diff --git a/src/textual/renderables/sparkline.py b/src/textual/renderables/sparkline.py
new file mode 100644
index 000000000..22bc959f7
--- /dev/null
+++ b/src/textual/renderables/sparkline.py
@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import statistics
+from typing import Sequence, Iterable, Callable, TypeVar
+
+from rich.color import Color
+from rich.console import ConsoleOptions, Console, RenderResult
+from rich.segment import Segment
+from rich.style import Style
+
+T = TypeVar("T", int, float)
+
+
+class Sparkline:
+    """A sparkline representing a series of data.
+
+    Args:
+        data (Sequence[T]): The sequence of data to render.
+        width (int, optional): The width of the sparkline/the number of buckets to partition the data into.
+        min_color (Color, optional): The color of values equal to the min value in data.
+        max_color (Color, optional): The color of values equal to the max value in data.
+        summary_function (Callable[list[T]]): Function that will be applied to each bucket.
+    """
+
+    BARS = "▁▂▃▄▅▆▇█"
+
+    def __init__(
+        self,
+        data: Sequence[T],
+        *,
+        width: int | None,
+        min_color: Color = Color.from_rgb(0, 255, 0),
+        max_color: Color = Color.from_rgb(255, 0, 0),
+        summary_function: Callable[[list[T]], float] = max,
+    ) -> None:
+        self.data = data
+        self.width = width
+        self.min_color = Style.from_color(min_color)
+        self.max_color = Style.from_color(max_color)
+        self.summary_function = summary_function
+
+    @classmethod
+    def _buckets(cls, data: Sequence[T], num_buckets: int) -> Iterable[list[T]]:
+        """Partition ``data`` into ``num_buckets`` buckets. For example, the data
+        [1, 2, 3, 4] partitioned into 2 buckets is [[1, 2], [3, 4]].
+
+        Args:
+            data (Sequence[T]): The data to partition.
+            num_buckets (int): The number of buckets to partition the data into.
+        """
+        num_steps, remainder = divmod(len(data), num_buckets)
+        for i in range(num_buckets):
+            start = i * num_steps + min(i, remainder)
+            end = (i + 1) * num_steps + min(i + 1, remainder)
+            partition = data[start:end]
+            if partition:
+                yield partition
+
+    def __rich_console__(
+        self, console: Console, options: ConsoleOptions
+    ) -> RenderResult:
+        width = self.width or options.max_width
+        len_data = len(self.data)
+        if len_data == 0:
+            yield Segment("▁" * width, style=self.min_color)
+            return
+        if len_data == 1:
+            yield Segment("█" * width, style=self.max_color)
+            return
+
+        minimum, maximum = min(self.data), max(self.data)
+        extent = maximum - minimum or 1
+
+        buckets = list(self._buckets(self.data, num_buckets=self.width))
+
+        bucket_index = 0
+        bars_rendered = 0
+        step = len(buckets) / width
+        summary_function = self.summary_function
+        min_color, max_color = self.min_color.color, self.max_color.color
+        while bars_rendered < width:
+            partition = buckets[int(bucket_index)]
+            partition_summary = summary_function(partition)
+            height_ratio = (partition_summary - minimum) / extent
+            bar_index = int(height_ratio * (len(self.BARS) - 1))
+            bar_color = _blend_colors(min_color, max_color, height_ratio)
+            bars_rendered += 1
+            bucket_index += step
+            yield Segment(text=self.BARS[bar_index], style=Style.from_color(bar_color))
+
+
+def _blend_colors(color1: Color, color2: Color, ratio: float) -> Color:
+    """Given two RGB colors, return a color that sits some distance between
+    them in RGB color space.
+
+    Args:
+        color1 (Color): The first color.
+        color2 (Color): The second color.
+        ratio (float): The ratio of color1 to color2.
+
+    Returns:
+        Color: A Color representing the blending of the two supplied colors.
+    """
+    r1, g1, b1 = color1.triplet
+    r2, g2, b2 = color2.triplet
+    dr = r2 - r1
+    dg = g2 - g1
+    db = b2 - b1
+    return Color.from_rgb(
+        red=r1 + dr * ratio, green=g1 + dg * ratio, blue=b1 + db * ratio
+    )
+
+
+if __name__ == "__main__":
+    console = Console()
+
+    def last(l):
+        return l[-1]
+
+    funcs = min, max, last, statistics.median, statistics.mean
+    nums = [10, 2, 30, 60, 45, 20, 7, 8, 9, 10, 50, 13, 10, 6, 5, 4, 3, 7, 20]
+    console.print(f"data = {nums}\n")
+    for f in funcs:
+        console.print(
+            f"{f.__name__}:\t", Sparkline(nums, width=12, summary_function=f), end=""
+        )
+        console.print("\n")

