Ignite performance of common python and pandas constructs by exploiting the capabilities of NumPy, SciPy, and pandas, powered by oneAPI.

Learn how to take advantage of key Intel architectural innovations and optimized data science and machine learning libraries via smart application of NumPy, SciPy, and pandas techniques to achieve substantial speed-ups.

Key takeaways:

• Learn about NumPy aggregations, Universal Functions, Broadcasting, and other techniques used to expose CPU vectorization “under the hood”
• Use these techniques to achieve outsized performance gains by replacing python loop-centric or list-comprehension applications with smarter equivalents that are more maintainable, more efficient, and much faster
• Do hands-on measurement of your code’s acceleration to discover achieved performance boost, whether that’s 10X or over 100X