Mathematical operations allowed

At each level, we will describe briefly the type of math that is allowed, and more importantly, not allowed. At this level, we cannot perform any quantitative mathematical operations, such as addition or pision. These would not make any sense. Due to the lack of addition and pision, we obviously cannot find an average value at the nominal level. There is no average name or average job department.

We can, however, do basic counts using pandas' value_counts methods:

# Basic Value Counts of the Grade column
salary_ranges['Grade'].value_counts().head()


00000    61
07450    12
06870     9
07170     9
07420     9
Name: Grade, dtype: int64

The most commonly occurring Grade is 00000, meaning that that is our mode or most commonly occurring category. Because of our ability to count at the nominal level, graphs, like bar charts, are available to us:

# Bar Chart of the Grade column salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(20).plot(kind='bar')

The following is the result of the preceding code:

At the nominal level, we may also utilize pie charts:

# Bar Chart of the Grade column as a pie chart (top 5 values only)
salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(5).plot(kind='pie')

The following is the output of the preceding code: