![]() Next is to import the module and initialize a faker generator like this: This is where the python Faker library comes in handy. So we need dummy/fake people's bank details to test run the app database. Let assume we are need to test a banking database, so due to sensitive nature of this kind of data we can use a real production data. For example you can generate real names, addresses, latitude/longitude coordinates, phone numbers, fax numbers, occupations, profile titles, email addresses, website addresses, job titles, text data, random numbers, currencies, words, birthdates, hashes and uuids, date/time etc. With this python package, you can generate test data without infringing on peoples' privacy. ![]() On this page, I will introduce you to a python module that will help you generate good amount of dummy or fake data that looks just like the once in real life for you to test run your application. If you find yourself in the cenario above, then you are not alone. Then you suddenly realized that you don't have such data set available so you won't be able to put your app to real world test before it's final launch. ![]() There times when you need to have access to large amount of real world data to test an app you are developing. 3) To protect data Privacy due to security and many other constraints.
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