![]() phone_number_full may generate an international number with different formatĬode example: se=myDB.gen_data_series(data_type='date').phone_number_simple generates 10 digit US number in xxx-xxx-xxxx format.Company, Job title, phone number, license plate.Name, country, city, real (US) cities, US state, zipcode, latitude, longitude.Returns a Pandas series object with the desired number of entries and data type. But first, you have to create an object of pydb class: myDB = pydbgen.pydb() The gen_table() method allows you to build a database with as many tables as you want, filled with random data and fields of your choice. (On Mac OS), first install pip, curl -o get-pip.pyĬurrent version (1.0.0) of pydbgen comes with the following primary methods, (On Linux and Windows) You can use pip to install pydbgen: pip install pydbgen Here is the link if you want to look up more about Faker package, The original contribution of pydbgen is to take the single data-generating function from Faker and use it cleverly to generate Pandas data series or dataframe or SQLite database tables as per the specification of the user. Therefore, a simple phone number data type is introduced in pydbgen. Also the default phone number generated by Faker is free-format and does not correspond to US 10 digit format. Original function is written for few data types such as realistic email and license plate. Finally, the TABLE is inserted into a new or existing database file of user's choice.Īt its core, pydbgen uses Faker as the default random data generating engine for most of the data types. One can also designate a "PRIMARY KEY" for the database table. User can specify the number of samples needed. ![]() This Python package generates a random database TABLE (or a Pandas dataframe, or an Excel file) based on user's choice of data types (database fields). While it is easy to generate random numbers or simple words for Pandas or dataframe operation learning, it is often non-trivial to generate full data tables with meaningful yet random entries of most commonly encountered fields in the world of database, such as Would it not be great to have a simple tool or library to generate a large database with multiple tables, filled with data of one's own choice?Īfter all, databases break every now and then and it is safest to practice with a randomly generated one :-) Often, beginners in SQL or data science struggle with the matter of easy access to a large sample database file (. Tirthajyoti Sarkar, Fremont, USA Introduction ![]()
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