diff --git a/HISTORY.md b/HISTORY.md
index 4af2ad497..fe2469b70 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -17,7 +17,8 @@
 - Add `rasterized` arg for heatmaps (#359)
 - Support 1D cvt_archive_heatmap ({pr}`362`)
 - Add 3D plots for CVTArchive ({pr}`371`)
-- Add visualization of 3D QDax repertoires ({pr}`372`)
+- Add visualization of 3D QDax repertoires ({pr}`373`)
+- Enable plotting custom data in visualizations ({pr}`374`)
 
 #### Documentation
 
diff --git a/ribs/visualize/_cvt_archive_3d_plot.py b/ribs/visualize/_cvt_archive_3d_plot.py
index 2b96509f0..31e2dddfa 100644
--- a/ribs/visualize/_cvt_archive_3d_plot.py
+++ b/ribs/visualize/_cvt_archive_3d_plot.py
@@ -5,7 +5,7 @@
 from mpl_toolkits.mplot3d.art3d import Poly3DCollection
 from scipy.spatial import Voronoi  # pylint: disable=no-name-in-module
 
-from ribs.visualize._utils import (retrieve_cmap, set_cbar,
+from ribs.visualize._utils import (retrieve_cmap, set_cbar, validate_df,
                                    validate_heatmap_visual_args)
 
 
@@ -13,6 +13,7 @@ def cvt_archive_3d_plot(
     archive,
     ax=None,
     *,
+    df=None,
     measure_order=None,
     cmap="magma",
     lw=0.5,
@@ -150,6 +151,13 @@ def cvt_archive_3d_plot(
         archive (CVTArchive): A 3D :class:`~ribs.archives.CVTArchive`.
         ax (matplotlib.axes.Axes): Axes on which to plot the heatmap.
             If ``None``, we will create a new 3D axis.
+        df (ribs.archives.ArchiveDataFrame): If provided, we will plot data from
+            this argument instead of the data currently in the archive. This
+            data can be obtained by, for instance, calling
+            :meth:`ribs.archives.ArchiveBase.as_pandas()` and modifying the
+            resulting :class:`ArchiveDataFrame`. Note that, at a minimum, the
+            data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         measure_order (array-like of int): Specifies the axes order for plotting
             the measures. By default, the first measure (measure 0) in the
             archive appears on the x-axis, the second (measure 1) on y-axis, and
@@ -208,8 +216,8 @@ def cvt_archive_3d_plot(
     # Try getting the colormap early in case it fails.
     cmap = retrieve_cmap(cmap)
 
-    # Retrieve data from archive.
-    df = archive.as_pandas()
+    # Retrieve archive data.
+    df = archive.as_pandas() if df is None else validate_df(df)
     objective_batch = df.objective_batch()
     measures_batch = df.measures_batch()
     lower_bounds = archive.lower_bounds
@@ -289,7 +297,7 @@ def cvt_archive_3d_plot(
     objs = []  # Also record objective for each ridge so we can color it.
 
     # Map from centroid index to objective.
-    pt_to_obj = {elite.index: elite.objective for elite in archive}
+    pt_to_obj = dict(zip(df.index_batch(), objective_batch))
 
     # The points in the Voronoi diagram are indexed by their placement in the
     # input list. Above, when we called Voronoi, `centroids` were placed first,
diff --git a/ribs/visualize/_cvt_archive_heatmap.py b/ribs/visualize/_cvt_archive_heatmap.py
index 799ed9534..0648dbb90 100644
--- a/ribs/visualize/_cvt_archive_heatmap.py
+++ b/ribs/visualize/_cvt_archive_heatmap.py
@@ -7,7 +7,7 @@
 from scipy.spatial import Voronoi  # pylint: disable=no-name-in-module
 
 from ribs.visualize._utils import (archive_heatmap_1d, retrieve_cmap, set_cbar,
-                                   validate_heatmap_visual_args)
+                                   validate_df, validate_heatmap_visual_args)
 
