plt.legend position

Plt.legend position

Matplotlib is the package used for data visualization and is one of the most popular packages in python. Each pyplot function takes is used to make some changes to a figure e. A legend is used to describe elements for a plt.legend position area of a graph, plt.legend position. Python has a function called legend which is used to place a legend on the axis.

Matplotlib is a powerful Python library used for data visualization. It allows you to create various types of plots, such as line plots, scatter plots, bar plots, and more. One key feature of Matplotlib is the ability to add a legend to your plot. A legend provides important information about the data being visualized, such as labels for each line or marker in the plot. In some cases, you may want to adjust the position of the legend to avoid overlapping with your data or to improve the overall aesthetic of your plot. Fortunately, Matplotlib provides several options for moving the legend around. In this tutorial, we will explore how to move the Matplotlib legend using different methods.

Plt.legend position

When you're creating a chart or graph, a legend is an essential tool that helps readers understand the data being presented. Think of it as a map key that explains the symbols, colors, or line styles used in your chart. In Matplotlib, which is a popular plotting library for Python, adding a legend is straightforward, but you might want to move it around to make your graph clearer or more aesthetically pleasing. Before we dive into moving the legend, let's start by creating a simple plot and adding a legend to it. Here's a basic example:. In this code, we're plotting a simple line graph with x as our horizontal axis and y as our vertical axis. When we call plt. Now, let's talk about moving the legend. By default, Matplotlib tries to find the best location for the legend that doesn't overlap with the data. However, you might want to place it somewhere specific. You can do this by passing the loc argument to the plt.

Notice how we used ax.

The elements to be added to the legend are automatically determined, when you do not pass in any extra arguments. In this case, the labels are taken from the artist. A string starting with an underscore is the default label for all artists, so calling Axes. For full control of which artists have a legend entry, it is possible to pass an iterable of legend artists followed by an iterable of legend labels respectively:. This call signature is discouraged, because the relation between plot elements and labels is only implicit by their order and can easily be mixed up.

This legend guide is an extension of the documentation available at legend - please ensure you are familiar with contents of that documentation before proceeding with this guide. Calling legend with no arguments automatically fetches the legend handles and their associated labels. This functionality is equivalent to:. Proxy artists for further details. For full control of what is being added to the legend, it is common to pass the appropriate handles directly to legend :. In some cases, it is not possible to set the label of the handle, so it is possible to pass through the list of labels to legend :. Not all handles can be turned into legend entries automatically, so it is often necessary to create an artist which can. Suppose we wanted to create a legend which has an entry for some data which is represented by a red color:. Source code , png , pdf. There are many supported legend handles, instead of creating a patch of color we could have created a line with a marker:.

Plt.legend position

In data visualization, often create complex graphs that need to have legends for the reader to be able to interpret the graph. But what if those legends get in the way of the actual data that they need to see? In this Byte, we'll see how you can move the legend so that it's outside of the plot in Matplotlib. In Matplotlib, legends provide a mapping of labels to the elements of the plot. These can be very important to help the reader understand the visualization they're looking at. Without the legend, you might not know which line represented which data!

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Errorbar limit selection Errorbar limit selection. Each of these arguments adds a bit of style to your legend, making your plot more appealing and easier to understand. The default value of attribute loc is upper left. Defaults to axes. Step Demo Step Demo. This legend guide extends the legend docstring - please read it before proceeding with this guide. Interested in learning more? Maximize your earnings for your published articles in Dev Scripter ! No need to complete complicated processing with Python coding anymore, simply import your data, and drag and drop variables to create all kinds of data visualizations! BoundaryNorm matplotlib.

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Enhance the article with your expertise. Suggest Changes. What kind of Experience do you want to share? It is part of the SciPy ecosystem and works seamlessly with other libraries such as NumPy and Pandas. Machine Learning , Tutorials. If an entry contains a tuple, then the legend handler for all Artists in the tuple will be placed alongside a single label. If None, use rcParams["text. A few more attributes of legend function are: fontsize : This is used to denote the font of the legend. Colormap matplotlib. Matplotlib may be a multi-platform data visualization library built on NumPy arrays and designed to figure with the broader SciPy stack. There are numerous approaches to position your legend outside the Matplotlib plot box. Contourf and log color scale Contourf and log color scale. You can suggest the changes for now and it will be under the article's discussion tab.

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