The simplest example would be to plot a list of values. I will use 'fivethirtyeight' ('fivethirtyeight') 1.1) The simplest plot Matplotlib offers several cool style sheets to enhance the figure aesthetics. If your screen supports retina display, you can enhance the resolution of the figure by using %config InlineBackend.figure_format = ‘retina’ To enable the inline plotting in Jupyter Notebooks, you need to use the following at the beginning of the Notebook %matplotlib inline The tutorial is expected to work on older versions, too. Following system settings are used: Python version: 3.7.7, Matplotlib version: 3.1.3. The whole Notebook can be downloaded/forked from my GitHub repository. This first post of mine will teach you the essential basics of matplotlib, taking the use case of one-dimensional plots, together with basic bar and pie charts.
Pylab module download 3.7 series#
Being a gold badge holder of matplotlib on Stack Overflow, I thought of creating a series of tutorials aimed at teaching the basic know-hows of using python & matplotlib for plotting eye-catchy figures, providing ready-to-use, Jupyter Notebooks. Matplotlib is one such powerful plotting library for python that offers a varied spectrum of 1D, 2D, and 3D visualizations. Python being one of the most sought after programming languages today (in the active top 3 on Stack Overflow), offers tremendous possibilities of data visualization. Break the ice with matplotlib during a 10-minute coffee break!ĭata visualization is an essential, routine task in any field regardless of how small or large the data is, then be it plotting a simple sinusoidal curve or creating sophisticated, interactive dashboards for stakeholders.
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