This article reviews the most reliable data on climate change and transforms it into information through a wide and varied set of visualizations created using Python. I utilized matplotlib and seaborn, along with supporting libraries such as pandas, numpy, and pycountry, to clean and prepare the data for analysis.
Throughout the article, I honed my storytelling and data communication skills, making it accessible for readers with no prior knowledge to fully grasp the fundamentals of climate change, including its origins and its impact on our world, preparing them for future discussions and enabling them to form their own conclusions. It also equips them with a solid foundation to evaluate proposed policies for addressing this global challenge.
Thanks to this approach, we were able to find out how human-produced greenhouse gases (GHGs) like CO2, CH4, and N2O are primary drivers of global warming. Utilizing data from sources such as Our World in Data and the IPCC’s Sixth Assessment Report, I analyzed emissions by country and per capita, highlighting the complexity of attributing responsibility. The study also identifies key emission sources, advocating for innovative methods to reduce emissions. Despite certain limitations, the findings were able to get climate change explained and underscored the collective responsibility in addressing this global challenge.
Read the Full Article in PDF
Review the Code (Available on my GitHub Profile)
Download the dataframes here: Dataframes | Visualizing Climate Change
