AECData Python Library Step 3— Visualizations & Statistical Analysis

Welcome to the third tutorial on using the open-source AECdata library provided by 2050 Materials.

In this tutorial, we’ll learn how to plot visualizations and derive statistics from your data. This guide will cover grouping data by category and location, removing outliers, and calculating median values and quartiles. Plus, we’ll show how to create a distribution plot.

Setting Up Your Environment

Before diving into the statistics and plots, ensure you’ve imported the necessary classes from the aecdata library:

Start by creating an instance of the ProductStatistics class. This class extends the functionalities of the ProductData class, allowing for advanced data analysis.

One of the powerful features of the ProductStatistics class is its ability to group and filter data efficiently. Here’s how you can do it:

This code groups the data by country and material type, which is particularly useful for regional analysis and comparisons between different materials.

Visualizations can help understand the distribution of data. Let’s plot a histogram and a boxplot:

These plots will provide visual insights into the distribution and variance of the impact factors across different material types.

You’re now set up with aecdata and have used theProductStatistics class, to perform detailed statistical analysis and visualizations.

This tutorial covered grouping data, removing outliers, and visualizing distributions, which are crucial for making informed decisions based on your data.

Stay tuned for our next tutorial, where we’ll go over how to implement aecdata within a data-science environment!

Stay tuned, and happy coding!

This library is provided by 2050 Materials, a company dedicated to unlocking the value of data in the construction industry to enable the climate transition.

If you are interested in embedding this data within your workflows, or have a specific problem, reach out to us at api@2050-materials.com