Python is often used for data analysis and integration into web applications due to its large library of packages. It is well-suited for machine learning and data science tasks using libraries like NumPy, SciPy, Pandas, and Matplotlib that make performing complex data analysis easier. The combination of Python's teaching capabilities and extensive library ecosystem allows for developing modern models and forecasts directly applicable to production processes.

1. Python libraries

2. What is python?
 Python is often a choice for development that needs to be
applied for census and data analysis to work, or data scientists
whose work should be integrated into web applications or the
production environment.
 In particular, python actually looks at the learning point of the
machine.
 The combination of python's teaching and library libraries
makes it particularly suited to develop modern lenses and
predecessors forecasts directly connected to the production
process.

3. Cont..
 One of the biggest assets in python is the large library.
Libraries are the types and timetables of a particular
language.
 Hard libraries can make it easier for developers to
perform complex tasks and not rewrite many code
lines.

4. BASIC LIBRARIES FOR DATA SCIENCE
1.Numpy
2.Scipy
3.Pandai
4.Python
5.matplotlib