## 1. Overview

This article is a reference of all named colors in Pandas. It shows a list of more than 1200+ named colors in Python, Matplotlib and Pandas. They are based on the Python library Matplotlib.

The work in based on two articles:

We are going to build different color palettes and different conversion techniques.

The image below shows some of the colors:

## 2. Convert colors in Matplotlib, Python and Pandas

To learn more about the color conversion in Python you can check 5th step of the above article: Working with color names and color values

In this section we will cover the next conversions:

• RGB to HEX = `(0.0, 1.0, 1.0)` -> `#00FFFF`
• HEX to RGB = `#00FFFF` -> `(0.0, 1.0, 1.0)`

### 2.1. Convert RGB to HEX color in Python and Pandas

Let's start with conversion of RGB color in decimal code to HEX code. To do so we are going to use Matplotlib method: `mcolors.rgb2hex()`:

``````import matplotlib.colors as mcolors
mcolors.rgb2hex((0.0, 1.0, 1.0))
``````

result:

``````'#00ffff'
``````

### 2.2. Convert RGB to HSL in Pandas

To convert from RGB to HSL in Pandas we are going to use method: `rgb_to_hsv`:

``````mcolors.rgb_to_hsv((0, 0, 1))
``````

the output is array of hue, saturation and lightness:

``````array([0.66666667, 1.        , 1.        ])
``````

### 2.3. Convert HEX to RGB format in Python and Pandas

Similarly we can do the HEX to RGB conversion by using method - `mcolors.hex2color():

``````import matplotlib.colors as mcolors
mcolors.hex2color('#40E0D0')
``````

result:

``````(0.25098039215686274, 0.8784313725490196, 0.8156862745098039)
``````

The result has high decimal precision. If you like to reduce the decimal numbers we can use list comprehension:

``````[round(c, 5) for c in (0.25098039215686274, 0.8784313725490196, 0.8156862745098039)]
``````

will give us:

``````[0.25098, 0.87843, 0.81569]
``````

To apply to a column in Pandas DataFrame we can use a lambda expression:

``````df_colors['rgb'] = df_colors['rgb'].apply(lambda x:[round(c, 5) for c in x])
``````

### 2.4. Convert RGB to HEX color for whole column in Pandas

We can also apply the conversion to a single or multiple columns in Pandas DataFrame. For this purpose we are going to use method `apply()`:

``````df_colors['hex'] = df_colors['rgb'].apply(mcolors.rgb2hex)
``````

The following example demonstrate this:

``````import pandas as pd
colors = {
'name': mcolors.BASE_COLORS.keys(),
'rgb': mcolors.BASE_COLORS.values()

}

df_colors = pd.DataFrame(colors)
df_colors['hex'] = df_colors['rgb'].apply(mcolors.rgb2hex)
``````

The result DataFrame with converted RGB column to HEX one:

name rgb hex
0 b (0, 0, 1) #0000ff
1 g (0, 0.5, 0) #008000
2 r (1, 0, 0) #ff0000
3 c (0, 0.75, 0.75) #00bfbf
4 m (0.75, 0, 0.75) #bf00bf

## 3. BASE_COLORS: Named Colors in Pandas

We will start with the most basic colors with names from Matplotlib - `mcolors.BASE_COLORS`.

Below you can find a table with the color name, RGB and HEX values plus display of the color:

We are going to use the next code to generate the values:

``````import pandas as pd

def format_color_groups(df, color):
x = df.copy()
i = 0

for factor in color:
x.iloc[i, :-1] = ''
style = f'background-color: {color[i]}'
x.loc[i, 'display color as background'] = style
i = i + 1

return x

colors = {
'name': mcolors.BASE_COLORS.keys(),
'rgb': mcolors.BASE_COLORS.values()

}

df_colors = pd.DataFrame(colors)
df_colors['hex'] = df_colors['rgb'].apply(mcolors.rgb2hex)

df_colors['display color as background'] = ''
df_colors.style.apply(format_color_groups, color=df_colors.hex,  axis=None)
``````

