Visualizing Ice Core Datasets in Python Using a Jupyter Notebook Template
Contents
Visualizing Ice Core Datasets in Python Using a Jupyter Notebook Template#
Abstract#
Chemical constituents measured in ice cores are often visualized in a standard format, with figures produced by proprietary or closed tools. Such tools present an extra step that requires a context switch in the process of analyzing and visualizing results. Python and its many machine learning, numerical computation, and visualization libraries together with Jupyter notebooks offer a unified alternative that permits researchers to analyze and visualize ice core datasets in a single, highly-integrated ecosystem. This Jupyter notebook uses exclusively open-source Python libraries (specifically NumPy, Matplotlib, and Pandas) to create a readily reproducible, publication quality figure. We demonstrate the utility of the notebook as a template for ice core researchers engaging in open science by recreating multiple figures published by Schupbach et al. in Nature Communications, 16 April 2018. This notebook provides a step-by-step guide to systematically recreate two figures using Schupbach’s original data.
Local Installation#
This notebook may be run on a local machine under Python (>=3.8) with the following minimal packages:
Library |
Min Version |
Description |
|---|---|---|
NumPy |
1.17.4 |
Efficient, multi-dimensional array operations |
Pandas |
0.25.3 |
Data preparation and cleaning; SQL-like data manipulation |
Matplotlib |
3.1.2 |
Python plotting library with low-level control |
Seaborn |
0.10.0 |
Python visualization |
OpenPyXL |
3.0.3 |
Python library to read/write Excel files |