![]() In case you have further questions, you may leave a comment below.Import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df = pd. This post has shown how to add a legend to plots in Python Matplotlib and seaborn. In the video, we explain in some more detail how to add a legend to plots in Python Matplotlib and seaborn.įurthermore, you could have a look at some other tutorials on Statistics Globe: But when fine-tuning the position, you must bear in mind that the figure will have extra blank space on the right: g sns.displot( penguins, x'billlengthmm', hue'species', col'island', colwrap2, height3, ) sns.movelegend(g, 'upper left', bboxtoanchor(.55. Lastly, plt.show() is used to display the plot figure.ĭo you need more explanations on how to add a legend to a plot in Python Matplotlib and seaborn? Then you should have a look at the following YouTube video of the Statistics Globe YouTube channel. It’s also possible to move the legend created by a figure-level function. ![]() The result is a legend displayed on the top left corner of the plot, which is the default position. Next, we introduced the plt.legend() function wherein we parsed an array of weekdays to the labels = argument and defined the legend title as well. To the data parameter, we’re passing the name of the DataFrame, normdata. Those can be passed to the call to legend. It will automatically try to determine a useful number of legend entries to be shown and return a tuple of handles and labels. The plots it produces are often called lattice, trellis, or small-multiple graphics. Automated legend creation Another option for creating a legend for a scatter is to use the PathCollection.legendelements method. This class maps a dataset onto multiple axes arrayed in a grid of rows and columns that correspond to levels of variables in the dataset. Inside of the parenthesis, we’re providing arguments to three parameters: data, x, and y. Initialize the matplotlib figure and FacetGrid object. To do this, we’ll call the sns.scatterplot () function. That argument colored the points by the day of the week to which they belong. First, let’s just create a simple scatterplot. In the sns.scatterplot() function, we defined a new argument hue = to which we parsed day. scatterplot (df ,x = "total_bill", y = "tip", hue = "day" ) However, I cant then call to this legend object to change the number of columns, e.g., with: l ers. ![]() Therefore, run the code below to load the dataset: I can access the legend object that is created by calling ers.legend and this returns an object with type Legend (basically, a matplotlib object). We will make use of the tips dataset that comes pre-loaded in seaborn. In this example, we are going to build a simple scatter plot using the seaborn library.įirst, though, we will need to load the dataset that we will visualize in a scatter plot. The legend position can be changed, however, that is not the focus of this tutorial.Įxample 3: Build Simple Scatter Plot in seaborn ![]() Next, we introduced a new function plt.legend(), which displays the legend on the plot in the top left corner as the default position. If there are a large number of unique numeric values, the legend will show a representative, evenly-spaced set: tiprate tips. In the plt.plot() function, we defined a new argument label = where we parsed the legend label for each of the two lines plotted on the graph. The only difference is that, here, we have added a legend to the plot. The above plot is the same as the one created in the previous example.
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