Overview

If you ever create Data Functions in Spotfire, our Python package will be a new, valuable resource for you. The package is called spotfire-dsml v1.1.3 and can be downloaded from PyPI.

This Exchange component gathers assets created based on the functions in spotfire-dsml package. You can find here the documentation for this Python library but also various ready-to-use data functions together with Spotfire dashboard examples. You can find features from these distinctive sub-modules:

Modules available in release version 2.0.0:

• Time Series (time_series): Time series can be messy and challenging to work with. This sub-module contains functions for time-series data which specializes in time-series preprocessing and smoothing, ensuring your analyses are fast, accurate, and reliable. 
• NLP (nlp_preprocessing): For those delving into the world of text analytics, this sub-module offers pipeline-centric preprocessing solutions. It simplifies text data preparation, a critical step in natural language processing tasks. 
• Geoanalytics (geoanalytics): This sub-module contains geospatial analytics focusing on calculations dedicated to areas, shapes, distances and other relevant geolocation aspects. The results from these functions can be afterwards beneficially leveraged in Spotfire dashboards that offers rich, multi-layered map charts to represent geospatial data. 
• Missing Data Analysis (missing_data): Handling missing data is a typical step needed in any analysis. Use this module with wide range of features to get relevant info about missing data and leverage methods for missing data removal and imputation. 

Modules available in release version 1.1.0 (only dashboard examples, no exported data functions):

• ML Modeling (ml_modeling): Dive into pipeline-centric model training and evaluation. Whether you're a seasoned data scientist or just starting, this module equips you with the tools to build robust machine learning models effortlessly. 
• Explainability (ml_explain): Uncover the mysteries of model explainability using the XWiN methodology. Gain insights into your models, making your predictions more transparent and trustworthy.
• Monitoring (ml_drift): Detect and measure drift in your models with ease. Keeping your models up-to-date and accurate is crucial. This module simplifies the process by enabling you to decide when to trigger a new rebasing or retraining process. 
• Distribution Fitting (distribution_fitting): Distribution fitting and normality testing is useful, and at times, even a critical process across numerous industries. This sub-module aims to simplify the distribution fitting and normality testing processes with functions that can be applied to full datasets, rather than working with one column at a time. 

Details

This release consists of data functions and Spotfire examples for sub-modules and the documentation of the Python functions (as html files). The Spotfire examples (dxp) show how these functions can be called through the library via data functions (sfd) in Spotfire. After clicking the "Download" button, you can select the module/topic you would like to download. Please note, that some examples are available in the recent release 2.0.0 and some in the previous release (previous releases are available in "Download Previous Releases" section).  More details about specific modules and its features can be found in this community article.