The list of the basic colors in Pandas is shown below:

name rgb hex display color as background
0 b (0, 0, 1) #0000ff
1 g (0, 0.5, 0) #008000
2 r (1, 0, 0) #ff0000
3 c (0, 0.75, 0.75) #00bfbf
4 m (0.75, 0, 0.75) #bf00bf
5 y (0.75, 0.75, 0) #bfbf00
6 k (0, 0, 0) #000000
7 w (1, 1, 1) #ffffff

## 4. TABLEAU_COLORS: List of Named Colors in Python and Pandas

Next we will cover the list of TABLEAU_COLORS in Pandas. They are limited to the most popular colors only. The code below will list all of them:

``````import pandas as pd

def format_color_groups(df, color):
x = df.copy()
i = 0

for factor in color:
x.iloc[i, :-1] = ''
style = f'background-color: {color[i]}'
x.loc[i, 'display color as background'] = style
i = i + 1

return x

colors = {
'name': mcolors.TABLEAU_COLORS.keys(),
'hex': mcolors.TABLEAU_COLORS.values()

}

df_colors = pd.DataFrame(colors)
df_colors['rgb'] = df_colors['hex'].apply(mcolors.hex2color)

df_colors['rgb'] = df_colors['rgb'].apply(lambda x:[round(c, 5) for c in x])

df_colors['display color as background'] = ''
df_colors.style.apply(format_color_groups, color=df_colors.hex,  axis=None)
``````

And the table is:

name hex rgb display color as background
0 tab:blue #1f77b4 [0.12157, 0.46667, 0.70588]
1 tab:orange #ff7f0e [1.0, 0.49804, 0.0549]
2 tab:green #2ca02c [0.17255, 0.62745, 0.17255]
3 tab:red #d62728 [0.83922, 0.15294, 0.15686]
4 tab:purple #9467bd [0.58039, 0.40392, 0.74118]
5 tab:brown #8c564b [0.54902, 0.33725, 0.29412]
6 tab:pink #e377c2 [0.8902, 0.46667, 0.76078]
7 tab:gray #7f7f7f [0.49804, 0.49804, 0.49804]
8 tab:olive #bcbd22 [0.73725, 0.74118, 0.13333]
9 tab:cyan #17becf [0.0902, 0.7451, 0.81176]

## 5. CSS4_COLORS colors in Python and Pandas

The CSS4_COLORS contains many different named colors which can be used in Python and Matplotlib. We can list all of them by:

``````def format_color_groups(df, color):
x = df.copy()
i = 0

for factor in color:
x.iloc[i, :-1] = ''
style = f'background-color: {color[i]}'
x.loc[i, 'display color as background'] = style
i = i + 1

return x

colors = {
'name': mcolors.CSS4_COLORS.keys(),
'hex': mcolors.CSS4_COLORS.values()

}

df_colors = pd.DataFrame(colors)
df_colors['rgb'] = df_colors['hex'].apply(mcolors.hex2color)

df_colors['rgb'] = df_colors['rgb'].apply(lambda x:[round(c, 5) for c in x])

df_colors['display color as background'] = ''
df_colors.style.apply(format_color_groups, color=df_colors.hex,  axis=None)
``````

We can find all the colors from this pallet below:

