Note that you need to have Numpy installed for Matplotlib to work. Import Matplotlib and Numpy.įirst import Matplotlib and Matplotlib's pyplot. The scripting layer is considered a lighter interface to simplify common tasks and for quick and easy generation of graphics and plots. Matplotlib's architecture is composed of three main layers: the back-end layer, the artist layer where much of the heavy lifting happens, and the scripting layer. You can generate plots, histograms, box plots, bar charts, line plots, scatterplots, etc., with just a few lines of code. Matplotlib tries to make basic things easy and hard things possible. Matplotlib is one of the most widely used, if not the most popular data visualization libraries in Python. Let's learn first about Matplotlib and its architecture. In other words, any feature or design you include in your plot to make it more attractive or pleasing should support the message that the plot is meant to get across and not distract from it. Fundamental design principalsĪll great visuals follow three key principles: less is more, attract attention, and have impact. Here is a beginners guide to data visualisation using Matplotlib from a Pandas dataframe. Visuals such as plots and graphs can be very effective in clearly explaining data to various audiences. This can be especially useful when trying to explore the data and get acquainted with it. In a nutshell data visualization is a way to show complex data in a form that is graphical and easy to understand. Data Visualization is a big part of data analysis and data science.
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