Specifying colors#
Matplotlib recognizes the following formats to specify a color.
RGB or RGBA (red, green, blue, alpha) tuple of float values in a closed interval [0, 1].
Case-insensitive hex RGB or RGBA string.
Case-insensitive RGB or RGBA string equivalent hex shorthand of duplicated characters.
String representation of float value in closed interval [0, 1] for grayscale values.
Single character shorthand notation for some basic colors.
The colors green, cyan, magenta, and yellow do not coincide with X11/CSS4 colors. Their particular shades were chosen for better visibility of colored lines against typical backgrounds.
- ‘b’ as blue
- ‘g’ as green
- ‘r’ as red
- ‘c’ as cyan
- ‘m’ as magenta
- ‘y’ as yellow
- ‘k’ as black
- ‘w’ as white
Case-insensitive X11/CSS4 color name with no spaces.
Case-insensitive color name from xkcd color survey with ‘xkcd:’ prefix.
Case-insensitive Tableau Colors from ‘T10’ categorical palette.
This is the default color cycle.
- ‘tab:blue’
- ‘tab:orange’
- ‘tab:green’
- ‘tab:red’
- ‘tab:purple’
- ‘tab:brown’
- ‘tab:pink’
- ‘tab:gray’
- ‘tab:olive’
- ‘tab:cyan’
«CN» color spec where ‘C’ precedes a number acting as an index into the default property cycle.
Matplotlib indexes color at draw time and defaults to black if cycle does not include color.
rcParams[«axes.prop_cycle»] (default: cycler(‘color’, [‘#1f77b4’, ‘#ff7f0e’, ‘#2ca02c’, ‘#d62728’, ‘#9467bd’, ‘#8c564b’, ‘#e377c2’, ‘#7f7f7f’, ‘#bcbd22’, ‘#17becf’]) )
«Red», «Green», and «Blue» are the intensities of those colors. In combination, they represent the colorspace.
Transparency#
The alpha value of a color specifies its transparency, where 0 is fully transparent and 1 is fully opaque. When a color is semi-transparent, the background color will show through.
The alpha value determines the resulting color by blending the foreground color with the background color according to the formula
The following plot illustrates the effect of transparency.
import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import numpy as np fig, ax = plt.subplots(figsize=(6.5, 1.65), layout='constrained') ax.add_patch(Rectangle((-0.2, -0.35), 11.2, 0.7, color='C1', alpha=0.8)) for i, alpha in enumerate(np.linspace(0, 1, 11)): ax.add_patch(Rectangle((i, 0.05), 0.8, 0.6, alpha=alpha, zorder=0)) ax.text(i+0.4, 0.85, f"alpha:.1f>", ha='center') ax.add_patch(Rectangle((i, -0.05), 0.8, -0.6, alpha=alpha, zorder=2)) ax.set_xlim(-0.2, 13) ax.set_ylim(-1, 1) ax.set_title('alpha values') ax.text(11.3, 0.6, 'zorder=1', va='center', color='C0') ax.text(11.3, 0, 'zorder=2\nalpha=0.8', va='center', color='C1') ax.text(11.3, -0.6, 'zorder=3', va='center', color='C0') ax.axis('off')
The orange rectangle is semi-transparent with alpha = 0.8. The top row of blue squares is drawn below and the bottom row of blue squares is drawn on top of the orange rectangle.
See also Zorder Demo to learn more on the drawing order.
«CN» color selection#
Matplotlib converts «CN» colors to RGBA when drawing Artists. The Styling with cycler section contains additional information about controlling colors and style properties.
import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl th = np.linspace(0, 2*np.pi, 128) def demo(sty): mpl.style.use(sty) fig, ax = plt.subplots(figsize=(3, 3)) ax.set_title('style: '.format(sty), color='C0') ax.plot(th, np.cos(th), 'C1', label='C1') ax.plot(th, np.sin(th), 'C2', label='C2') ax.legend() demo('default') demo('seaborn-v0_8')
The first color ‘C0’ is the title. Each plot uses the second and third colors of each style’s rcParams[«axes.prop_cycle»] (default: cycler(‘color’, [‘#1f77b4’, ‘#ff7f0e’, ‘#2ca02c’, ‘#d62728’, ‘#9467bd’, ‘#8c564b’, ‘#e377c2’, ‘#7f7f7f’, ‘#bcbd22’, ‘#17becf’]) ). They are ‘C1’ and ‘C2’ , respectively.
Comparison between X11/CSS4 and xkcd colors#
95 out of the 148 X11/CSS4 color names also appear in the xkcd color survey. Almost all of them map to different color values in the X11/CSS4 and in the xkcd palette. Only ‘black’, ‘white’ and ‘cyan’ are identical.