name hex rgb display color as background
0 aliceblue #F0F8FF [0.94118, 0.97255, 1.0]
1 antiquewhite #FAEBD7 [0.98039, 0.92157, 0.84314]
2 aqua #00FFFF [0.0, 1.0, 1.0]
3 aquamarine #7FFFD4 [0.49804, 1.0, 0.83137]
4 azure #F0FFFF [0.94118, 1.0, 1.0]
5 beige #F5F5DC [0.96078, 0.96078, 0.86275]
6 bisque #FFE4C4 [1.0, 0.89412, 0.76863]
7 black #000000 [0.0, 0.0, 0.0]
8 blanchedalmond #FFEBCD [1.0, 0.92157, 0.80392]
9 blue #0000FF [0.0, 0.0, 1.0]
10 blueviolet #8A2BE2 [0.54118, 0.16863, 0.88627]
11 brown #A52A2A [0.64706, 0.16471, 0.16471]
12 burlywood #DEB887 [0.87059, 0.72157, 0.52941]
13 cadetblue #5F9EA0 [0.37255, 0.61961, 0.62745]
14 chartreuse #7FFF00 [0.49804, 1.0, 0.0]
15 chocolate #D2691E [0.82353, 0.41176, 0.11765]
16 coral #FF7F50 [1.0, 0.49804, 0.31373]
17 cornflowerblue #6495ED [0.39216, 0.58431, 0.92941]
18 cornsilk #FFF8DC [1.0, 0.97255, 0.86275]
19 crimson #DC143C [0.86275, 0.07843, 0.23529]
20 cyan #00FFFF [0.0, 1.0, 1.0]
21 darkblue #00008B [0.0, 0.0, 0.5451]
22 darkcyan #008B8B [0.0, 0.5451, 0.5451]
23 darkgoldenrod #B8860B [0.72157, 0.52549, 0.04314]
24 darkgray #A9A9A9 [0.66275, 0.66275, 0.66275]
25 darkgreen #006400 [0.0, 0.39216, 0.0]
26 darkgrey #A9A9A9 [0.66275, 0.66275, 0.66275]
27 darkkhaki #BDB76B [0.74118, 0.71765, 0.41961]
28 darkmagenta #8B008B [0.5451, 0.0, 0.5451]
29 darkolivegreen #556B2F [0.33333, 0.41961, 0.18431]
30 darkorange #FF8C00 [1.0, 0.54902, 0.0]
31 darkorchid #9932CC [0.6, 0.19608, 0.8]
32 darkred #8B0000 [0.5451, 0.0, 0.0]
33 darksalmon #E9967A [0.91373, 0.58824, 0.47843]
34 darkseagreen #8FBC8F [0.56078, 0.73725, 0.56078]
35 darkslateblue #483D8B [0.28235, 0.23922, 0.5451]
36 darkslategray #2F4F4F [0.18431, 0.3098, 0.3098]
37 darkslategrey #2F4F4F [0.18431, 0.3098, 0.3098]
38 darkturquoise #00CED1 [0.0, 0.80784, 0.81961]
39 darkviolet #9400D3 [0.58039, 0.0, 0.82745]
40 deeppink #FF1493 [1.0, 0.07843, 0.57647]
41 deepskyblue #00BFFF [0.0, 0.74902, 1.0]
42 dimgray #696969 [0.41176, 0.41176, 0.41176]
43 dimgrey #696969 [0.41176, 0.41176, 0.41176]
44 dodgerblue #1E90FF [0.11765, 0.56471, 1.0]
45 firebrick #B22222 [0.69804, 0.13333, 0.13333]
46 floralwhite #FFFAF0 [1.0, 0.98039, 0.94118]
47 forestgreen #228B22 [0.13333, 0.5451, 0.13333]
48 fuchsia #FF00FF [1.0, 0.0, 1.0]
49 gainsboro #DCDCDC [0.86275, 0.86275, 0.86275]
