A vast array of changes in Earth’s climate are taking place as we continue to elevate the levels of greenhouse gases in the atmosphere through fossil fuel burning and other anthropogenic activities. In this post, I will use LOSCAR and Python to predict the possible atmospheric CO2 level and global temperature in the future (until 2500).
Compared with seaborn, plotly and cufflinks make plots interactive. However, some plotting styles (especially statistical analysis) are more handy in seaborn. This post will dive a little deeper into these plotting packages.
Through using the basemap toolkit and the NetCDF data format, we can visualize the oceanography data quickly!
This is based on a project from one of my Udemy courses. In this project, I will focus on exploratory data analysis of stock prices, which is only meant to practice the skills of data collection, data manipulation and data analysis using pandas and plotly.
In a previous post, I drew this map using the basemap toolkit. It seems easier and can be interactive when plotted using plotly.