diff --git a/ci/310-numba.yaml b/ci/310-numba.yaml
index 4a1e9c5f..b04be43f 100644
--- a/ci/310-numba.yaml
+++ b/ci/310-numba.yaml
@@ -7,6 +7,7 @@ dependencies:
   - scipy
   - beautifulsoup4
   - scikit-learn
+  - distinctipy
   # testing
   - pytest
   - pytest-cov
diff --git a/ci/310.yaml b/ci/310.yaml
index a92794a5..762461d6 100644
--- a/ci/310.yaml
+++ b/ci/310.yaml
@@ -7,6 +7,7 @@ dependencies:
   - scipy
   - beautifulsoup4
   - scikit-learn
+  - distinctipy
   # testing
   - pytest
   - pytest-cov
diff --git a/ci/311.yaml b/ci/311.yaml
index 9348d111..8751112f 100644
--- a/ci/311.yaml
+++ b/ci/311.yaml
@@ -7,6 +7,7 @@ dependencies:
   - scipy
   - beautifulsoup4
   - scikit-learn
+  - distinctipy
   # testing
   - pytest
   - pytest-cov
diff --git a/ci/38-numba.yaml b/ci/38-numba.yaml
index f7759ab9..6c0f5196 100644
--- a/ci/38-numba.yaml
+++ b/ci/38-numba.yaml
@@ -7,6 +7,7 @@ dependencies:
   - scipy
   - beautifulsoup4
   - scikit-learn
+  - distinctipy
   # testing
   - pytest
   - pytest-cov
diff --git a/ci/38.yaml b/ci/38.yaml
index c2aee545..38819e39 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -19,3 +19,4 @@ dependencies:
   - zstd
   - networkx
   - libpysal
+  - distinctipy
diff --git a/ci/39-numba.yaml b/ci/39-numba.yaml
index 026b441b..0dcb9dfb 100644
--- a/ci/39-numba.yaml
+++ b/ci/39-numba.yaml
@@ -7,6 +7,7 @@ dependencies:
   - scipy
   - beautifulsoup4
   - scikit-learn
+  - distinctipy
   # testing
   - pytest
   - pytest-cov
diff --git a/ci/39.yaml b/ci/39.yaml
index 10b6eec7..e76c9fa1 100644
--- a/ci/39.yaml
+++ b/ci/39.yaml
@@ -19,3 +19,4 @@ dependencies:
   - zstd
   - networkx
   - libpysal
+  - distinctipy
diff --git a/mapclassify/__init__.py b/mapclassify/__init__.py
index b0772f81..227f356b 100644
--- a/mapclassify/__init__.py
+++ b/mapclassify/__init__.py
@@ -17,6 +17,7 @@
     Percentiles,
     Quantiles,
     StdMean,
+    UniqueValue,
     UserDefined,
     gadf,
     load_example,
diff --git a/mapclassify/classifiers.py b/mapclassify/classifiers.py
index 60add361..29697d6f 100644
--- a/mapclassify/classifiers.py
+++ b/mapclassify/classifiers.py
@@ -5,7 +5,11 @@
 import functools
 import warnings
 
+import distinctipy
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import scipy.stats as stats
 from sklearn.cluster import KMeans as KMEANS
 
@@ -28,6 +32,7 @@
     "Quantiles",
     "Percentiles",
     "StdMean",
+    "UniqueValue",
     "UserDefined",
     "gadf",
     "KClassifiers",
@@ -49,6 +54,7 @@
     "Quantiles",
     "Percentiles",
     "StdMean",
+    "UniqueValue",
     "UserDefined",
 )
 
@@ -205,6 +211,12 @@ def head_tail_breaks(values, cuts):
     return cuts
 
 
+def unique_value(values):
+    """Unique value classifier."""
+    labels, counts = np.unique(values, return_counts=True)
+    return labels, counts
+
+
 def quantile(y, k=4):
     """
     Calculates the quantiles for an array.
@@ -1080,6 +1092,109 @@ def plot(
         return f, ax
 
