# Sdtypes Let SDV know what type of data you have in each column of your table. In the SDV, the data types are specified by **sdtype**, denoting a semantic or statistical meaning. SDV is designed to create synthetic data differently based on each sdtype. {% hint style="info" %} An sdtype is a high level concept that tells SDV what your data *means*. An sdtype does not depend on how a computer stores the data. A single sdtype (such as `"categorical"`) can be stored by a computer in several ways (string, integer, etc). {% endhint %} ## Common Sdtypes Below are 5 common sdtypes that describe columns in a dataset. ### Boolean A `boolean` columns contains `TRUE` or `FALSE` values and may contain some missing data. ```json { "is_active": { "sdtype": "boolean" } } ``` {% hint style="success" %} **Synthetic data goal:** Your synthetic data will have the same proportion of True/False/missing values as the real data. {% endhint %} ### Categorical Sdtype `categorical` describes columns that contain distinct categories. The defining aspect of a categorical column is that **only the values that appear in the real data are valid**. The categories may be ordered or unordered. {% hint style="info" %} An example of categorical data is tax payer status such as `Single`, `Married filing jointly`, `Widowed`, etc. Only these distinct categories are allowed. {% endhint %} ```json { "gender": { "sdtype": "categorical" } } ``` {% hint style="success" %} **Synthetic data goal:** Your synthetic data will contain the exact same category values as the real data, in similar proportions. *If you want the synthetic data to include new values that were not in the original data, then the column is not categorical. See* [*other sdtypes*](#other-sdtypes) *below.* {% endhint %} ### Datetime Sdtype `datetime` describes columns that indicate a point of time. This can be at any granularity: to the nearest day, minute, second or even nanosecond. Typically, the datetime will be represented as a string. **Properties** * (required) `datetime_format`: A string describing the format as defined by [Python's strftime module](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes). ```json { "start_date": { "sdtype": "datetime", "datetime_format": "%Y-%m-%d" } } ``` {% hint style="success" %} **Synthetic data goal:** Your synthetic data will contain datetime values that are within the same overall range and distribution shape as the real data. The values will also conform to the datetime format. {% endhint %} ### Numerical Sdtype `numerical` describes data with numbers. The defining aspect of numerical data is that **there is an order and you can apply a variety of mathematical computations** to the values (average, sum, etc.) The actual values may follow a specific format, such as being rounded to 2 decimal digits and remaining between min/max bounds. {% hint style="info" %} Some data may appear numerical but actually represents distinct categories. For example, HTTP response codes such as `200`, `404`, etc. are categorical data. These numbers don't have a specific order, and they cannot be combined or averaged. {% endhint %} **Properties** * `computer_representation`: A string that represents how you'll ultimately store the data. This determines the min and max values allowed Available options are: `'Float'`, `'Int8'`, `'Int16'`, `'Int32'`, `'Int64'`, `'UInt8'`, `'UInt16'`, `'UInt32'`, `'UInt64'` ```json { "age": { "sdtype": "numerical", "computer_representation": "Int64" }, "transaction_amt": { "sdtype": "numerical", "computer_represntation": "Float" } } ``` {% hint style="success" %} **Synthetic data goal:** Your synthetic data will contain numerical values that are within the same overall range and distribution shape as the real data. They will also be rounded to the same precision as your real data. {% endhint %} ### ID Sdtype `id` describes columns that are used to identify rows (eg. as a primary or foreign key). ID columns do not have any other mathematical or special meanings. Typically, an ID column follows a particular structure, for example being exactly 8 digits long with a `-` in the middle. **Properties** * `regex_format`: A string describing the format of the ID as a [regular expression](https://docs.python.org/3/library/re.html) ```json { "product_code": { "sdtype": "id", "regex_format": "[0-9]{4}-[0-9]{4}" } } ``` {% hint style="success" %} **Synthetic data goal:** Your synthetic data will contain brand new, randomly generated IDs based on the regex. If you have multiple tables, the primary and foreign key IDs will match up. {% endhint %} ## Additional Sdtypes: Domain-Specific Concepts & PII You may find that some of the columns in your dataset represent high-level, concepts in your domain. Such data might also contain sensitive, Personal Identifiable Information (PII). **Properties** * `pii`: A boolean denoting whether the data is sensitive * (default) `true`: The column is sensitive, meaning the values should be anonymized. If not provided, we assume that the column is PII. * `false`: The column is not sensitive, meaning the exact set of values can be reused in the synthetic data ```json { "user_ssn": { "sdtype": "ssn" }, "user_city": { "sdtype": "city", "pii": false } } ``` {% hint style="success" %} **Synthetic data goals:** Your synthetic data will contain entirely new values that do not necessarily appear in the original data. If you are using SDV Enterprise, the synthetic data may conform to some generic properties that are non-identifiable (eg. an area code of a phone number). {% endhint %} Browse below for some common sdtypes related to different concepts. {% tabs %} {% tab title="Personal Info" %} These sdtypes describe the information about a person.
