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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