Abstract
This post concludes the "Working with CGM Data" series by demonstrating how to create insightful visualizations in Tableau using continuous glucose monitor (CGM) data. It covers two visualization categories—CGM-style and data analysis-style—and highlights their unique features and applications. Advanced Tableau techniques, including calculated fields and dynamic parameters, ensure precise and actionable insights. This guide demonstrates how to translate raw CGM data into actionable visual reports, creating a framework for analyzing glucose management trends.
Key Points
Read more: Working with CGM Data: Part 4 – Visualizing the Data in Tableau
# Import packages
# For data manipulation
import numpy as np
import pandas as pd
# For working with datetime objects
from datetime import datetime
# For working with SQLite databases
import sqlite3Abstract
Establishing a reliable and efficient process for managing continuous glucose monitor (CGM) data ensures the dataset remains accurate, consistent, and manageable. Using Python and SQLite, new data is cleaned, validated , and added to the database, and prepped for visualizations in Tableau.
Key Points
Read more: Working with CGM Data: Part 3 - Cleaning and Processing New Data with Python and SQLite
# Import packages
# For data manipulation
import pandas as pd
# For working with SQLite databases
import sqlite3Abstract
Explore the use of Python, SQLite, and SQL Magic to manage growing datasets efficiently.
Key Points
# Import packages
# For data manipulation
import numpy as np
import pandas as pd
# for displaying and modifying the working directory
import os as os
# For working with datetime objects
from datetime import datetimeAbstract
Managing continuous glucose monitor (CGM) data efficiently becomes increasingly challenging as datasets grow larger. This post details a streamlined approach for preparing a base dataset to facilitate CGM data analysis. Key steps include optimizing the data download process, filtering relevant information, formatting and enriching the dataset, and preparing it for visualization. By implementing these methods, you can reduce redundancy, ensure data integrity, and create a foundation for meaningful analysis and visualization.
Key Points
Read more: Working with CGM Data: Part 1 – Building the Base Dataset with Python
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