Interactive Notebooks and Examples - moj-analytical-services/splink_demos

Interactive Notebooks and Examples

Interactive notebooks are web-based interactive computing environments that allow users to create and share documents containing live code, equations, visualizations, and narrative text. They are widely used in data science, machine learning, and scientific computing for exploratory analysis, visualization, and education. In this document, we will explore the use of interactive notebooks and examples in the context of the splink_demos project.

Key Technologies and Dependencies

The splink_demos project uses several key technologies and dependencies, including:

• Jupyter Notebook and JupyterLab
• ipywidgets
• nbmake
• pytest
• pyspark
• splink
• pyarrow
• scikit-learn
• duckdb
• Binder and GitHub Pages

These technologies provide a rich and interactive environment for data analysis, visualization, and machine learning.

Jupyter Notebook and JupyterLab

Jupyter Notebook and JupyterLab are open-source web applications for creating and sharing documents that contain live code, equations, visualizations, and narrative text. Jupyter Notebook is the original notebook interface, while JupyterLab is a more recent and powerful interface that provides a full-featured coding environment, including a text editor, terminal, and file browser.

The splink_demos project includes several Jupyter Notebooks that demonstrate various aspects of the splink library, including:

• Exploratory analysis
• Blocking
• Visualizing predictions
• Real-time record linkage

These notebooks provide a hands-on and interactive way to learn about the splink library and its capabilities.

ipywidgets

ipywidgets is a Python library that provides interactive HTML widgets for Jupyter Notebooks and JupyterLab. These widgets allow users to interact with the notebook in real-time, providing a more dynamic and engaging experience.

The splink_demos project includes several notebooks that use ipywidgets to provide interactive interfaces for data analysis and visualization. For example, the cluster_studio.html notebook provides an interactive interface for exploring and visualizing clusters of records.

nbmake

nbmake is a Python library that provides a command-line interface for building and deploying Jupyter Notebooks. It allows users to build notebooks that can be deployed as standalone HTML pages or as part of a larger web application.

The splink_demos project includes an nbmake.cfg file that defines the build process for the notebooks. This file specifies the notebooks to be built, the output format, and any additional build options.

pytest

pytest is a popular Python testing framework that provides a simple and powerful way to write and run automated tests. The splink_demos project includes several pytest tests that validate the behavior of the splink library.

pyspark

pyspark is a Python library for working with Apache Spark, a distributed computing system for large-scale data processing. The splink_demos project includes several notebooks that demonstrate the use of pyspark with splink for large-scale record linkage.

splink

splink is a Python library for probabilistic record linkage, a technique for identifying and linking records that refer to the same entity across multiple data sources. The splink_demos project includes several notebooks that demonstrate the use of splink for record linkage, including:

• Exploratory analysis
• Blocking
• Visualizing predictions
• Real-time record linkage

pyarrow

pyarrow is a Python library for working with Apache Arrow, a columnar in-memory data format for efficient data interchange between systems. The splink_demos project includes several notebooks that demonstrate the use of pyarrow for efficient data serialization and deserialization.

scikit-learn

scikit-learn is a popular Python library for machine learning. The splink_demos project includes several notebooks that demonstrate the use of scikit-learn for machine learning tasks, including:

• Clustering
• Classification
• Regression

duckdb

duckdb is an in-memory analytical database written in C++. The splink_demos project includes several notebooks that demonstrate the use of duckdb for fast and efficient data analysis.

Binder and GitHub Pages

Binder and GitHub Pages are web-based services that allow users to deploy and share Jupyter Notebooks and other web-based applications. The splink_demos project includes a binder configuration file that defines the dependencies and environment for the notebooks. This file can be used to launch an interactive environment for the notebooks using Binder.

The splink_demos project also includes a GitHub Pages configuration file that defines the build process for deploying the notebooks as standalone HTML pages. This allows users to share the notebooks with others, even if they do not have a Jupyter environment installed.

Conclusion

Interactive notebooks and examples provide a powerful and engaging way to learn about data analysis, visualization, and machine learning. The splink_demos project provides several notebooks and examples that demonstrate the use of splink and related technologies for record linkage, data analysis, and machine learning. By using Jupyter Notebooks and JupyterLab, ipywidgets, nbmake, pytest, pyspark, splink, pyarrow, scikit-learn, duckdb, Binder, and GitHub Pages, users can learn about these technologies in an interactive and hands-on way.