50 ghostwhite #F8F8FF [0.97255, 0.97255, 1.0]
51 gold #FFD700 [1.0, 0.84314, 0.0]
52 goldenrod #DAA520 [0.8549, 0.64706, 0.12549]
53 gray #808080 [0.50196, 0.50196, 0.50196]
54 green #008000 [0.0, 0.50196, 0.0]
55 greenyellow #ADFF2F [0.67843, 1.0, 0.18431]
56 grey #808080 [0.50196, 0.50196, 0.50196]
57 honeydew #F0FFF0 [0.94118, 1.0, 0.94118]
58 hotpink #FF69B4 [1.0, 0.41176, 0.70588]
59 indianred #CD5C5C [0.80392, 0.36078, 0.36078]
60 indigo #4B0082 [0.29412, 0.0, 0.5098]
61 ivory #FFFFF0 [1.0, 1.0, 0.94118]
62 khaki #F0E68C [0.94118, 0.90196, 0.54902]
63 lavender #E6E6FA [0.90196, 0.90196, 0.98039]
64 lavenderblush #FFF0F5 [1.0, 0.94118, 0.96078]
65 lawngreen #7CFC00 [0.48627, 0.98824, 0.0]
66 lemonchiffon #FFFACD [1.0, 0.98039, 0.80392]
67 lightblue #ADD8E6 [0.67843, 0.84706, 0.90196]
68 lightcoral #F08080 [0.94118, 0.50196, 0.50196]
69 lightcyan #E0FFFF [0.87843, 1.0, 1.0]
70 lightgoldenrodyellow #FAFAD2 [0.98039, 0.98039, 0.82353]
71 lightgray #D3D3D3 [0.82745, 0.82745, 0.82745]
72 lightgreen #90EE90 [0.56471, 0.93333, 0.56471]
73 lightgrey #D3D3D3 [0.82745, 0.82745, 0.82745]
74 lightpink #FFB6C1 [1.0, 0.71373, 0.75686]
75 lightsalmon #FFA07A [1.0, 0.62745, 0.47843]
76 lightseagreen #20B2AA [0.12549, 0.69804, 0.66667]
77 lightskyblue #87CEFA [0.52941, 0.80784, 0.98039]
78 lightslategray #778899 [0.46667, 0.53333, 0.6]
79 lightslategrey #778899 [0.46667, 0.53333, 0.6]
80 lightsteelblue #B0C4DE [0.6902, 0.76863, 0.87059]
81 lightyellow #FFFFE0 [1.0, 1.0, 0.87843]
82 lime #00FF00 [0.0, 1.0, 0.0]
83 limegreen #32CD32 [0.19608, 0.80392, 0.19608]
84 linen #FAF0E6 [0.98039, 0.94118, 0.90196]
85 magenta #FF00FF [1.0, 0.0, 1.0]
86 maroon #800000 [0.50196, 0.0, 0.0]
87 mediumaquamarine #66CDAA [0.4, 0.80392, 0.66667]
88 mediumblue #0000CD [0.0, 0.0, 0.80392]
89 mediumorchid #BA55D3 [0.72941, 0.33333, 0.82745]
90 mediumpurple #9370DB [0.57647, 0.43922, 0.85882]
91 mediumseagreen #3CB371 [0.23529, 0.70196, 0.44314]
92 mediumslateblue #7B68EE [0.48235, 0.40784, 0.93333]
93 mediumspringgreen #00FA9A [0.0, 0.98039, 0.60392]
94 mediumturquoise #48D1CC [0.28235, 0.81961, 0.8]
95 mediumvioletred #C71585 [0.78039, 0.08235, 0.52157]
96 midnightblue #191970 [0.09804, 0.09804, 0.43922]
97 mintcream #F5FFFA [0.96078, 1.0, 0.98039]
