diff --git a/examples/color/colormaps_reference.py b/examples/color/colormaps_reference.py
index e7ff56dea745..9f2c87e4a661 100644
--- a/examples/color/colormaps_reference.py
+++ b/examples/color/colormaps_reference.py
@@ -51,7 +51,8 @@
                              'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
                              'seismic']),
          ('Qualitative',    ['Accent', 'Dark2', 'Paired', 'Pastel1',
-                             'Pastel2', 'Set1', 'Set2', 'Set3']),
+                             'Pastel2', 'Set1', 'Set2', 'Set3', 'Vega10',
+                             'Vega20', 'Vega20b', 'Vega20c']),
          ('Miscellaneous',  ['gist_earth', 'terrain', 'ocean', 'gist_stern',
                              'brg', 'CMRmap', 'cubehelix',
                              'gnuplot', 'gnuplot2', 'gist_ncar',
diff --git a/lib/matplotlib/_cm.py b/lib/matplotlib/_cm.py
index 3df87a44dd0f..57df2009a129 100644
--- a/lib/matplotlib/_cm.py
+++ b/lib/matplotlib/_cm.py
@@ -1277,6 +1277,94 @@ def gfunc32(x):
 }
 
 
+# Categorical palettes from Vega:
+# https://github.com/vega/vega/wiki/Scales
+# (divided by 255)
+#
+
+_Vega10_data = (
+    (0.12156862745098039, 0.4666666666666667,  0.7058823529411765  ),  # 1f77b4
+    (1.0,                 0.4980392156862745,  0.054901960784313725),  # ff7f0e
+    (0.17254901960784313, 0.6274509803921569,  0.17254901960784313 ),  # 2ca02c
+    (0.8392156862745098,  0.15294117647058825, 0.1568627450980392  ),  # d62728
+    (0.5803921568627451,  0.403921568627451,   0.7411764705882353  ),  # 9467bd
+    (0.5490196078431373,  0.33725490196078434, 0.29411764705882354 ),  # 8c564b
+    (0.8901960784313725,  0.4666666666666667,  0.7607843137254902  ),  # e377c2
+    (0.4980392156862745,  0.4980392156862745,  0.4980392156862745  ),  # 7f7f7f
+    (0.7372549019607844,  0.7411764705882353,  0.13333333333333333 ),  # bcbd22
+    (0.09019607843137255, 0.7450980392156863,  0.8117647058823529),    # 17becf
+    )
+
+_Vega20_data = (
+	(0.12156862745098039, 0.4666666666666667,  0.7058823529411765  ),  # 1f77b4
+	(0.6823529411764706,  0.7803921568627451,  0.9098039215686274  ),  # aec7e8
+	(1.0,                 0.4980392156862745,  0.054901960784313725),  # ff7f0e
+	(1.0,                 0.7333333333333333,  0.47058823529411764 ),  # ffbb78
+	(0.17254901960784313, 0.6274509803921569,  0.17254901960784313 ),  # 2ca02c
+	(0.596078431372549,   0.8745098039215686,  0.5411764705882353  ),  # 98df8a
+	(0.8392156862745098,  0.15294117647058825, 0.1568627450980392  ),  # d62728
+	(1.0,                 0.596078431372549,   0.5882352941176471  ),  # ff9896
+	(0.5803921568627451,  0.403921568627451,   0.7411764705882353  ),  # 9467bd
+	(0.7725490196078432,  0.6901960784313725,  0.8352941176470589  ),  # c5b0d5
+	(0.5490196078431373,  0.33725490196078434, 0.29411764705882354 ),  # 8c564b
+	(0.7686274509803922,  0.611764705882353,   0.5803921568627451  ),  # c49c94
+	(0.8901960784313725,  0.4666666666666667,  0.7607843137254902  ),  # e377c2
+	(0.9686274509803922,  0.7137254901960784,  0.8235294117647058  ),  # f7b6d2
+	(0.4980392156862745,  0.4980392156862745,  0.4980392156862745  ),  # 7f7f7f
+	(0.7803921568627451,  0.7803921568627451,  0.7803921568627451  ),  # c7c7c7
+	(0.7372549019607844,  0.7411764705882353,  0.13333333333333333 ),  # bcbd22
+	(0.8588235294117647,  0.8588235294117647,  0.5529411764705883  ),  # dbdb8d
+	(0.09019607843137255, 0.7450980392156863,  0.8117647058823529  ),  # 17becf
+	(0.6196078431372549,  0.8549019607843137,  0.8980392156862745),    # 9edae5
+    )
