For recent grads and new data scientists coming into the fold, Python has clearly become the most popular programming language of choice over the past 24 months. Considering its rate of growth and widespread usage, evidenced by the sheer count of mentions in Data Scientist job descriptions, we can say with a fair degree of confidence: there’s a strong likelihood that Python is here to stay. And, our customers are clamoring for it. Well, you asked and we listened. You can now write Python natively within Spotfire, without the need for other tools. Read on for more exciting developments on how you can get the most out of Python in Spotfire.
Write and manage Python natively
With the same ease and convenience of writing R language functions and managing those packages, Spotfire users will find the Python experience released in 10.7 similar to how stats and ML libraries may be leveraged to build data functions as the basis for exploratory analysis, dashboards, or advanced analytics applications. The Spotfire® Analyst client now comes with a bundled Python engine for adding or importing Python packages for use with Spotfire data functions. Writing Python functions natively within Spotfire blends a streamlined offering for visual and advanced analytics by:
- Eliminating barriers to sharing value with more stakeholders throughout the enterprise when relying upon several disparate tools, some open source
- Simplifying the high cost of task switching across various tools; translating single data science projects across platforms requires heavy manual work involving duplicative efforts (similar to the classic inefficiencies of legacy BI – ETL workflows)
A streamlined data science workflow benefits the enterprise
By embedding models into visual analytics, data science is driving better decision frameworks for improved outcomes. This makes each side of the “barbell” in large organizations more efficient: enabling citizen data scientists to self-serve quickly and use apps with advanced analytics underpinning them—essentially running Python functions as an engine within dashboards—all while liberating data scientists from low-value, time-consuming tasks.
Just as with the classic data preparation inefficiency of basic formatting tasks over actual analysis, these are wasteful activities for highly valuable, and expensive, data science resources. Furthermore, this duality also frees up Data Scientists to spend time modeling deeper analyses by running workflows created by Spotfire. In this way, Spotfire satisfies coders and non-coders alike, expanding the reach of data science across organizations by creating more advanced analytics applications.
As embedded analyses are widely shared and consumed through dashboards, this also prompts the savvy analyst or citizen data scientist to explore them or even customize completely new low-code custom apps through self-service analytics. It’s through the automation of these workflows that Spotfire serves end-to-end analytics use cases across the enterprise.
All with the appropriate governance
All of this is made available while showing appropriate care and concern for governing which packages are used across an organization. Designs of a seamless, invisible workflow in Spotfire ensures that all Python users utilize the same packages and versions of packages. Administrators or superusers can control which packages and versions of packages are automatically available, providing the potential for embedding Python packages in Spotfire for individual or group-level access privileges.
This is your opportunity to deliver more business value to your stakeholders and expand the reach of data science across your organization with advanced analytics applications using Python. For more on how to get the most out of Python in Spotfire, watch our on-demand Dr. Spotfire session.