# Specify probabilities of each category (must sum to 1.0) Optionally, you can also specify the weights to give to each value if you don’t want each element in the list to have an equal chance of being selected. Print(fake.boolean(chance_of_getting_true=25))įor categorical columns, you can specify a list of values to randomly choose from. You can even specify the percent the random value is likely to be “True” with boolean columns. Generating categorical columns based on probabilities/weights Print(fake.bothify('PROD-?-#', letters='ABCDE'))
#Generate fake data python code
If you needed to create fake data that needed a specific format, such as a product code or iPhone model, you can do that too: # Use bothify to generate random numbers(#) or letters(?). Generate columns that match specific formats Print('Catch phrase: ' + fake.catch_phrase()) Print('Bldg #: ' + fake.building_number()) Print('Street address: ' + fake.street_address()) Print('Company suffix: ' + pany_suffix()) If you prefer to create a company focused dataset, you can do that too. Print('ASCII Emails: ' + fake.ascii_email())įaker can easily generate realistic looking PII. Print('Free emails: ' + fake.ascii_free_email()) Print('Safe emails: ' + fake.ascii_safe_email()) Print('Company emails: ' + fake.ascii_company_email()) # Generate prefixes and suffixes (there are also gender specific versions e.g. Print('Female first names: ' + fake.first_name_female()) Print('Male first names: ' + fake.first_name_male()) For example: # There are specific versions of these generators There are providers for different types of data we can generate on a fake “customer” by calling the appropriate Faker provider. For example, we can easily generate 5 fake first names: # First nameįaker will generate random data every time it is called Once we have our instance, we can use that instance to call any number of fake data “providers” Faker includes. For this demo, we’ll create an instance of Faker called fake and use that instance to generate all our fake data. Next, let’s instantiate the Faker library.
The hana_ml library will be used to upload the dataset we create to SAP HANA Cloud. In addition to Faker and numpy, we’ll also need the handy pandas library. To begin, let’s make sure we have the necessary libraries installed. sales) based on a distribution or randomly select from a list. We will also use the Python numpy library since it will allow to create numeric fields (e.g. We’ll explore those most relevant for customer demos but the documentation details all the “providers” of fake data available in the library.
It is useful to create realistic looking datasets and can generate all types of data. For this demo, we’ll upload the newly created datasets to SAP HANA Cloud as tables.įaker is a Python library that generates fake data for you. Once we create the datasets, we have a lot of flexibility with how we use them.
#Generate fake data python how to
We can easily create such datasets in Python, and this blog will serve as a guide on how to use the Faker, numpy, and pandas libaries in Python to generate any datasets you need. Also, it would be nice to generate realistic looking PII data in case you needed to demonstrate data masking. Ideally, we would be able to create a dataset of any size easily and able to specify constraints on the data, such as matching data formats the customer may use or specifying the statistical distribution of the random data. We can create more engaging customer experiences if we had more realistic datasets that more closely resembled their own data. The Python code is available at and is released under an MIT license.As Solution Advisors, we often need to create custom datasets to support customer opportunities. The project documentation is available at and it can be installed from the Python packaging index. Finally, complex data generation approaches can be built up by combining generated values using dependent fields meta-fields and customisable data transformations. Health specific options include age, deceased flag and date (based on a risk of death algorithm), and UK NHS number.
Despite its name, Headfake can be used to generate all kinds of data including numerical / date data (following specified statistical distributions or as fake indexes) gender-specific names addresses and text data of different kinds. This tool allows users to use a shareable configuration file/template to generate fake data files and structures which can be embedded into development and testing. To address this we developed health data faker (Headfake), a Python package and command-line script. But, this is not always a useful approach, and a more declarative approach for data generation can be a better option in some cases. This has led to an increase in the use of techniques such as synthetic data generation (which learn from existing data sets). Health data is often of a sensitive nature and cannot be shared.
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