98 mistyrose #FFE4E1 [1.0, 0.89412, 0.88235]
99 moccasin #FFE4B5 [1.0, 0.89412, 0.7098]
100 navajowhite #FFDEAD [1.0, 0.87059, 0.67843]
101 navy #000080 [0.0, 0.0, 0.50196]
102 oldlace #FDF5E6 [0.99216, 0.96078, 0.90196]
103 olive #808000 [0.50196, 0.50196, 0.0]
104 olivedrab #6B8E23 [0.41961, 0.55686, 0.13725]
105 orange #FFA500 [1.0, 0.64706, 0.0]
106 orangered #FF4500 [1.0, 0.27059, 0.0]
107 orchid #DA70D6 [0.8549, 0.43922, 0.83922]
108 palegoldenrod #EEE8AA [0.93333, 0.9098, 0.66667]
109 palegreen #98FB98 [0.59608, 0.98431, 0.59608]
110 paleturquoise #AFEEEE [0.68627, 0.93333, 0.93333]
111 palevioletred #DB7093 [0.85882, 0.43922, 0.57647]
112 papayawhip #FFEFD5 [1.0, 0.93725, 0.83529]
113 peachpuff #FFDAB9 [1.0, 0.8549, 0.72549]
114 peru #CD853F [0.80392, 0.52157, 0.24706]
115 pink #FFC0CB [1.0, 0.75294, 0.79608]
116 plum #DDA0DD [0.86667, 0.62745, 0.86667]
117 powderblue #B0E0E6 [0.6902, 0.87843, 0.90196]
118 purple #800080 [0.50196, 0.0, 0.50196]
119 rebeccapurple #663399 [0.4, 0.2, 0.6]
120 red #FF0000 [1.0, 0.0, 0.0]
121 rosybrown #BC8F8F [0.73725, 0.56078, 0.56078]
122 royalblue #4169E1 [0.2549, 0.41176, 0.88235]
123 saddlebrown #8B4513 [0.5451, 0.27059, 0.07451]
124 salmon #FA8072 [0.98039, 0.50196, 0.44706]
125 sandybrown #F4A460 [0.95686, 0.64314, 0.37647]
126 seagreen #2E8B57 [0.18039, 0.5451, 0.34118]
127 seashell #FFF5EE [1.0, 0.96078, 0.93333]
128 sienna #A0522D [0.62745, 0.32157, 0.17647]
129 silver #C0C0C0 [0.75294, 0.75294, 0.75294]
130 skyblue #87CEEB [0.52941, 0.80784, 0.92157]
131 slateblue #6A5ACD [0.41569, 0.35294, 0.80392]
132 slategray #708090 [0.43922, 0.50196, 0.56471]
133 slategrey #708090 [0.43922, 0.50196, 0.56471]
134 snow #FFFAFA [1.0, 0.98039, 0.98039]
135 springgreen #00FF7F [0.0, 1.0, 0.49804]
136 steelblue #4682B4 [0.27451, 0.5098, 0.70588]
137 tan #D2B48C [0.82353, 0.70588, 0.54902]
138 teal #008080 [0.0, 0.50196, 0.50196]
139 thistle #D8BFD8 [0.84706, 0.74902, 0.84706]
140 tomato #FF6347 [1.0, 0.38824, 0.27843]
141 turquoise #40E0D0 [0.25098, 0.87843, 0.81569]
142 violet #EE82EE [0.93333, 0.5098, 0.93333]
143 wheat #F5DEB3 [0.96078, 0.87059, 0.70196]
144 white #FFFFFF [1.0, 1.0, 1.0]
145 whitesmoke #F5F5F5 [0.96078, 0.96078, 0.96078]
146 yellow #FFFF00 [1.0, 1.0, 0.0]
147 yellowgreen #9ACD32 [0.60392, 0.80392, 0.19608]

