diff --git a/bigframes/operations/_matplotlib/core.py b/bigframes/operations/_matplotlib/core.py
index 7cbeb3df4f..6f40ab5516 100644
--- a/bigframes/operations/_matplotlib/core.py
+++ b/bigframes/operations/_matplotlib/core.py
@@ -14,8 +14,12 @@
 
 import abc
 import typing
+import uuid
 
 import matplotlib.pyplot as plt
+import pandas as pd
+
+import bigframes.dtypes as dtypes
 
 
 class MPLPlot(abc.ABC):
@@ -38,12 +42,13 @@ def _kind(self):
 
     def __init__(self, data, **kwargs) -> None:
         self.kwargs = kwargs
-        self.data = self._compute_plot_data(data)
+        self.data = data
 
     def generate(self) -> None:
-        self.axes = self.data.plot(kind=self._kind, **self.kwargs)
+        plot_data = self._compute_plot_data()
+        self.axes = plot_data.plot(kind=self._kind, **self.kwargs)
 
-    def _compute_plot_data(self, data):
+    def _compute_sample_data(self, data):
         # TODO: Cache the sampling data in the PlotAccessor.
         sampling_n = self.kwargs.pop("sampling_n", 100)
         sampling_random_state = self.kwargs.pop("sampling_random_state", 0)
@@ -53,6 +58,9 @@ def _compute_plot_data(self, data):
             sort=False,
         ).to_pandas()
 
+    def _compute_plot_data(self):
+        return self._compute_sample_data(self.data)
+
 
 class LinePlot(SamplingPlot):
     @property
@@ -70,3 +78,56 @@ class ScatterPlot(SamplingPlot):
     @property
     def _kind(self) -> typing.Literal["scatter"]:
         return "scatter"
+
+    def __init__(self, data, **kwargs) -> None:
+        super().__init__(data, **kwargs)
+
+        c = self.kwargs.get("c", None)
+        if self._is_sequence_arg(c) and len(c) != self.data.shape[0]:
+            raise ValueError(
+                f"'c' argument has {len(c)} elements, which is "
+                + f"inconsistent with 'x' and 'y' with size {self.data.shape[0]}"
+            )
+
+    def _compute_plot_data(self):
+        data = self.data.copy()
+
+        c = self.kwargs.get("c", None)
+        c_id = None
+        if self._is_sequence_arg(c):
+            c_id = self._generate_new_column_name(data)
+            print(c_id)
+            data[c_id] = c
+
+        sample = self._compute_sample_data(data)
+
+        # Works around a pandas bug:
+        # https://github.com/pandas-dev/pandas/commit/45b937d64f6b7b6971856a47e379c7c87af7e00a
+        if self._is_column_name(c, sample) and sample[c].dtype == dtypes.STRING_DTYPE:
+            sample[c] = sample[c].astype("object")
+
+        if c_id is not None:
+            self.kwargs["c"] = sample[c_id]
+            sample = sample.drop(columns=[c_id])
+
+        return sample
+
+    def _is_sequence_arg(self, arg):
+        return (
+            arg is not None
+            and not isinstance(arg, str)
+            and isinstance(arg, typing.Iterable)
+        )
+
+    def _is_column_name(self, arg, data):
+        return (
+            arg is not None
+            and pd.core.dtypes.common.is_hashable(arg)
+            and arg in data.columns
+        )
+
+    def _generate_new_column_name(self, data):
+        col_name = None
+        while col_name is None or col_name in data.columns:
+            col_name = f"plot_temp_{str(uuid.uuid4())[:8]}"
+        return col_name
diff --git a/tests/system/small/operations/test_plotting.py b/tests/system/small/operations/test_plotting.py
index 47491cdada..117bcd2f8e 100644
--- a/tests/system/small/operations/test_plotting.py
+++ b/tests/system/small/operations/test_plotting.py
@@ -208,6 +208,67 @@ def test_scatter(scalars_dfs):
     )
 
 
+@pytest.mark.parametrize(
+    ("c"),
+    [
+        pytest.param("red", id="red"),
+        pytest.param("c", id="int_column"),
+        pytest.param("species", id="color_column"),
+        pytest.param(["red", "green", "blue"], id="color_sequence"),
+        pytest.param([3.4, 5.3, 2.0], id="number_sequence"),
+        pytest.param(
+            [3.4, 5.3],
+            id="length_mismatches_sequence",
+            marks=pytest.mark.xfail(
+                raises=ValueError,
+            ),
+        ),
+    ],
+)
+def test_scatter_args_c(c):
+    data = {
+        "a": [1, 2, 3],
+        "b": [1, 2, 3],
+        "c": [1, 2, 3],
+        "species": ["r", "g", "b"],
+    }
+    df = bpd.DataFrame(data)
+    pd_df = pd.DataFrame(data)
+
+    ax = df.plot.scatter(x="a", y="b", c=c)
+    pd_ax = pd_df.plot.scatter(x="a", y="b", c=c)
+    assert len(ax.collections[0].get_facecolor()) == len(
+        pd_ax.collections[0].get_facecolor()
+    )
+    for idx in range(len(ax.collections[0].get_facecolor())):
+        tm.assert_numpy_array_equal(
+            ax.collections[0].get_facecolor()[idx],
+            pd_ax.collections[0].get_facecolor()[idx],
+        )
+
+
+def test_scatter_args_c_sampling():
+    data = {
+        "plot_temp_0": [1, 2, 3, 4, 5],
+        "plot_temp_1": [5, 4, 3, 2, 1],
+    }
+    c = ["red", "green", "blue", "orange", "black"]
+
+    df = bpd.DataFrame(data)
+    pd_df = pd.DataFrame(data)
+
+    ax = df.plot.scatter(x="plot_temp_0", y="plot_temp_1", c=c, sampling_n=3)
+    pd_ax = pd_df.plot.scatter(x="plot_temp_0", y="plot_temp_1", c=c)
+    assert len(ax.collections[0].get_facecolor()) == len(
+        pd_ax.collections[0].get_facecolor()
+    )
+    for idx in range(len(ax.collections[0].get_facecolor())):
+        tm.assert_numpy_array_equal(
+            ax.collections[0].get_facecolor()[idx],
+            pd_ax.collections[0].get_facecolor()[idx],
+        )
+
+
 def test_sampling_plot_args_n():
     df = bpd.DataFrame(np.arange(1000), columns=["one"])
     ax = df.plot.line()