Feature Types

Tabular data (pd.DataFrame), as discussed previously, is made up of observations (rows) and features (columns). Data type (df.dtypes) of features fall into two primary categories: numeric and categorical.

There also exists a third special category of data type called missing. Missing data is a special data type because it is not a data type at all. It is a placeholder for a value that is not known or not applicable. Missing data is represented by NaN (not a number) in pandas. More on missing data in a bit.

To study these feature types, we will use the dataset of food safety scores for restaurants in San Francisco. The scores and violation information have been made available by the San Francisco Department of Public Health.

import pandas as pd 

data = pd.read_csv('https://raw.githubusercontent.com/fahadsultan/csc272/main/data/restaurants_truncated.csv', index_col=0)
data.head()

	id	zip	phone	lat	lng	type	score	risk	violation
0	70064	94103.0	1.415565e+10	NaN	NaN	Routine - Unscheduled	75.0	High Risk	Improper reheating of food
1	90039	94103.0	NaN	NaN	NaN	Routine - Unscheduled	81.0	High Risk	High risk food holding temperature
2	89059	94115.0	1.415369e+10	NaN	NaN	Complaint	NaN	NaN	NaN
3	91044	94112.0	NaN	NaN	NaN	Routine - Unscheduled	84.0	Moderate Risk	Inadequate and inaccessible handwashing facili...
4	62768	94122.0	NaN	37.765421	-122.477256	Routine - Unscheduled	90.0	Low Risk	Food safety certificate or food handler card n...

Numerical Features

Numeric data is data that can be represented as numbers. These variables generally describe some numeric quantity or amount and are also sometimes referred to as “quantitative” variables.

Since numerical features are already represented as numbers, they are already ready to be used in machine learning models and there is no need to encode them.

In the example above, numerical features include zip, phone, lat, lng, score.

data[['zip', 'phone', 'lat', 'lng', 'score']].head()

	zip	phone	lat	lng	score
0	94105	NaN	37.787925	-122.400953	82.0
1	94109	NaN	37.786108	-122.425764	NaN
2	94115	NaN	37.791607	-122.434563	82.0
3	94115	NaN	37.788932	-122.433895	78.0
4	94110	NaN	37.739161	-122.416967	94.0

Discrete Features

Discrete data is data that is counted. For example, the number of students in a class is discrete data. You can count the number of students in a class. You can not count the number of students in a class and get a fraction of a student. You can only count whole students.

In the restaurants inspection data set, zip, phone, score are discrete features.

data[['zip', 'phone', 'score']].head()

	zip	phone	score
0	94105	NaN	82.0
1	94109	NaN	NaN
2	94115	NaN	82.0
3	94115	NaN	78.0
4	94110	NaN	94.0

Continuous Features

Continuous data is data that is measured. For example, the height of a student is continuous data. You can measure the height of a student. You can measure the height of a student and get a fraction of a student. You can measure a student and get a height of 5 feet and 6.5 inches.

In the restaurants inspection data set, lat, lng are continuous features.

data[['lat', 'lng']].head()

	lat	lng
0	37.787925	-122.400953
1	37.786108	-122.425764
2	37.791607	-122.434563
3	37.788932	-122.433895
4	37.739161	-122.416967

Categorical Features

Categorical data is data that is not numeric. It is often represented as text or a set of text values. These variables generally describe some characteristic or quality of a data unit, and are also sometimes referred to as “qualitative” variables.

data[['type', 'risk', 'violation']].head()

	type	risk	violation
0	Routine - Unscheduled	High Risk	High risk food holding temperature
1	Complaint	NaN	NaN
2	Routine - Unscheduled	Low Risk	Inadequate warewashing facilities or equipment
3	Routine - Unscheduled	Low Risk	Improper food storage
4	Routine - Unscheduled	Low Risk	Unapproved or unmaintained equipment or utensils

Ordinal Features

Ordinal data is data that is ordered in some way. For example, the size of a t-shirt is ordinal data. The sizes are ordered from smallest to largest. The sizes are more or less than each other. They are different and ordered.

data[['risk']].head()

	risk
0	High Risk
1	NaN
2	Low Risk
3	Low Risk
4	Low Risk

Nominal Features

Nominal data is data that is not ordered in any way. For example, the color of a car is nominal data. There is no order to the colors. The colors are not more or less than each other. They are just different.

data[['type', 'violation']].head()

	type	violation
0	Routine - Unscheduled	High risk food holding temperature
1	Complaint	NaN
2	Routine - Unscheduled	Inadequate warewashing facilities or equipment
3	Routine - Unscheduled	Improper food storage
4	Routine - Unscheduled	Unapproved or unmaintained equipment or utensils

`.dtypes` attribute

.dtypes is an attribute of a DataFrame that returns the data type of each column. The data types are returned as a Series with the column names as the index labels.

data.dtypes

id             int64
zip          float64
phone        float64
lat          float64
lng          float64
type          object
score        float64
risk          object
violation     object
dtype: object

In pandas, object is the data type used for string columns, while int64 and float64 are used for integer and floating-point columns, respectively.

`.astype()`

Cast a pandas object to a specified dtype

data.head()

	id	zip	phone	lat	lng	type	score	risk	violation
0	70064	94103.0	1.415565e+10	NaN	NaN	Routine - Unscheduled	75.0	High Risk	Improper reheating of food
1	90039	94103.0	NaN	NaN	NaN	Routine - Unscheduled	81.0	High Risk	High risk food holding temperature
2	89059	94115.0	1.415369e+10	NaN	NaN	Complaint	NaN	NaN	NaN
3	91044	94112.0	NaN	NaN	NaN	Routine - Unscheduled	84.0	Moderate Risk	Inadequate and inaccessible handwashing facili...
4	62768	94122.0	NaN	37.765421	-122.477256	Routine - Unscheduled	90.0	Low Risk	Food safety certificate or food handler card n...

data['zip'].astype(int)