WEBINAR / EVENT
Building Gurobi Models from Pandas Dataframes
Webinar: Building Gurobi Models from Pandas Dataframes.
January 31 | February 7 | February 9
WEBINAR / EVENT
Building Gurobi Models from Pandas Dataframes
Webinar: Building Gurobi Models from Pandas Dataframes.
January 31 | February 7 | February 9
WEBINAR / EVENT
Building Gurobi Models from Pandas Dataframes
Webinar: Building Gurobi Models from Pandas Dataframes.
January 31 | February 7 | February 9
Event Recap
As the de-facto standard in Python for data manipulation and analysis, pandas is deeply integrated into many analytics projects and tools. Gurobi’s Python API, gurobipy, does not directly interact with standard pandas data structures, so incorporating decision optimization into data science applications is not as straightforward as it could be. For better interoperability between the two libraries, what is needed is a higher-level syntax to build optimization models directly from suitably structured pandas data.
Our solution is gurobipy-pandas, a convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series, and to extract solutions as pandas objects. This webinar will walk through basic concepts and complete modeling examples to demonstrate best practices for using this library.
Presented Materials:
Download the presentation, here.
Event Recap
As the de-facto standard in Python for data manipulation and analysis, pandas is deeply integrated into many analytics projects and tools. Gurobi’s Python API, gurobipy, does not directly interact with standard pandas data structures, so incorporating decision optimization into data science applications is not as straightforward as it could be. For better interoperability between the two libraries, what is needed is a higher-level syntax to build optimization models directly from suitably structured pandas data.
Our solution is gurobipy-pandas, a convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series, and to extract solutions as pandas objects. This webinar will walk through basic concepts and complete modeling examples to demonstrate best practices for using this library.
Presented Materials:
Download the presentation, here.
Event Recap
As the de-facto standard in Python for data manipulation and analysis, pandas is deeply integrated into many analytics projects and tools. Gurobi’s Python API, gurobipy, does not directly interact with standard pandas data structures, so incorporating decision optimization into data science applications is not as straightforward as it could be. For better interoperability between the two libraries, what is needed is a higher-level syntax to build optimization models directly from suitably structured pandas data.
Our solution is gurobipy-pandas, a convenient wrapper library to connect pandas with gurobipy. It enables users to efficiently build mathematical optimization models from data stored in DataFrames and Series, and to extract solutions as pandas objects. This webinar will walk through basic concepts and complete modeling examples to demonstrate best practices for using this library.
Presented Materials:
Download the presentation, here.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Simon Bowly
Senior Developer
Dr. Simon Bowly completed his PhD in 2019 at the University of Melbourne, focusing on generating difficult test cases for optimization algorithms. Simon has previously lectured in applied data science and discrete optimization at Monash University, worked in optimization consulting for transport and logistics projects, and developed software for real time analytics in power generation applications.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Simon Bowly
Senior Developer
Dr. Simon Bowly completed his PhD in 2019 at the University of Melbourne, focusing on generating difficult test cases for optimization algorithms. Simon has previously lectured in applied data science and discrete optimization at Monash University, worked in optimization consulting for transport and logistics projects, and developed software for real time analytics in power generation applications.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Senior Developer
Simon Bowly
Dr. Simon Bowly completed his PhD in 2019 at the University of Melbourne, focusing on generating difficult test cases for optimization algorithms. Simon has previously lectured in applied data science and discrete optimization at Monash University, worked in optimization consulting for transport and logistics projects, and developed software for real time analytics in power generation applications.
