How to generate synthetic data using Python

Python is one of the most popular computer languages ​​today, especially in the field of data.

Python can use three libraries to generate synthetic data

1. Scikit-learn

Scikit-learn is one of the most widely used Python libraries for machine learning tasks. One, provides implementations of almost classical algorithms that can generate data for regression, classification, or clustering tasks.

2. SymPy

SymPy is another library that helps users generate synthetic data. Users can specify symbolic expressions for the data they want to create, helping users create synthetic data as needed.

3. Pydbgen

Categorical data can also be generated using Python’s Pydbgen library. Many different types of data can be easily generated using this library, including:

Name, country, city, zip code, latitude and longitude;

Time and date;

Email;

Company, position, phone number and license plate.

Python code to create a simple data frame

导入pydbgen
从pydbgen导入pydbgen
src_db=pydbgen.pydb()
pydb_df=src_db.gen_dataframe(1000,fields=['name','city','phone','license_plate'],phone_simple=True)
pydb_df.head()

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