 
+class UniqueValue:
+    def __init__(self, gdf, column):
+        """Unique value classifier.
+
+        A unique value classifier is appropriate for a categorical
+        variable that has a discrete set of unique values. For
+        example, a map of US counties where the variable State takes
+        on the same value for counties belonging to the same state.
+
+        This classifier creates k unique colors associated with the k
+        unique values of the column specified.
+
+
+        Parameters
+        ----------
+        gdf : Geopandas GeoDataFrame
+
+        column : str
+                 Column of data frame with unique values to classify.
+        """
+
+        self.name = "UniqueValue"
+        self.y = gdf[column]
+        bins, counts = unique_value(self.y)
+        self.yb = self.y
+        self.counts = counts
+        self.classes = bins
+        self.colors = distinctipy.get_colors(len(counts))
+        self.column = column
+        self.gdf = gdf
+
+    def plot(
+        self,
+        figsize=(16, 9),
+        fontsize=17,
+        legend=True,
+        sort_by_counts=False,
+        ascending=True,
+        bbox_to_anchor=(1.7, 1),
+        loc="upper right",
+        axis_off=True,
+        title=None,
+        title_fontsize="medium",
+    ):
+        """
+        Parameters
+        ----------
+        figsize : tuple
+                  Size of figure, width and hight in inches.
+        fontsize : int
+                   Size of fonts for legend entries.
+        legend : bool, default True
+                 Show legend.
+        sort_by_counts: bool, default False
+                        Sort legend entries by counts.
+        ascending : bool, default True
+                    Use ascending sort.
+        bbox_to_anchor : tuple
+                         Lower left corner of legend box.
+        loc : str
+              matplotlib legend location
+        axis_off : bool, default True
+                   Omit axis.
+        title  : str
+                 Legend title.
+
+        title_fontsize : {'xx-small', 'x-small, 'small', 'medium', 'large',
+                         x-large', xx-large'}
+                         The font size of the legend's title.
+        """
+        c = np.array(self.colors)
+        _colors = c[np.searchsorted(self.classes, self.yb)]
+        self.gdf.plot(color=_colors, figsize=figsize)
+        patch_list = []
+        df = pd.DataFrame(
+            data={"label": self.classes, "count": self.counts, "color": self.colors}
+        )
+        sort_col = "label"
+        if sort_by_counts:
+            sort_col = "count"
+        if legend:
+            w = len(str(df["count"].max()))
+            legend_dict = {}
+            for index, row in df.sort_values(
+                by=sort_col, ascending=ascending
+            ).iterrows():
+                entry = f'{row["label"]:>{w}} ({row["count"]})'
+                legend_dict[entry] = row["color"]
+                data_key = mpatches.Patch(color=row["color"], label=entry)
+                patch_list.append(data_key)
+
+            plt.legend(
+                handles=patch_list,
+                bbox_to_anchor=bbox_to_anchor,
+                loc=loc,
+                fontsize=fontsize,
+                title=title,
+                title_fontsize=title_fontsize,
+            )
+        if axis_off:
+            plt.axis("off")
+
+
 class HeadTailBreaks(MapClassifier):
     """
     Head/tail Breaks Map Classification for Heavy-tailed Distributions.
diff --git a/notebooks/07_unique.ipynb b/notebooks/07_unique.ipynb
new file mode 100644
index 00000000..001bdf0a
--- /dev/null
+++ b/notebooks/07_unique.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Unique Value Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-05T19:18:41.941529Z",
+     "start_time": "2022-11-05T19:18:40.603589Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import mapclassify\n",
+    "import numpy\n",
+    "import libpysal\n",
+    "import geopandas\n",
+    "import distinctipy\n",
+    "import matplotlib.patches as mpatches\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas\n",
+    "\n",
+    "mapclassify.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "libpysal.examples.load_example(\"South\")\n",
+    "gdf = geopandas.read_file(libpysal.examples.get_path('south.shp'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uv = mapclassify.UniqueValue(gdf, 'STATE_NAME')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uv.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uv.plot(sort_by_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uv.plot(sort_by_counts=True, ascending=False, bbox_to_anchor=(1.3,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uv.plot(sort_by_counts=True, ascending=False, bbox_to_anchor=(1.3,1),\n",
+    "       title='State')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_20m.zip\"\n",
+    "us = geopandas.read_file(url)\n",
+    "us = us[us.STATEFP!='02']\n",
+    "us = us[us.STATEFP!='15']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(sort_by_counts=True, ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(sort_by_counts=True, ascending=False,\n",
+    "                                           fontsize=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(sort_by_counts=True, ascending=False,\n",
+    "                                           fontsize=8, bbox_to_anchor=(1.1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(sort_by_counts=True, ascending=False,\n",
+    "                                           fontsize=8, bbox_to_anchor=(1.1, 1), loc='center right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=True, axis_off=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=True, axis_off=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=True, axis_off=False, \n",
+    "                                            title='state fips')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=True, axis_off=False, \n",
+    "                                            title='state fips',\n",
+    "                                           title_fontsize='large')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapclassify.UniqueValue(us, 'STATEFP').plot(legend=True, axis_off=False, \n",
+    "                                            title='state fips',\n",
+    "                                           title_fontsize='x-large')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 584658b9..036f592e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ numpy>=1.3
 scikit-learn
 pandas>=1.0
 networkx
+distinctipy