are considered y-o-y basis, generally ‘0’ is used as an objective benchmark. When profitability, growth, improvement etc. This splits the axis into four quadrants. Benchmark The threshold limit for the X and Y-axes can be defined as the Benchmark. Life Expectancy at Birth X-axis represents the Gross National Income (GNI) per Capita and Y-axis represents the Life Expectancy at Birth, the data will be plotted to the appropriate quadrant based on these two metrics. Gross National Income (GNI) per Capita ii. We will perform a quadrant analysis on how the G-20 nations are performing with respect to two KPIs: i. Axis The parameters(KPIs) the data is plotted in the graph based on are defined in the X and Y-axes. There are three components for a Quadrant Analysis Chart: gni_pc: Gross National Income (GNI) per Capita Components of Quadrant Analysis Our data set consists of the following data: i. We will use some KPIs from Human Development Index published by the United Nations Development Program, the full kaggle dataset can be accessed here. However the process is not so straight-forward in python.
Preparation of Quadrant Analysis in data visualization tools like Power BI and Tableau are very straight forward. After identifying which quadrant the entity belongs to, actions can be taken to improve performance under relevant KPIs. Depending on how an entity performs under either of the KPIs, the entity is grouped into either of the quadrants. In a quadrant analysis, performance under two parameters are assessed for each entity. A Quadrant chart is technically a scatter plot that is divided into four sections or quadrants, hence the name. What is Quadrant Analysis?Ī Quadrant Analysis chart is a very common tool used for decision making especially in business setting.
#Quadrants of a graph how to#
Step by step guide on how to do it in python using pandas, matplotlib and seaborn libraries. Explaining what is Quadrant Analysis and where is it employed.