diff --git a/tests/renderables/test_sparkline.py b/tests/renderables/test_sparkline.py
new file mode 100644
index 000000000..74f1f2f6b
--- /dev/null
+++ b/tests/renderables/test_sparkline.py
@@ -0,0 +1,41 @@
+from tests.utilities.render import render
+from textual.renderables.sparkline import Sparkline
+
+GREEN = "\x1b[38;2;0;255;0m"
+RED = "\x1b[38;2;255;0;0m"
+BLENDED = "\x1b[38;2;127;127;0m"  # Color between red and green
+STOP = "\x1b[0m"
+
+
+def test_sparkline_no_data():
+    assert render(Sparkline([], width=4)) == f"{GREEN}▁▁▁▁{STOP}"
+
+
+def test_sparkline_single_datapoint():
+    assert render(Sparkline([2.5], width=4)) == f"{RED}████{STOP}"
+
+
+def test_sparkline_two_values_min_max():
+    assert render(Sparkline([2, 4], width=2)) == f"{GREEN}▁{STOP}{RED}█{STOP}"
+
+
+def test_sparkline_expand_data_to_width():
+    assert render(Sparkline([2, 4],
+                            width=4)) == f"{GREEN}▁{STOP}{GREEN}▁{STOP}{RED}█{STOP}{RED}█{STOP}"
+
+
+def test_sparkline_expand_data_to_width_non_divisible():
+    assert render(Sparkline([2, 4], width=3)) == f"{GREEN}▁{STOP}{GREEN}▁{STOP}{RED}█{STOP}"
+
+
+def test_sparkline_shrink_data_to_width():
+    assert render(Sparkline([2, 2, 4, 4, 6, 6], width=3)) == f"{GREEN}▁{STOP}{BLENDED}▄{STOP}{RED}█{STOP}"
+
+
+def test_sparkline_shrink_data_to_width_non_divisible():
+    assert render(
+        Sparkline([1, 2, 3, 4, 5], width=3, summary_function=min)) == f"{GREEN}▁{STOP}{BLENDED}▄{STOP}{RED}█{STOP}"
+
+
+def test_sparkline_color_blend():
+    assert render(Sparkline([1, 2, 3], width=3)) == f"{GREEN}▁{STOP}{BLENDED}▄{STOP}{RED}█{STOP}"

Sparkline renderable
Implement a renderable that generates a sparkline.

The input should be a list of floats of any size. The Sparkline renderable should use the same block characters as used in the scrollbar to render a minature graph.

```python
class Sparkline:
    def __init__(self, data: list[float], width:int | None):
        ...
```

Should look something like the following:

https://blog.jonudell.net/2021/08/05/the-tao-of-unicode-sparklines/

I think we should also color each bar by blending two colors.

Note that the width may not match the data length, so you may need to pick the closes data point.
tests/renderables/test_sparkline.py::test_sparkline_no_data tests/renderables/test_sparkline.py::test_sparkline_single_datapoint tests/renderables/test_sparkline.py::test_sparkline_two_values_min_max tests/renderables/test_sparkline.py::test_sparkline_expand_data_to_width tests/renderables/test_sparkline.py::test_sparkline_expand_data_to_width_non_divisible tests/renderables/test_sparkline.py::test_sparkline_shrink_data_to_width tests/renderables/test_sparkline.py::test_sparkline_shrink_data_to_width_non_divisible tests/renderables/test_sparkline.py::test_sparkline_color_blend