Python Numerical Library
Advanced Data Analytics Functions for Python Applications
Is your Data Science team using Python and asking the Engineering team to implement their data models? Are the models out of date by the time the Engineering team completes the implementation? Using IMSL Python Numerical Library (PyNL), your Data Science team can leverage all of the Mathematical and Statistical algorithms found in IMSL C.
Access the Power of the IMSL C Numerical Library from Python
PyNL provides mathematical and statistical functionality for building advanced data analytics algorithms in Python. Built on IMSL C Numerical Library, PyNL brings 40 years of numerical expertise, rigorous testing, and native performance to the Python environment. By leveraging these algorithms, users can save weeks or months of development effort by embedding PyNL functions rather than building new algorithms from scratch.
PyNL requires a commercial or evaluation version of IMSL C Numerical Library. Request an evaluation today to try out PyNL in your application.
Functional Areas Python Numerical Library
|Data Mining and Forecasting Functionality||Mathematics|
|Decision Trees||Linear Algebra|
|Apriori Analysis||Eigensystem Analysis|
|Cluster Analysis||Linear Programming|