#A list of hex colours running between blue and purple CB91_Grad_BP = Īn example of a Seaborn heatmap figure, which can take a list of hex colours as its cmap argument. Here’s an example of a gradient running between two of my brand’s colours, and how they look on a matplotlib chart. Creating such a long list manually can be a pain - I instead suggest generating one automatically using a site like colordesigner.io (simply choose the colours that you want the gradient to transition between, max out the number of gradient steps, then extract the hex codes from the resulting HTML).Įdit: Peter Cahill has written a Python function that will generate gradient lists with n steps between two colour hexes- see the responses to this blog, below. Of course, to make these gradients nice and smooth, you need lots of defined hex values in that list (at least 40 in my experience). You can also define gradients using lists of hex colours, which can then be used pretty much anywhere where viridis is a valid entry (e.g. It will now use ‘CB91_Blue’ as the default chart colour for all plots in the notebook AND cycle through the colour list in order when creating multi-category plots. color_list = plt.rcParams = plt.cycler(color=color_list) We can put these defined colours into a list, and then pass this list into Matplotlib’s colour ‘cycler’.
#Background image cycler code#
Also, I can confirm that as a Brit, switching indiscriminately between ‘color’ and ‘colour’ has resulted in some deeply annoying code errors in my time. Colormind.io is especially cool - it uses deep learning to learn colour styles from photographs, movies, and popular art, and hence recommend hues and shades that go well together. Note - if you want to create your own colour palette, there are some useful tools online that can help. We can set these as variables, by passing their hexes as strings: CB91_Blue = '#2CBDFE' CB91_Green = '#47DBCD' CB91_Pink = '#F3A0F2' CB91_Purple = '#9D2EC5' CB91_Violet = '#661D98' CB91_Amber = '#F5B14C'
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