How to build Matplotlib charts that people actually want to read and use. Data science is a lot about convincing people, showing what you have found, what patterns you actually understood. Charting comes a long way in this exercise.
Data science is a lot about convincing people, showing what you have found, what patterns you actually understood. Charting comes a long way in this exercise. Sadly many scientific libraries allow you to produce charts out of the box such as the one below.
Are your eyes drawn to the chart with a sense of curiosity? Personally, mine are not and more importantly, I think the non-technical, general public will dismiss it rather fast as you have to make a conscious effort to go beyond the visual lack of clarity.
Most will dismiss the importance of aesthetics, but I really think it is one of the connecting bridges between “tech” and “business” people.
It helps to better communicate.
Charts are about what you want to show, what is the most important information that a reader should extract? Is it the axis? You can hardly miss the dark frame and lines. If you want to read the bar value, it is stacked between the error bar lines. The important information is hidden.
In this post, I am taking the example of bar plots pretending that we measured some score in different groups of people. The required code is shared at the end of the post.
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Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.