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# Scalar Data Type Examples

In data science, we have several common scalar
data types.
We’re going to learn about the ones that you’re
most likely to encounter.
First, we have categorical data types.
We typically encounter three categorical data
types in data science.
First, we have a character, which represents
a single letter, digit, or symbol.
We can string together a sequence of characters
(called a character string) to represent words,
numbers, and bodies of text.
Character strings are very flexible for storing
data, but they are *not* very efficient in
terms of data processing and storage space.
Second, we have a Boolean, which represents
either a true or a false value (and only a
true or a false value).
Booleans allow us to efficiently store and
process data composed of either yes or no answers.
Third, we have an Enumeration, which represents
a set of named categories.
Enumerations allow us to efficiently store
and process lists of named categories that
contain a high degree of duplication.
Next, we have numerical data types.
We typically encounter three numerical data
types in data science.
First, we have an integer, which represents
a whole number.
Integers work well for storing and processing
numbers that do not contain fractional values.
Second, we have a decimal, which represents
a decimal fraction.
Decimals work well when we’re dealing with
fractional values (like money) that require
perfectly accurate decimal arithmetic.
Third, we have a float, which represents numbers
using a binary-equivalent of scientific notation.
Floats work well when we’re dealing with very
large or very small values but perfectly accurate
measurements and arithmetic are not required.
Finally, we have temporal data types.
We typically encounter four temporal data
types in data science.
First, we have a date, which represents time
as a calendar day.
A Date data type works well when we just need
to specify a year, a month, and a day but
nothing more.
Second, we have a time data type, which represents
a time of day.
A time data type is used when we just need
to represent an hour, a minute, a second,
and millisecond but not a date.
Third, we have a date-time data type, which
represents both a date and a time of day.
This data type is used when we need to represent
time across days.
Fourth, we have a date-time with time-zone
offset.
This data type is used when we need to represent
dates and times across multiple time-zones
around the world.
We could represent temporal data using numerical
data types, however, it’s often more efficient
and convenient to use these specialized temporal
data types instead.
There are several other scalar data types
you may encounter in data science.
However, the data types you’ve seen here are ones that you are most likely to encounter first.