["python","matplotlib","legend"]
creating over 20 unique legend colors using matplotlib

I am plotting 20 different lines on a single plot using matplotlib. I use a for loop for plotting and label every line with its key and then use the legend function for key in dict.keys(): plot(x,dict[key], label = key) graph.legend() But using this way, the graph repeats a lot of colors in the legend. Is there any way to ensure a unique color is assigned to each line using matplotlib and over 20 lines? thanks
The answer to your question is related to two other SO questions. The answer to How to pick a new color for each plotted line within a figure in matplotlib? explains how to define the default list of colors that is cycled through to pick the next color to plot. This is done with the Axes.set_color_cycle method. You want to get the correct list of colors though, and this is most easily done using a color map, as is explained in the answer to this question: Create a color generator from given colormap in matplotlib. There a color map takes a value from 0 to 1 and returns a color. So for your 20 lines, you want to cycle from 0 to 1 in steps of 1/20. Specifically you want to cycle form 0 to 19/20, because 1 maps back to 0. This is done in this example: import matplotlib.pyplot as plt import numpy as np NUM_COLORS = 20 cm = plt.get_cmap('gist_rainbow') fig = plt.figure() ax = fig.add_subplot(111) ax.set_prop_cycle(color=[cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]) for i in range(NUM_COLORS): ax.plot(np.arange(10)*(i+1)) fig.savefig('moreColors.png') plt.show() This is the resulting figure: Alternative, better (debatable) solution There is an alternative way that uses a ScalarMappable object to convert a range of values to colors. The advantage of this method is that you can use a non-linear Normalization to convert from line index to actual color. The following code produces the same exact result: import matplotlib.pyplot as plt import matplotlib.cm as mplcm import matplotlib.colors as colors import numpy as np NUM_COLORS = 20 cm = plt.get_cmap('gist_rainbow') cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1) scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm) fig = plt.figure() ax = fig.add_subplot(111) # old way: #ax.set_prop_cycle(color=[cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]) # new way: ax.set_prop_cycle(color=[scalarMap.to_rgba(i) for i in range(NUM_COLORS)]) for i in range(NUM_COLORS): ax.plot(np.arange(10)*(i+1)) fig.savefig('moreColors.png') plt.show()