'Interpretation of Visualizations of Soil Data and Weather APIs' by get_ambee weatherapi weather
Graphical visualizations that help stakeholders understand weather data are vital to any data service provider and decision-makers. The process of obtaining accurate, hyperlocal weather data and presenting it in understandable media is perceived to be complex and technical. This blog is mainly constructed to understand some visual plots and charts and methods to use data. It aims to help viewers understand and make informed decisions from the visualizations.
The agro-weather data used here is from a verified source, and the visualization holds relevance to that particular region. By understanding this process in detail, policymakers or stakeholders can avoid any naive approach to visualizing the datasets, misinterpretation, and mistrust. I prefer to use Python libraries to create visualizations as it has an array of inbuilt libraries. The code in this blog is written and run on a Jupyter notebook. All you need to do is download the Jupyter notebook on your system and download the python libraries.Another environment that I use to run visualization codes/scripts is Google Colab. This provides an online environment linked to your Google account and uses Google Servers, so there is no need to use local systems resources.
Correlation denotes how linearly related one feature is to another. In layman's terms, it shows how one feature varies based on a variation of another feature.A higher value denotes more correlation between the features.‘annot’ is a parameter passed to display the correlation values in the plot when made True.Line plots are simple, intuitive visuals that display numerical values on one side and categorical values on the other.