List of named colors#
First we define a helper function for making a table of colors, then we use it on some common color categories.
import math from matplotlib.patches import Rectangle import matplotlib.pyplot as plt import matplotlib.colors as mcolors def plot_colortable(colors, *, ncols=4, sort_colors=True): cell_width = 212 cell_height = 22 swatch_width = 48 margin = 12 # Sort colors by hue, saturation, value and name. if sort_colors is True: names = sorted( colors, key=lambda c: tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(c)))) else: names = list(colors) n = len(names) nrows = math.ceil(n / ncols) width = cell_width * 4 + 2 * margin height = cell_height * nrows + 2 * margin dpi = 72 fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi) fig.subplots_adjust(margin/width, margin/height, (width-margin)/width, (height-margin)/height) ax.set_xlim(0, cell_width * 4) ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.) ax.yaxis.set_visible(False) ax.xaxis.set_visible(False) ax.set_axis_off() for i, name in enumerate(names): row = i % nrows col = i // nrows y = row * cell_height swatch_start_x = cell_width * col text_pos_x = cell_width * col + swatch_width + 7 ax.text(text_pos_x, y, name, fontsize=14, horizontalalignment='left', verticalalignment='center') ax.add_patch( Rectangle(xy=(swatch_start_x, y-9), width=swatch_width, height=18, facecolor=colors[name], edgecolor='0.7') ) return fig
Base colors#
Tableau Palette#
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)