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You can do this either by using the label= keyword in each of your plt.plot() calls or by assigning your labels as a tuple or list within legend, as in this working example: import numpy as np import matplotlib.pyplot as plt x = np.linspace(-0.75,1,100) y0 = np.exp(2 + 3*x - 7*x**3) y1 = 7-4*np.sin(4*x) plt.plot(x,y0,x,y1) plt.gca().legend(('y0 ...
1 Αυγ 2024 · To create a legend in Matplotlib, you can use the legend() function after plotting your data. This function adds a legend to your plot, which helps to identify different data series. import matplotlib.pyplot as plt # Plot data plt.plot([1, 2, 3], [4, 5, 6], label='Line 1') plt.plot([1, 2, 3], [6, 5, 4], label='Line 2') # Create a legend plt ...
To make a legend for all artists on an Axes, call this function with an iterable of strings, one for each legend item. For example: ax.plot([1, 2, 3]) ax.plot([5, 6, 7]) ax.legend(['First line', 'Second line']) Parameters: handleslist of (Artist or tuple of Artist), optional. A list of Artists (lines, patches) to be added to the legend.
29 Ιουλ 2016 · This code allow to show all desired border lines of a complex legend: import matplotlib.pylab as plt import numpy as np plt.close('all') # test data N = 25 y = np.random.randn(N) x = np.arange(N) y2 = np.random.randn(25) # serie A p1a, = plt.plot(x, y, "ro", ms=10, mfc="r", mew=2, mec="r") p1b, = plt.plot(x[:5], y[:5], "w+", ms=10, mec="w", mew ...
Plot legends give meaning to a visualization, assigning meaning to the various plot elements. We previously saw how to create a simple legend; here we'll take a look at customizing the placement and aesthetics of the legend in Matplotlib.
from matplotlib.lines import Line2D from matplotlib.patches import Patch legend_elements = [Line2D ([0], [0], color = 'b', lw = 4, label = 'Line'), Line2D ([0], [0], marker = 'o', color = 'w', label = 'Scatter', markerfacecolor = 'g', markersize = 15), Patch (facecolor = 'orange', edgecolor = 'r', label = 'Color Patch')] # Create the figure fig ...
Understanding how to position legends, whether inside or outside a chart, can enhance data interpretation. This article offers a comprehensive guide on leveraging the legend() function in matplotlib for enhancing your data visualizations.
