset which has already been loaded for you.
The diabetes
DataFrame has 0's in the column BMI
. But BMI
cannot be 0. It should instead be NaN
. In this exercise, you'll learn to identify such discrepancies. You'll perform simple data analysis to catch missing values and replace them. Both numpy
and pandas
have been imported into your DataFrame as np
and pd
respectively.
Additionally, you can play around with the dataset like printing it's .head()
, .info()
etc. to get more familiar with the dataset.
This exercise is part of the course
Dealing with Missing Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the description of the data
print(___)