 # Matplotlib functions tend to have a ton of args.
 # pylint: disable = too-many-arguments
@@ -16,6 +16,7 @@
 def cvt_archive_heatmap(archive,
                         ax=None,
                         *,
+                        df=None,
                         transpose_measures=False,
                         cmap="magma",
                         aspect=None,
@@ -98,6 +99,13 @@ def cvt_archive_heatmap(archive,
         archive (CVTArchive): A 1D or 2D :class:`~ribs.archives.CVTArchive`.
         ax (matplotlib.axes.Axes): Axes on which to plot the heatmap.
             If ``None``, the current axis will be used.
+        df (ribs.archives.ArchiveDataFrame): If provided, we will plot data from
+            this argument instead of the data currently in the archive. This
+            data can be obtained by, for instance, calling
+            :meth:`ribs.archives.ArchiveBase.as_pandas()` and modifying the
+            resulting :class:`ArchiveDataFrame`. Note that, at a minimum, the
+            data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set
@@ -173,6 +181,9 @@ def cvt_archive_heatmap(archive,
     # Try getting the colormap early in case it fails.
     cmap = retrieve_cmap(cmap)
 
+    # Retrieve archive data.
+    df = archive.as_pandas() if df is None else validate_df(df)
+
     if archive.measure_dim == 1:
         # Read in pcm kwargs -- the linewidth and edgecolor are overwritten by
         # our arguments.
@@ -195,8 +206,6 @@ def cvt_archive_heatmap(archive,
             [archive.upper_bounds[0]],
         ))
 
-        df = archive.as_pandas()
-
         # centroid_sort_idx tells us which index to place the centroid at such
         # that it is sorted, i.e., it maps from the indices in the centroid
         # array to the cell indices. This means that if you index with it, e.g.,
@@ -279,7 +288,7 @@ def cvt_archive_heatmap(archive,
         # the region index of each point.
         region_obj = [None] * len(vor.regions)
         min_obj, max_obj = np.inf, -np.inf
-        pt_to_obj = {elite.index: elite.objective for elite in archive}
+        pt_to_obj = dict(zip(df.index_batch(), df.objective_batch()))
         for pt_idx, region_idx in enumerate(
                 vor.point_region[:-4]):  # Exclude faraway_pts.
             if region_idx != -1 and pt_idx in pt_to_obj:
diff --git a/ribs/visualize/_grid_archive_heatmap.py b/ribs/visualize/_grid_archive_heatmap.py
index 918951e64..703f713bd 100644
--- a/ribs/visualize/_grid_archive_heatmap.py
+++ b/ribs/visualize/_grid_archive_heatmap.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from ribs.visualize._utils import (archive_heatmap_1d, retrieve_cmap, set_cbar,
-                                   validate_heatmap_visual_args)
+                                   validate_df, validate_heatmap_visual_args)
 
 # Matplotlib functions tend to have a ton of args.
 # pylint: disable = too-many-arguments
@@ -12,6 +12,7 @@
 def grid_archive_heatmap(archive,
                          ax=None,
                          *,
+                         df=None,
                          transpose_measures=False,
                          cmap="magma",
                          aspect=None,
@@ -86,6 +87,13 @@ def grid_archive_heatmap(archive,
         archive (GridArchive): A 1D or 2D :class:`~ribs.archives.GridArchive`.
         ax (matplotlib.axes.Axes): Axes on which to plot the heatmap.
             If ``None``, the current axis will be used.
+        df (ribs.archives.ArchiveDataFrame): If provided, we will plot data from
+            this argument instead of the data currently in the archive. This
+            data can be obtained by, for instance, calling
+            :meth:`ribs.archives.ArchiveBase.as_pandas()` and modifying the
+            resulting :class:`ArchiveDataFrame`. Note that, at a minimum, the
+            data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set
@@ -138,8 +146,10 @@ def grid_archive_heatmap(archive,
     # Try getting the colormap early in case it fails.
     cmap = retrieve_cmap(cmap)
 
