Incorrect marker sizes with Seaborn relplot and sc

2020-06-30 03:46发布

I'm trying to understand how to get the legend examples to align with the dots plotted using Seaborn's relplot in a Jupyter notebook. I have a size (float64) column in my pandas DataFrame df:

sns.relplot(x="A", y="B", size="size", data=df)

The values in the size column are [0.0, -7.0, -14.0, -7.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 8.0, 2.0, 0.0, -4.0, 7.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, -3.0, 0.0, 1.0, 7.0] and as you can see, the minimum value is -14 and the maximum value is 8. It looks like the legend is aligned well with that. However, look at the actual dots plotted, there's a dot considerably smaller than the one corresponding to -16 in the legend. There's also no dot plotted as large as the 8 in the legend.

What am I doing wrong -- or is this a bug?

Example of sizes not occurring in the dataset

I'm using pandas 0.24.2 and seaborn 0.9.0.


Edit: Looking closer at the Seaborn relplot example:

relplot example

the smallest weight is 1613 but there's an orange dot to the far left in the plot that's smaller than the dot for 1500 in the legend. I think this points to this being a bug.

1条回答
来,给爷笑一个
2楼-- · 2020-06-30 04:21

Not sure what seaborn does here, but if you're willing to use matplotlib alone, it could look like

import numpy as np; np.random.rand
import matplotlib.pyplot as plt
import pandas as pd

s = [0.0, -7.0, -14.0, -7.0, 0.0, 1.0, 0.0, 0.0, 0.0, -1.0, 0.0, 8.0, 2.0, 
     0.0, -4.0, 7.0, -4.0, 0.0, 0.0, 4.0, 0.0, 0.0, -3.0, 0.0, 1.0, 7.0]
x = np.linspace(0, 2*np.pi, len(s))
y = np.sin(x)
df = pd.DataFrame({"A" : x, "B" : y, "size" : s})

# calculate some sizes in points^2 from the initial values
smin = df["size"].min()
df["scatter_sizes"] = 0.25 * (df["size"] - smin + 3)**2
# state the inverse of the above transformation
finv = lambda y: 2*np.sqrt(y)+smin-3

sc = plt.scatter(x="A", y="B", s="scatter_sizes", data=df)
plt.legend(*sc.legend_elements("sizes", func=finv), title="Size")

plt.show()

enter image description here

More details are in the Scatter plots with a legend example.

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