+
+_Vega20b_data = (
+	(0.2235294117647059,  0.23137254901960785, 0.4745098039215686 ),  # 393b79
+	(0.3215686274509804,  0.32941176470588235, 0.6392156862745098 ),  # 5254a3
+	(0.4196078431372549,  0.43137254901960786, 0.8117647058823529 ),  # 6b6ecf
+	(0.611764705882353,   0.6196078431372549,  0.8705882352941177 ),  # 9c9ede
+	(0.38823529411764707, 0.4745098039215686,  0.2235294117647059 ),  # 637939
+	(0.5490196078431373,  0.6352941176470588,  0.3215686274509804 ),  # 8ca252
+	(0.7098039215686275,  0.8117647058823529,  0.4196078431372549 ),  # b5cf6b
+	(0.807843137254902,   0.8588235294117647,  0.611764705882353  ),  # cedb9c
+	(0.5490196078431373,  0.42745098039215684, 0.19215686274509805),  # 8c6d31
+	(0.7411764705882353,  0.6196078431372549,  0.2235294117647059 ),  # bd9e39
+	(0.9058823529411765,  0.7294117647058823,  0.3215686274509804 ),  # e7ba52
+	(0.9058823529411765,  0.796078431372549,   0.5803921568627451 ),  # e7cb94
+	(0.5176470588235295,  0.23529411764705882, 0.2235294117647059 ),  # 843c39
+	(0.6784313725490196,  0.28627450980392155, 0.2901960784313726 ),  # ad494a
+	(0.8392156862745098,  0.3803921568627451,  0.4196078431372549 ),  # d6616b
+	(0.9058823529411765,  0.5882352941176471,  0.611764705882353  ),  # e7969c
+	(0.4823529411764706,  0.2549019607843137,  0.45098039215686275),  # 7b4173
+	(0.6470588235294118,  0.3176470588235294,  0.5803921568627451 ),  # a55194
+	(0.807843137254902,   0.42745098039215684, 0.7411764705882353 ),  # ce6dbd
+	(0.8705882352941177,  0.6196078431372549,  0.8392156862745098 ),  # de9ed6
+    )
+
+_Vega20c_data = (
+	(0.19215686274509805, 0.5098039215686274,  0.7411764705882353  ),  # 3182bd
+	(0.4196078431372549,  0.6823529411764706,  0.8392156862745098  ),  # 6baed6
+	(0.6196078431372549,  0.792156862745098,   0.8823529411764706  ),  # 9ecae1
+	(0.7764705882352941,  0.8588235294117647,  0.9372549019607843  ),  # c6dbef
+	(0.9019607843137255,  0.3333333333333333,  0.050980392156862744),  # e6550d
+	(0.9921568627450981,  0.5529411764705883,  0.23529411764705882 ),  # fd8d3c
+	(0.9921568627450981,  0.6823529411764706,  0.4196078431372549  ),  # fdae6b
+	(0.9921568627450981,  0.8156862745098039,  0.6352941176470588  ),  # fdd0a2
+	(0.19215686274509805, 0.6392156862745098,  0.32941176470588235 ),  # 31a354
+	(0.4549019607843137,  0.7686274509803922,  0.4627450980392157  ),  # 74c476
+	(0.6313725490196078,  0.8509803921568627,  0.6078431372549019  ),  # a1d99b
+	(0.7803921568627451,  0.9137254901960784,  0.7529411764705882  ),  # c7e9c0
+	(0.4588235294117647,  0.4196078431372549,  0.6941176470588235  ),  # 756bb1
+	(0.6196078431372549,  0.6039215686274509,  0.7843137254901961  ),  # 9e9ac8
+	(0.7372549019607844,  0.7411764705882353,  0.8627450980392157  ),  # bcbddc
+	(0.8549019607843137,  0.8549019607843137,  0.9215686274509803  ),  # dadaeb
+	(0.38823529411764707, 0.38823529411764707, 0.38823529411764707 ),  # 636363
+	(0.5882352941176471,  0.5882352941176471,  0.5882352941176471  ),  # 969696
+	(0.7411764705882353,  0.7411764705882353,  0.7411764705882353  ),  # bdbdbd
+	(0.8509803921568627,  0.8509803921568627,  0.8509803921568627  ),  # d9d9d9
+    )
+
+
 datad = {
     'afmhot': _afmhot_data,
     'autumn': _autumn_data,
@@ -1358,3 +1446,8 @@ def gfunc32(x):
 datad['Set1'] = {'listed': _Set1_data}
 datad['Set2'] = {'listed': _Set2_data}
 datad['Set3'] = {'listed': _Set3_data}
+
+datad['Vega10'] = {'listed': _Vega10_data}
+datad['Vega20'] = {'listed': _Vega20_data}
+datad['Vega20b'] = {'listed': _Vega20b_data}
+datad['Vega20c'] = {'listed': _Vega20c_data}