+    # Retrieve archive data.
+    df = archive.as_pandas() if df is None else validate_df(df)
+
     if archive.measure_dim == 1:
-        df = archive.as_pandas()
         cell_objectives = np.full(archive.cells, np.nan)
         cell_idx = archive.int_to_grid_index(df.index_batch()).squeeze()
         cell_objectives[cell_idx] = df.objective_batch()
@@ -161,7 +171,6 @@ def grid_archive_heatmap(archive,
 
     elif archive.measure_dim == 2:
         # Retrieve data from archive.
-        df = archive.as_pandas()
         objective_batch = df.objective_batch()
         lower_bounds = archive.lower_bounds
         upper_bounds = archive.upper_bounds
diff --git a/ribs/visualize/_parallel_axes_plot.py b/ribs/visualize/_parallel_axes_plot.py
index 1afb7b687..28b57ca45 100644
--- a/ribs/visualize/_parallel_axes_plot.py
+++ b/ribs/visualize/_parallel_axes_plot.py
@@ -4,7 +4,7 @@
 import numpy as np
 from matplotlib.cm import ScalarMappable
 
-from ribs.visualize._utils import retrieve_cmap, set_cbar
+from ribs.visualize._utils import retrieve_cmap, set_cbar, validate_df
 
 # Matplotlib functions tend to have a ton of args.
 # pylint: disable = too-many-arguments
@@ -13,6 +13,7 @@
 def parallel_axes_plot(archive,
                        ax=None,
                        *,
+                       df=None,
                        measure_order=None,
                        cmap="magma",
                        linewidth=1.5,
@@ -78,6 +79,13 @@ def parallel_axes_plot(archive,
         archive (ArchiveBase): Any ribs archive.
         ax (matplotlib.axes.Axes): Axes on which to create the plot.
             If ``None``, the current axis will be used.
+        df (ribs.archives.ArchiveDataFrame): If provided, we will plot data from
+            this argument instead of the data currently in the archive. This
+            data can be obtained by, for instance, calling
+            :meth:`ribs.archives.ArchiveBase.as_pandas()` and modifying the
+            resulting :class:`ArchiveDataFrame`. Note that, at a minimum, the
+            data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         measure_order (list of int or list of (int, str)): If this is a list
             of ints, it specifies the axes order for measures (e.g. ``[2, 0,
             1]``). If this is a list of tuples, each tuple takes the form
@@ -155,7 +163,7 @@ def parallel_axes_plot(archive,
         upper_bounds = archive.upper_bounds[cols]
 
     host_ax = plt.gca() if ax is None else ax  # Try to get current axis.
-    df = archive.as_pandas(include_solutions=False)
+    df = archive.as_pandas() if df is None else validate_df(df)
     vmin = df["objective"].min() if vmin is None else vmin
     vmax = df["objective"].max() if vmax is None else vmax
     norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)
diff --git a/ribs/visualize/_sliding_boundaries_archive_heatmap.py b/ribs/visualize/_sliding_boundaries_archive_heatmap.py
index aa81d7946..207150cc1 100644
--- a/ribs/visualize/_sliding_boundaries_archive_heatmap.py
+++ b/ribs/visualize/_sliding_boundaries_archive_heatmap.py
@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from ribs.visualize._utils import (retrieve_cmap, set_cbar,
+from ribs.visualize._utils import (retrieve_cmap, set_cbar, validate_df,
                                    validate_heatmap_visual_args)
 
 # Matplotlib functions tend to have a ton of args.
@@ -12,6 +12,7 @@
 def sliding_boundaries_archive_heatmap(archive,
                                        ax=None,
                                        *,
+                                       df=None,
                                        transpose_measures=False,
                                        cmap="magma",
                                        aspect="auto",
@@ -64,6 +65,13 @@ def sliding_boundaries_archive_heatmap(archive,
             :class:`~ribs.archives.SlidingBoundariesArchive`.
         ax (matplotlib.axes.Axes): Axes on which to plot the heatmap.
             If ``None``, the current axis will be used.
+        df (ribs.archives.ArchiveDataFrame): If provided, we will plot data from
+            this argument instead of the data currently in the archive. This
+            data can be obtained by, for instance, calling
+            :meth:`ribs.archives.ArchiveBase.as_pandas()` and modifying the
+            resulting :class:`ArchiveDataFrame`. Note that, at a minimum, the
+            data must contain columns for index, objective, and measures. To
+            display a custom metric, replace the "objective" column.
         transpose_measures (bool): By default, the first measure in the archive
             will appear along the x-axis, and the second will be along the
             y-axis. To switch this behavior (i.e. to transpose the axes), set
@@ -110,8 +118,8 @@ def sliding_boundaries_archive_heatmap(archive,
     # Try getting the colormap early in case it fails.
     cmap = retrieve_cmap(cmap)
 
