Detecting missing values
Datasets usually come with hidden missing values filled in for missing values like 'NA', '.' or others. In this exercise, you will work with the college dataset which contains various details of college students. Your task is to identify the missing values by analyzing the dataset.
To achieve this, you can use the .info() method from pandas and the numpy function sort() along with the .unique() method to clearly distinguish the dummy value representing the missing data.
The college.csv file has been loaded for you. The packages numpy and pandas have already been imported as np and pd respectively.
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.
# Read the dataset 'college.csv'
college = ___
print(college.head())