For example, ‘blue’ maps to ‘#0000FF’ whereas ‘xkcd:blue’ maps to ‘#0343DF’ . Due to these name collisions, all xkcd colors have the ‘xkcd:’ prefix.
The visual below shows name collisions. Color names where color values agree are in bold.
import matplotlib.colors as mcolors import matplotlib.patches as mpatch overlap = name for name in mcolors.CSS4_COLORS if f'xkcd:name>' in mcolors.XKCD_COLORS> fig = plt.figure(figsize=[9, 5]) ax = fig.add_axes([0, 0, 1, 1]) n_groups = 3 n_rows = len(overlap) // n_groups + 1 for j, color_name in enumerate(sorted(overlap)): css4 = mcolors.CSS4_COLORS[color_name] xkcd = mcolors.XKCD_COLORS[f'xkcd:color_name>'].upper() # Pick text colour based on perceived luminance. rgba = mcolors.to_rgba_array([css4, xkcd]) luma = 0.299 * rgba[:, 0] + 0.587 * rgba[:, 1] + 0.114 * rgba[:, 2] css4_text_color = 'k' if luma[0] > 0.5 else 'w' xkcd_text_color = 'k' if luma[1] > 0.5 else 'w' col_shift = (j // n_rows) * 3 y_pos = j % n_rows text_args = dict(fontsize=10, weight='bold' if css4 == xkcd else None) ax.add_patch(mpatch.Rectangle((0 + col_shift, y_pos), 1, 1, color=css4)) ax.add_patch(mpatch.Rectangle((1 + col_shift, y_pos), 1, 1, color=xkcd)) ax.text(0.5 + col_shift, y_pos + .7, css4, color=css4_text_color, ha='center', **text_args) ax.text(1.5 + col_shift, y_pos + .7, xkcd, color=xkcd_text_color, ha='center', **text_args) ax.text(2 + col_shift, y_pos + .7, f' color_name>', **text_args) for g in range(n_groups): ax.hlines(range(n_rows), 3*g, 3*g + 2.8, color='0.7', linewidth=1) ax.text(0.5 + 3*g, -0.3, 'X11/CSS4', ha='center') ax.text(1.5 + 3*g, -0.3, 'xkcd', ha='center') ax.set_xlim(0, 3 * n_groups) ax.set_ylim(n_rows, -1) ax.axis('off') plt.show()
Total running time of the script: ( 0 minutes 2.179 seconds)
Colors with Python
Color is a big component of visuals. It also caters our visual cortex which evolved after millions of years to process colors at conscious and subconscious levels.
Whether you are a web designer, scientist, consultant, financier, student or researcher colors you use make a huge impact in the outcomes of your work.
We created this color manipulation guide that goes through the usage of colormaps and hex color collections in detail.
You can add _r to any builtin colormap to get the reversed colormap as above.
1- cmap, color, color_palette
We will use a few very useful Python functions and parameters in this tutorial. Ahead of the examples let’s take a quick look at a few of them.
cmap: This parameter is available in some Python charting functions. It takes a Python Colormap and applies it to the whole graph which can be very convenient for quick and beautiful coloring results.
color: This parameter is used when a few items or a single item needs to be colored such as a title, axis label, text label, bar or scatter point. Some objects might require the usage of colors parameter instead.
color_palette: This is a great function from seaborn library. It can be used to create color palettes and individual colors from Python Colormaps. We will make examples below.
2- Color vs Colormap
Colormaps are collections of colors that are used by certain Python objects such as plotting functions to distribute a colormap to the values in the whole plot.
Individual colors are useful when coloring individual objects such as, title of a chart, a single trendline, points on a scatter plot (such as centroids), axes labels etc. Usually passed to color or colors parameters.
We have fantastic builtin colormaps that are available in matplotlib and seaborn and we can also derive specific individual colors from those colormaps. Colormaps are utilized via cmap parameter.
In this tutorial we will show a few color operations that you can use to step up your Python color game and make the really beautiful visual touch that’s needed to your design objects, Python charts and animations.
import seaborn as sns from PIL import Image, ImageDraw iter = 15 palette = list(reversed(sns.color_palette("Spectral_r", iter).as_hex())) print(palette) width_px=1000 new = Image.new(mode="RGB", size=(width_px,120)) for i in range(iter): newt = Image.new(mode="RGB", size=(width_px//iter,100), color=palette[i]) new.paste(newt, (i*width_px//iter,10)) ['#be254a', '#dc484c', '#ef6645', '#f88c51', '#fdb365', '#fed27f', '#feeb9d', '#fffebe', '#f0f9a7', '#d8ef9b', '#b3e0a2', '#89d0a4', '#60bba8', '#3f97b7', '#4273b3']