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.
#Fake data generator 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. As Solution Advisors, we often need to create custom datasets to support customer opportunities.
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