## 6. XKCD_Colors: colors in Pandas and Python

Finally we will cover huge list of named colors in Python: XKCD_Colors. The list contains about 950 colors. So we are going to list only some of them:

The code for listing all 900+ XKCD_Colors in Pandas is similar to the previous ones:

``````def format_color_groups(df, color):
x = df.copy()
i = 0

for factor in color:
x.iloc[i, :-1] = ''
style = f'background-color: {color[i]}'
x.loc[i, 'display color as background'] = style
i = i + 1

return x

colors = {
'name': mcolors.XKCD_COLORS.keys(),
'hex': mcolors.XKCD_COLORS.values()

}

df_colors = pd.DataFrame(colors)
df_colors['rgb'] = df_colors['hex'].apply(mcolors.hex2color)

df_colors['rgb'] = df_colors['rgb'].apply(lambda x:[round(c, 5) for c in x])

df_colors['display color as background'] = ''
df_colors.style.apply(format_color_groups, color=df_colors.hex,  axis=None)
``````

Sample of the first 15 colors:

name hex rgb display color as background
0 xkcd:cloudy blue #acc2d9 [0.67451, 0.76078, 0.85098]
1 xkcd:dark pastel green #56ae57 [0.33725, 0.68235, 0.34118]
2 xkcd:dust #b2996e [0.69804, 0.6, 0.43137]
3 xkcd:electric lime #a8ff04 [0.65882, 1.0, 0.01569]
4 xkcd:fresh green #69d84f [0.41176, 0.84706, 0.3098]
5 xkcd:light eggplant #894585 [0.53725, 0.27059, 0.52157]
6 xkcd:nasty green #70b23f [0.43922, 0.69804, 0.24706]
7 xkcd:really light blue #d4ffff [0.83137, 1.0, 1.0]
8 xkcd:tea #65ab7c [0.39608, 0.67059, 0.48627]
9 xkcd:warm purple #952e8f [0.58431, 0.18039, 0.56078]
10 xkcd:yellowish tan #fcfc81 [0.98824, 0.98824, 0.50588]
11 xkcd:cement #a5a391 [0.64706, 0.63922, 0.56863]
12 xkcd:dark grass green #388004 [0.21961, 0.50196, 0.01569]
13 xkcd:dusty teal #4c9085 [0.29804, 0.56471, 0.52157]
14 xkcd:grey teal #5e9b8a [0.36863, 0.60784, 0.54118]

## 7. Matplotlib colors palettes - cmaps

In this section we can find list and view of all Matplotlib colors palettes. They are known as `cmap` and can be found in Pandas functions like parameters.

In total there are 166 Matplotlib colors palettes:

['magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'twilight_shifted', 'turbo', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu', 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Spectral', 'Wistia', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone', 'brg', 'bwr', 'cool', 'coolwarm', 'copper', 'cubehelix', 'flag', 'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow', 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray', 'hot', 'hsv', 'jet', 'nipy_spectral', 'ocean', 'pink', 'prism', 'rainbow', 'seismic', 'spring', 'summer', 'terrain', 'winter', 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c', 'magma_r', 'inferno_r', 'plasma_r', 'viridis_r', 'cividis_r', 'twilight_r', 'twilight_shifted_r', 'turbo_r', 'Blues_r', 'BrBG_r', 'BuGn_r', 'BuPu_r', 'CMRmap_r', 'GnBu_r', 'Greens_r', 'Greys_r', 'OrRd_r', 'Oranges_r', 'PRGn_r', 'PiYG_r', 'PuBu_r', 'PuBuGn_r', 'PuOr_r', 'PuRd_r', 'Purples_r', 'RdBu_r', 'RdGy_r', 'RdPu_r', 'RdYlBu_r', 'RdYlGn_r', 'Reds_r', 'Spectral_r', 'Wistia_r', 'YlGn_r', 'YlGnBu_r', 'YlOrBr_r', 'YlOrRd_r', 'afmhot_r', 'autumn_r', 'binary_r', 'bone_r', 'brg_r', 'bwr_r', 'cool_r', 'coolwarm_r', 'copper_r', 'cubehelix_r', 'flag_r', 'gist_earth_r', 'gist_gray_r', 'gist_heat_r', 'gist_ncar_r', 'gist_rainbow_r', 'gist_stern_r', 'gist_yarg_r', 'gnuplot_r', 'gnuplot2_r', 'gray_r', 'hot_r', 'hsv_r', 'jet_r', 'nipy_spectral_r', 'ocean_r', 'pink_r', 'prism_r', 'rainbow_r', 'seismic_r', 'spring_r', 'summer_r', 'terrain_r', 'winter_r', 'Accent_r', 'Dark2_r', 'Paired_r', 'Pastel1_r', 'Pastel2_r', 'Set1_r', 'Set2_r', 'Set3_r', 'tab10_r', 'tab20_r', 'tab20b_r', 'tab20c_r']

The image below show the palette name and the colors:

The code to generate this image can be found on this link: How to view all colormaps available in matplotlib?

## 8. Conclusion

We saw how to list huge amount of named colors in Python and Matplotlib. We saw also how to use named colors in Pandas.

Finally we learned how to convert different color formats in Python and Pandas.

Multiple examples of Matplotlib colors are shown. This article can be used as a quick guide or reference for Matplotlib colors and Python styling.