-    # Retrieve data from archive.
-    df = archive.as_pandas()
+    # Retrieve archive data.
+    df = archive.as_pandas() if df is None else validate_df(df)
     measures_batch = df.measures_batch()
     x = measures_batch[:, 0]
     y = measures_batch[:, 1]
diff --git a/ribs/visualize/_utils.py b/ribs/visualize/_utils.py
index dfcbbf7aa..6b393aed8 100644
--- a/ribs/visualize/_utils.py
+++ b/ribs/visualize/_utils.py
@@ -3,6 +3,8 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+from ribs.archives import ArchiveDataFrame
+
 
 def retrieve_cmap(cmap):
     """Retrieves colormap from Matplotlib."""
@@ -39,6 +41,17 @@ def validate_heatmap_visual_args(aspect, cbar, measure_dim, valid_dims,
                          "or matplotlib.axes.Axes")
 
 
+def validate_df(df):
+    """Helper to validate the df passed into visualization functions."""
+
+    # Cast to an ArchiveDataFrame in case someone passed in a regular DataFrame
+    # or other object.
+    if not isinstance(df, ArchiveDataFrame):
+        df = ArchiveDataFrame(df)
+
+    return df
+
+
 def set_cbar(t, ax, cbar, cbar_kwargs):
     """Sets cbar on the Axes given cbar arg."""
     cbar_kwargs = {} if cbar_kwargs is None else cbar_kwargs
@@ -119,5 +132,4 @@ def archive_heatmap_1d(
 
     # Create color bar.
     set_cbar(t, ax, cbar, cbar_kwargs)
-
     return ax
diff --git a/tests/visualize/baseline_images/cvt_archive_3d_plot_test/plot_metadata_with_df.png b/tests/visualize/baseline_images/cvt_archive_3d_plot_test/plot_metadata_with_df.png
new file mode 100644
index 000000000..6fe001b00
Binary files /dev/null and b/tests/visualize/baseline_images/cvt_archive_3d_plot_test/plot_metadata_with_df.png differ
diff --git a/tests/visualize/baseline_images/cvt_archive_heatmap_test/plot_metadata_with_df.png b/tests/visualize/baseline_images/cvt_archive_heatmap_test/plot_metadata_with_df.png
new file mode 100644
index 000000000..1c8946919
Binary files /dev/null and b/tests/visualize/baseline_images/cvt_archive_heatmap_test/plot_metadata_with_df.png differ
diff --git a/tests/visualize/baseline_images/grid_archive_heatmap_test/plot_metadata_with_df.png b/tests/visualize/baseline_images/grid_archive_heatmap_test/plot_metadata_with_df.png
new file mode 100644
index 000000000..e353c8471
Binary files /dev/null and b/tests/visualize/baseline_images/grid_archive_heatmap_test/plot_metadata_with_df.png differ
diff --git a/tests/visualize/baseline_images/parallel_axes_plot_test/plot_metadata_with_df.png b/tests/visualize/baseline_images/parallel_axes_plot_test/plot_metadata_with_df.png
new file mode 100644
index 000000000..aeabf380f
Binary files /dev/null and b/tests/visualize/baseline_images/parallel_axes_plot_test/plot_metadata_with_df.png differ
diff --git a/tests/visualize/baseline_images/sliding_boundaries_archive_heatmap_test/plot_metadata_with_df.png b/tests/visualize/baseline_images/sliding_boundaries_archive_heatmap_test/plot_metadata_with_df.png
new file mode 100644
index 000000000..6366796a9
Binary files /dev/null and b/tests/visualize/baseline_images/sliding_boundaries_archive_heatmap_test/plot_metadata_with_df.png differ
diff --git a/tests/visualize/conftest.py b/tests/visualize/conftest.py
index 91a7230f7..67910128e 100644
--- a/tests/visualize/conftest.py
+++ b/tests/visualize/conftest.py
@@ -40,6 +40,8 @@ def add_uniform_sphere_2d(archive, x_range, y_range):
     The solutions are the same as the measures (the (x,y) coordinates).
 
