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pyplot.scatter reduce marker size

Here is an example of a scatter plot with high 2D point density, just for illustration.

How can I reduce the size of the markers to better distinguish the individual points? The size of the plot should remain as it is.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
from matplotlib import cm

np.random.seed(10)

n=3000

x = np.arange(n)
z = np.sin(x/n)
y = np.random.randint(0, 100, size=(n))

colvals = preprocessing.minmax_scale(z)

plt.scatter(x, y, color=cm.rainbow(colvals), marker='.')
plt.xlabel('x')
plt.ylabel('y')

plt.show()

Answer*

[`plt.scatter`] has a parameter `s` for controlling the marker size.

* `s` can either be a single float that applies to all points, e.g. all size 5:

```
    s = 5
    plt.scatter(x, y, color=cm.viridis(colvals), marker='.', s=s)
    ```

* Or `s` can be an array of sizes that maps to every point, e.g. size 5 when `z > 0.5` and size 30 otherwise:

```
    s = np.where(z > 0.5, 5, 30)
    plt.scatter(x, y, color=cm.viridis(colvals), marker='.', s=s)
    ```

---

Note that to match the size of scatter and plot markers, the plot marker's `markersize` should be the square root of the scatter marker's `s`:

```
s = 900

plt.scatter(-1, 0, s=s, color='tab:orange')
plt.plot(1, 0, 'o', markersize=s ** 0.5)
```

[`plt.scatter`]: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html

Edit Summary*

Cancel

Side note: I swapped out rainbow for viridis since the rainbow colormap should be avoided when possible.
– tdy
Commented Mar 19, 2022 at 0:02
1

Thanks for the link to the interesting paper, many researchers still use rainbow or monochrome color maps with increasing brightness.
– len
Commented Mar 21, 2022 at 7:48

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pyplot.scatter reduce marker size

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