![]() ![]() The Axes is the area on which the data is plotted with functions such as plot() and scatter() and that can have ticks, labels, etc. You’ll also find that you can add a legend and color bar, for example, to your Figure. A Figure can have several other things in it, such as a suptitle, which is a centered title to the figure. You can create multiple independent Figures. It’s the top-level component of all the ones that you will consider in the following points. The Figure is the overall window or page that everything is drawn on. ![]() ![]() In essence, there are two big components that you need to take into account: Or, in other words, what the anatomy of a matplotlib plot looks like: You’ll read more about these defaults in the section that deals with the differences between pylab and pyplot.įor now, you’ll understand that working with matplotlib will already become a lot easier when you understand how the underlying components are instantiated. 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. ![]()
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