     x_range and y_range are tuples of (lower_bound, upper_bound).
+
+    The metadata contains the positive sphere function for the points.
     """
     xxs, yys = np.meshgrid(
         np.linspace(x_range[0], x_range[1], 100),
@@ -47,10 +49,12 @@ def add_uniform_sphere_2d(archive, x_range, y_range):
     )
     xxs, yys = xxs.ravel(), yys.ravel()
     coords = np.stack((xxs, yys), axis=1)
+    sphere = xxs**2 + yys**2
     archive.add(
         solution_batch=coords,
-        objective_batch=-(xxs**2 + yys**2),  # Negative sphere.
+        objective_batch=-sphere,  # Negative sphere.
         measures_batch=coords,
+        metadata_batch=sphere,  # Positive sphere.
     )
 
 
@@ -60,6 +64,8 @@ def add_uniform_sphere_3d(archive, x_range, y_range, z_range):
     The solutions are the same as the measures (the (x,y,z) coordinates).
 
     x_range, y_range, and z_range are tuples of (lower_bound, upper_bound).
+
+    The metadata contains the positive sphere function for the points.
     """
     xxs, yys, zzs = np.meshgrid(
         np.linspace(x_range[0], x_range[1], 40),
@@ -68,8 +74,10 @@ def add_uniform_sphere_3d(archive, x_range, y_range, z_range):
     )
     xxs, yys, zzs = xxs.ravel(), yys.ravel(), zzs.ravel()
     coords = np.stack((xxs, yys, zzs), axis=1)
+    sphere = xxs**2 + yys**2 + zzs**2
     archive.add(
         solution_batch=coords,
-        objective_batch=-(xxs**2 + yys**2 + zzs**2),  # Negative sphere.
+        objective_batch=-sphere,  # Negative sphere.
         measures_batch=coords,
+        metadata_batch=sphere,  # Positive sphere.
     )
diff --git a/tests/visualize/cvt_archive_3d_plot_test.py b/tests/visualize/cvt_archive_3d_plot_test.py
index d91f8494d..e91f5a200 100644
--- a/tests/visualize/cvt_archive_3d_plot_test.py
+++ b/tests/visualize/cvt_archive_3d_plot_test.py
@@ -200,3 +200,14 @@ def test_transparent(cvt_archive_3d):
 def test_plot_elites(cvt_archive_3d):
     plt.figure(figsize=(8, 6))
     cvt_archive_3d_plot(cvt_archive_3d, cell_alpha=0.0, plot_elites=True)
+
+
+@image_comparison(baseline_images=["plot_metadata_with_df"],
+                  remove_text=False,
+                  extensions=["png"],
+                  tol=CVT_IMAGE_TOLERANCE)
+def test_plot_metadata_with_df(cvt_archive_3d):
+    plt.figure(figsize=(8, 6))
+    df = cvt_archive_3d.as_pandas(include_metadata=True)
+    df["objective"] = df["metadata"]
+    cvt_archive_3d_plot(cvt_archive_3d, df=df)
diff --git a/tests/visualize/cvt_archive_heatmap_test.py b/tests/visualize/cvt_archive_heatmap_test.py
index a1ee505cd..d5a6827d6 100644
--- a/tests/visualize/cvt_archive_heatmap_test.py
+++ b/tests/visualize/cvt_archive_heatmap_test.py
@@ -219,6 +219,17 @@ def test_rasterized(cvt_archive_2d):
     cvt_archive_heatmap(cvt_archive_2d, rasterized=True)
 
