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matplotlibmarkerscatter

Matplotlib--scatter plot with half filled markers

Question: Using a scatter plot in matplotlib, is there a simple way get a half-filled marker?

I know half-filled markers can easily be done using a line plot, but I would like to use 'scatter' because I want to use marker size and color (i.e., alternate marker face color) to represent other data. (I believe this will be easier with a scatter plot since I want to automate making a large number of plots from a large data set.)

I can't seem to make half-filled markers properly using a scatter plot. That is to say, instead of a half-filled marker, the plot shows half of a marker. I've been using matplotlib.markers.MarkerStyle, but that seems to only get me halfway there. I'm able to get following output using the code below.

enter image description here

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle

plt.scatter(1, 1, marker=MarkerStyle('o', fillstyle='full'), edgecolors='k', s=500)
plt.scatter(2, 2, marker=MarkerStyle('o', fillstyle='left'), edgecolors='k', s=500)
plt.scatter(3, 3, marker=MarkerStyle('o', fillstyle='right'), edgecolors='k', s=500)
plt.scatter(4, 4, marker=MarkerStyle('o', fillstyle='top'), edgecolors='k', s=500)
plt.scatter(5, 5, marker=MarkerStyle('o', fillstyle='bottom'), edgecolors='k', s=500)

plt.show()
Solution

  • As mentioned in the comments, I don't see why you have to use plt.scatter but if you want to, you can fake a combined marker:

    from matplotlib.markers import MarkerStyle
from matplotlib import pyplot as plt

#data generation
import pandas as pd
import numpy as np
np.random.seed(123)
n = 10
df = pd.DataFrame({"X": np.random.randint(1, 20, n), 
      "Y": np.random.randint(10, 30, n), 
      "S": np.random.randint(50, 500, n), 
      "C1": np.random.choice(["red", "blue", "green"], n),
      "C2": np.random.choice(["yellow", "grey"], n)})

fig, ax = plt.subplots()

ax.scatter(df.X, df.Y, s=df.S, c=df.C1, edgecolor="black", marker=MarkerStyle("o", fillstyle="right"))
ax.scatter(df.X, df.Y, s=df.S, c=df.C2, edgecolor="black", marker=MarkerStyle("o", fillstyle="left"))
    
plt.show()

    Sample output: enter image description here

    This works, of course, also for continuous data:

    from matplotlib import pyplot as plt
from matplotlib.markers import MarkerStyle

import pandas as pd
import numpy as np
np.random.seed(123)
n = 10
df = pd.DataFrame({"X": np.random.randint(1, 20, n), 
      "Y": np.random.randint(10, 30, n), 
      "S": np.random.randint(100, 1000, n), 
      "C1": np.random.randint(1, 100, n),
      "C2": np.random.random(n)})

fig, ax = plt.subplots(figsize=(10,8))

im1 = ax.scatter(df.X, df.Y, s=df.S, c=df.C1, edgecolor="black", marker=MarkerStyle("o", fillstyle="right"), cmap="autumn")
im2 = ax.scatter(df.X, df.Y, s=df.S, c=df.C2, edgecolor="black", marker=MarkerStyle("o", fillstyle="left"), cmap="winter")
cbar1 = plt.colorbar(im1, ax=ax)
cbar1.set_label("right half", rotation=90)
cbar2 = plt.colorbar(im2, ax=ax)
cbar2.set_label("left half", rotation=90)

plt.show()

    Sample output:

    enter image description here

    But be reminded that plt.plot with marker definitions might be faster for large-scale datasets: The plot function will be faster for scatterplots where markers don't vary in size or color.