＊phone_numberA local or international phone number such as '+1(555)123-4567'. Different countries have different formats.
＊emailA person's email such as 'first_last@gmail.com'
ssnA social security number such as 000-00-0000
first_nameA person's first name
last_nameA person's last name
*＊ Licensed, enterprise users will see higher quality data. The SDV will extract the deeper meaning and replicate it in the synthetic data. To learn more,* [*visit our website*](https://datacebo.com/pricing/)*.* {% endtab %} {% tab title="Location" %} These sdtypes describe a location around the world.
＊country_codeA 2-character country code such as 'US'
＊administrative_unitThe name of a region inside the country such as 'Massachusetts'. Countries call this concept different names such as state or province.
＊state_abbrFor countries that call their regions states, this refers to the 2-character code such as 'MA'
＊cityThe full name of the city such as 'Boston'
＊postcodeThe internationally-recognized, 5-digit postcode such as 02116
＊street_addressThe street and building number such as '123 Main St'. The exact format of this may vary by country
＊secondary_addressAdditional information about units in the building, such as 'Apartment #3'.
latitudeThe latitude of a location, expressed as a decimal
longitudeThe longitude of a location, expressed as a decimal
*＊ Licensed, enterprise users will see higher quality data. The SDV will extract the deeper meaning and replicate it in the synthetic data. To learn more,* [*visit our website*](https://datacebo.com/pricing/)*.* {% endtab %} {% tab title="Networking" %} These sdtypes describe information about computer networks and the internet.
ipv4_addressAn IP address, using the v4 protocol
ipv6_addressAn IP address, using the v6 protool
mac_addressA media access control address
user_agent_stringA user agent string sent by the HTTP protocol
{% endtab %} {% tab title="Banking" %} These sdtypes describe information needed for banking functions such as payment transfers.
ibanAn international bank account number
swift11A SWIFT bank code that uses 11 digits
swift8A SWIFT bank code that uses 8 digits
credit_card_numberA credit card number, expressed using digits
{% endtab %} {% tab title="Automotive" %} These sdtypes describe concepts from the automotive industry.
vinA vehicle identification number
license_plateA license plate number, expressed using digits, letters or other characters. The format varies by country.
{% endtab %} {% tab title="Other" %} Many other sdtypes are possible. The SDV models can use the [Python Faker library](https://faker.readthedocs.io/en/master/providers.html) for new data types. You can input any of the function names as sdtypes. For example, inputting the sdtype `passport_number` will use [this function](https://faker.readthedocs.io/en/master/providers/faker.providers.passport.html#faker.providers.passport.Provider.passport_number) to generate meaningful numbers. For full SDV support, [file a request](https://github.com/sdv-dev/SDV/issues/new/choose) to help us prioritize other data types. {% endtab %} {% endtabs %} ### FAQs
How does the SDV factor in different countries or languages Many concepts vary based on the country and language. For example, phone numbers are represented differently in different countries. The SDV aims to provide worldwide support. You can specify the locales when you create a synthesizer. This lets the SDV know that any higher-level concepts should conform to the right formatting rules for that country. For example, assume you provide the following info: ```python from sdv.single_table import GaussianCopulaSynthesizer synthesizer = GaussianCopulaSynthesizer( metadata, locales=['en_US', 'nl_BE']) ``` Then for every concept described in your metadata, the SDV will generate values only from the US or Belgium in the appropriate language (English or Dutch).
Does the SDV understand the context of PII data? The public SDV randomly creates values corresponding to the concept, without taking additional context into account. Sometimes this may not be enough. For example, you may want to extract geographical areas from `phone_number` to ensure that it follows the same patterns. These features are available to licensed users. To learn more, [contact us](https://datacebo.com/contact/).