 
+@image_comparison(baseline_images=["plot_metadata_with_df"],
+                  remove_text=False,
+                  extensions=["png"],
+                  tol=CVT_IMAGE_TOLERANCE)
+def test_plot_metadata_with_df(cvt_archive_2d):
+    plt.figure(figsize=(8, 6))
+    df = cvt_archive_2d.as_pandas(include_metadata=True)
+    df["objective"] = df["metadata"]
+    cvt_archive_heatmap(cvt_archive_2d, df=df)
+
+
 #
 # Tests for `clip` parameter
 #
diff --git a/tests/visualize/grid_archive_heatmap_test.py b/tests/visualize/grid_archive_heatmap_test.py
index 3ff574e5c..35e939aa6 100644
--- a/tests/visualize/grid_archive_heatmap_test.py
+++ b/tests/visualize/grid_archive_heatmap_test.py
@@ -194,6 +194,16 @@ def test_rasterized(grid_archive_2d):
     grid_archive_heatmap(grid_archive_2d, rasterized=True)
 
 
+@image_comparison(baseline_images=["plot_metadata_with_df"],
+                  remove_text=False,
+                  extensions=["png"])
+def test_plot_metadata_with_df(grid_archive_2d):
+    plt.figure(figsize=(8, 6))
+    df = grid_archive_2d.as_pandas(include_metadata=True)
+    df["objective"] = df["metadata"]
+    grid_archive_heatmap(grid_archive_2d, df=df)
+
+
 #
 # 1D tests
 #
diff --git a/tests/visualize/parallel_axes_plot_test.py b/tests/visualize/parallel_axes_plot_test.py
index e7fef5287..faaf4033d 100644
--- a/tests/visualize/parallel_axes_plot_test.py
+++ b/tests/visualize/parallel_axes_plot_test.py
@@ -86,3 +86,13 @@ def test_3d_sorted(grid_archive_3d):
 def test_3d_vertical_cbar(grid_archive_3d):
     plt.figure(figsize=(8, 6))
     parallel_axes_plot(grid_archive_3d, cbar_kwargs={"orientation": "vertical"})
+
+
+@image_comparison(baseline_images=["plot_metadata_with_df"],
+                  remove_text=False,
+                  extensions=["png"])
+def test_plot_metadata_with_df(grid_archive_3d):
+    plt.figure(figsize=(8, 6))
+    df = grid_archive_3d.as_pandas(include_metadata=True)
+    df["objective"] = df["metadata"]
+    parallel_axes_plot(grid_archive_3d, df=df)
diff --git a/tests/visualize/sliding_boundaries_archive_heatmap_test.py b/tests/visualize/sliding_boundaries_archive_heatmap_test.py
index 2efedf95c..d92e3042c 100644
--- a/tests/visualize/sliding_boundaries_archive_heatmap_test.py
+++ b/tests/visualize/sliding_boundaries_archive_heatmap_test.py
@@ -29,10 +29,12 @@ def add_random_sphere(archive, x_range, y_range):
         (x_range[1], y_range[1]),
         (1000, 2),
     )
+    sphere = np.sum(np.square(solutions), axis=1)
     archive.add(
         solution_batch=solutions,
-        objective_batch=-np.sum(np.square(solutions), axis=1),
+        objective_batch=-sphere,
         measures_batch=solutions,
+        metadata_batch=sphere,
     )
 
 
@@ -158,3 +160,13 @@ def test_rasterized(sliding_archive_2d):
     sliding_boundaries_archive_heatmap(sliding_archive_2d,
                                        boundary_lw=1.0,
                                        rasterized=True)
+
+
+@image_comparison(baseline_images=["plot_metadata_with_df"],
+                  remove_text=False,
+                  extensions=["png"])
+def test_plot_metadata_with_df(sliding_archive_2d):
+    plt.figure(figsize=(8, 6))
+    df = sliding_archive_2d.as_pandas(include_metadata=True)
+    df["objective"] = df["metadata"]
+    sliding_boundaries_archive_heatmap(sliding_archive_2d, df=df)