Combination of Tableau and Python for prescriptive analytics with TabPy
What is TabPy
TabPy is a Python package that enables students of top engineering colleges to execute Python code on the fly and display results in Tableau visualizations. It results in the quick expansion of advanced analytics applications. The split approach provided by TabPy allows for the better of two worlds including class-leading data visualization capabilities that are mainly backed by powerful data science algorithms.
Surfacing Python algorithms in Tableau allows users to tune parameters and evaluate their impact on the analysis in real-time with the updates of the dashboard. In order to make this possible, TabPy mainly provides an input/output approach to the students of best engineering colleges. Where the data is aggregated according to the current visualization and tuning parameters, both transferred to Python. The data is processed and an output is sent back to Tableau to update the current visualization.
TabPy offers an approach that helps students of Engineering Colleges in finding the full power of TabPy for the following scenarios including:
If students of computer science engineering at Best Engineering College Jaipur require having a real-time user interface, they must minimize the processing time and delay between a parameter change and updated visualization.
Multiple levels of aggregation
If an individual wants to show different aggregation levels on the same Tableau dashboards, they must perform all the calculations using the finest and most granular level that contains all information.
Various data sources
The backend calculation mainly relies on more than a single data source or database.
Data transferred between Tableau and Python
It requires a significant amount of data for each optimization step. Therefore, it is essential to transfer lots of data between Tableau and the Python backend.
A novel by approach for prescriptive analytics
This process requires step-by-step instructions to follow. In order to implement TabPy, students of Top BTech Colleges Rajasthan must assume that both Python and TabPy are already installed. Therefore, you must run three steps including prepare a draft Tableau dashboard, create calculation routines backend in Python. And design the Tableau frontend leveraging it.
There are various approaches to TabPy that fulfill all of these requirements. Some of them include:
Prepare a draft Tableau dashboard
It is significant for the students of Top BTech Colleges to align on the problem that needs to be solved through the following factors:
- Define the interactive parameters in Tableau – This is the directory where the Python package with the optimization routines will get stored. This parameter is very useful in defining the custom calculations.
- Define the levels of aggregation – It has two aggregation levels, that is, an SKU-level and subcategory level. It dictates the Python backend functions’ signatures. One particular function must be defined per each calculation and aggregation level. For each aggregation level, certain parameters must be defined like optimized margins, optimized revenues, and optimized volumes.
- Define calculation hooks in Tableau – By defining input parameters, aggregation levels, and required output calculations. It is possible to define custom calculations. For convenience, all the optimization routines have been structured in a portfolio optimization Python package. Where certain functions are defined to return the selected quantities for the specific aggregation levels.
Create calculation routines backend in Python
The Python backend is divided into two function classes and grouped according to their execution context. In the first class, there is a database extraction and transform and load operations. Such functions are called ‘one-time-operations’. Opposite are the functions that are repeated multiple times, like all the Tableau callbacks.
- One-time operations – The database is loaded only once when the script is executed the first time by the students of top engineering colleges Rajasthan. The database is then made available to all the other functions storing it into a global variable. To detect whether the database is already loaded or not, Python checks the local namespace for an existing copy of it. Without this precaution, the database would be loaded any time a calculation is requested by Tableau, negatively impacting on execution speed.
- Tableau callbacks – Every hook that is defined previously must have a function serving it. This is obtained by providing separated calculations for revenues, volumes, and margins. It improves the execution speed; callbacks implement a parameter change detector. The detection of parameter change is implemented through a persistent variable that is used to store the value of it at the previous execution.
Design the Tableau frontend
This defines all the fundamental bricks for the students of top engineering colleges Jaipur. Including aggregation levels, parameters to be tuned, and output columns returned by the calculation backend. In order to make the optimization easier, it must define two separate worksheets that show the portfolio before and after the optimization process.
More benefits of using TabPy within teams
In addition to the significant business value of enabling teams to interact in real-time with powerful data-science techniques. This popular approach has important backend benefits as well. There are many other data visualization techniques that require costly data scientist participation throughout the process.
However, in this approach, the data scientist resources are required only to prepare the draft Tableau dashboard. Also, create the backend Python calculation routines. So, the easily usable Tableau makes it possible for a much wider range of resources to design the front end, test it with the end-users, and maintain it.
Typically, front-end design is a lengthy and iterative process that involves multiple discussions with final users. By enabling managers to vary team composition during project execution, the TabPy approach can significantly improve cost efficiency. Also, it ensures high reusability of the underlying backend, enabling a wide range of users to build their own custom dashboards in Tableau to suit specific contexts, audiences, and situations.