![]() What you can’t see on the surface is that you have -maybe unconsciously- made use of the built-in defaults that take care of the creation of the underlying components, such as the Figure and the Axes. Note that you import the pyplot module of the matplotlib library under the alias plt.Ĭongrats, you have now successfully created your first plot! Now let’s take a look at the resulting plot in a little bit more detail: Look at this example to see how easy it really is:ĮyJsYW5ndWFnZSI6InB5dGhvbiIsInNhbXBsZSI6IiMgSW1wb3J0IHRoZSBuZWNlc3NhcnkgcGFja2FnZXMgYW5kIG1vZHVsZXNcbmltcG9ydCBtYXRwbG90bGliLnB5cGxvdCBhcyBwbHRcbmltcG9ydCBudW1weSBhcyBucFxuXG4jIFByZXBhcmUgdGhlIGRhdGFcbnggPSBucC5saW5zcGFjZSgwLCAxMCwgMTAwKVxuXG4jIFBsb3QgdGhlIGRhdGFcbnBsdC5wbG90KHgsIHgsIGxhYmVsPSdsaW5lYXInKVxuXG4jIEFkZCBhIGxlZ2VuZFxucGx0LmxlZ2VuZCgpXG5cbiMgU2hvdyB0aGUgcGxvdFxucGx0LnNob3coKSJ9 As such, you don’t need much to get started: you need to make the necessary imports, prepare some data, and you can start plotting with the help of the plot() function! When you’re ready, don’t forget to show your plot using the show() function. Luckily, this library is very flexible and has a lot of handy, built-in defaults that will help you out tremendously. You’ll probably agree with me that it’s confusing and sometimes even discouraging seeing the amount of code that is necessary for some plots, not knowing where to start yourself and which components you should use. (To practice matplotlib interactively, try the free Matplotlib chapter at the start of this Intermediate Python course or see DataCamp’s Viewing 3D Volumetric Data With Matplotlib tutorial to learn how to work with matplotlib’s event handler API.) What does a Matplotlib Python Plot Look Like?Īt first sight, it will seem that there are quite some components to consider when you start plotting with this Python data visualization library. Lastly, you’ll briefly cover two ways in which you can customize Matplotlib: with style sheets and the rc settings.Showing, saving and closing your plots: show the plot, save one or more figures to, for example, pdf files, clear the axes, clear the figure or close the plot, etc.Basic plot customizations, with a focus on plot legends and text, titles, axes labels and plot layout.Plotting routines, from simple ways to plot your data to more advanced ways of visualizing your data.Plot creation, which could raise questions about what module you exactly need to import (pylab or pyplot?), how you exactly should go about initializing the figure and the Axes of your plot, how to use matplotlib in Jupyter notebooks, etc.The anatomy of a Matplotlib plot: what is a subplot? What are the Axes? What exactly is a figure?. ![]() When you’re working with the Python plotting library Matplotlib, the first step to answering the above questions is by building up knowledge on topics like: However, the step to presenting analyses, results or insights can be a bottleneck: you might not even know where to start or you might have already a right format in mind, but then questions like “Is this the right way to visualize the insights that I want to bring to my audience?” will have definitely come across your mind. Humans are very visual creatures: we understand things better when we see things visualized.
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