1. To get an idea of how the equals operator works, let's compare each element to a scalar value:

>>> college_ugds_ == .0019

2. This works as expected but becomes problematic whenever you attempt to compare DataFrames with missing values. This same equals operator may be used to compare two DataFrames with one another on an element-by-element basis. Take, for instance, college_ugds_ compared against itself, as follows:

>>> college_self_compare = college_ugds_ == college_ugds_
>>> college_self_compare.head()

3. At first glance, all the values appear to be equal, as you would expect. However, using the all method to determine if each column contains only True values yields an unexpected result:

>>> college_self_compare.all()
UGDS_WHITE    False
UGDS_BLACK    False
UGDS_HISP     False
UGDS_ASIAN    False
UGDS_AIAN     False
UGDS_NHPI     False
UGDS_2MOR     False
UGDS_NRA      False
UGDS_UNKN     False
dtype: bool

4. This happens because missing values do not compare equally with one another. If you tried to count missing values using the equal operator and summing up the boolean columns, you would get zero for each one:

>>> (college_ugds_ == np.nan).sum()
UGDS_WHITE    0
UGDS_BLACK    0
UGDS_HISP     0
UGDS_ASIAN    0
UGDS_AIAN     0
UGDS_NHPI     0
UGDS_2MOR     0
UGDS_NRA      0
UGDS_UNKN     0
dtype: int64

5. The primary way to count missing values uses the isnull method:

>>> college_ugds_.isnull().sum()
UGDS_WHITE    661
UGDS_BLACK    661
UGDS_HISP     661
UGDS_ASIAN    661
UGDS_AIAN     661
UGDS_NHPI     661
UGDS_2MOR     661
UGDS_NRA      661
UGDS_UNKN     661
dtype: int64

6. The correct way to compare two entire DataFrames with one another is not with the equals operator but with the equals method:

>>> college_ugds_.equals(college_ugds_)
True