Matplotlib: avoiding overlapping datapoints in a “

问题:

When drawing a dot plot using matplotlib, I would like to offset overlapping datapoints to keep them all visible. For examples, if I have

CategoryA: 0,0,3,0,5  
CategoryB: 5,10,5,5,10  

I want each of the CategoryA "0" datapoints to be set side by side, rather than right on top of each other, while still remaining distinct from CategoryB.

In R (ggplot2) there is a "jitter" option that does this. Is there a similar option in matplotlib, or is there another approach that would lead to a similar result?

Edit: to clarify, the "beeswarm" plot in R is essentially what I have in mind, and pybeeswarm is an early but useful start at a matplotlib/Python version.

Edit: to add that Seaborn's Swarmplot, introduced in version 0.7, is an excellent implementation of what I wanted.

回答1:

Extending the answer by @user2467675, here's how I did it:

def rand_jitter(arr):
    stdev = .01*(max(arr)-min(arr))
    return arr + np.random.randn(len(arr)) * stdev

def jitter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs):
    return scatter(rand_jitter(x), rand_jitter(y), s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, verts=verts, hold=hold, **kwargs)

The stdev variable makes sure that the jitter is enough to be seen on different scales, but it assumes that the limits of the axes are 0 and the max value.

You can then call jitter instead of scatter.

回答2:

I used numpy.random to "scatter/beeswarm" the data along X-axis but around a fixed point for each category, and then basically do pyplot.scatter() for each category:

import matplotlib.pyplot as plt
import numpy as np

#random data for category A, B, with B "taller"
yA, yB = np.random.randn(100), 5.0+np.random.randn(1000)

xA, xB = np.random.normal(1, 0.1, len(yA)), 
         np.random.normal(3, 0.1, len(yB))

plt.scatter(xA, yA)
plt.scatter(xB, yB)
plt.show()

回答3:

One way to approach the problem is to think of each 'row' in your scatter/dot/beeswarm plot as a bin in a histogram:

data = np.random.randn(100)

width = 0.8     # the maximum width of each 'row' in the scatter plot
xpos = 0        # the centre position of the scatter plot in x

counts, edges = np.histogram(data, bins=20)

centres = (edges[:-1] + edges[1:]) / 2.
yvals = centres.repeat(counts)

max_offset = width / counts.max()
offsets = np.hstack((np.arange(cc) - 0.5 * (cc - 1)) for cc in counts)
xvals = xpos + (offsets * max_offset)

fig, ax = plt.subplots(1, 1)
ax.scatter(xvals, yvals, s=30, c='b')

This obviously involves binning the data, so you may lose some precision. If you have discrete data, you could replace:

counts, edges = np.histogram(data, bins=20)
centres = (edges[:-1] + edges[1:]) / 2.

with:

centres, counts = np.unique(data, return_counts=True)

---

An alternative approach that preserves the exact y-coordinates, even for continuous data, is to use a kernel density estimate to scale the amplitude of random jitter in the x-axis:

from scipy.stats import gaussian_kde

kde = gaussian_kde(data)
density = kde(data)     # estimate the local density at each datapoint

# generate some random jitter between 0 and 1
jitter = np.random.rand(*data.shape) - 0.5 

# scale the jitter by the KDE estimate and add it to the centre x-coordinate
xvals = 1 + (density * jitter * width * 2)

ax.scatter(xvals, data, s=30, c='g')
for sp in ['top', 'bottom', 'right']:
    ax.spines[sp].set_visible(False)
ax.tick_params(top=False, bottom=False, right=False)

ax.set_xticks([0, 1])
ax.set_xticklabels(['Histogram', 'KDE'], fontsize='x-large')
fig.tight_layout()

This second method is loosely based on how violin plots work. It still cannot guarantee that none of the points are overlapping, but I find that in practice it tends to give quite nice-looking results as long as there are a decent number of points (>20), and the distribution can be reasonably well approximated by a sum-of-Gaussians.

回答4:

Not knowing of a direct mpl alternative here you have a very rudimentary proposal:

from matplotlib import pyplot as plt
from itertools import groupby

CA = [0,4,0,3,0,5]  
CB = [0,0,4,4,2,2,2,2,3,0,5]  

x = []
y = []
for indx, klass in enumerate([CA, CB]):
    klass = groupby(sorted(klass))
    for item, objt in klass:
        objt = list(objt)
        points = len(objt)
        pos = 1 + indx + (1 - points) / 50.
        for item in objt:
            x.append(pos)
            y.append(item)
            pos += 0.04

plt.plot(x, y, 'o')
plt.xlim((0,3))

plt.show()

回答5:

Seaborn provides histogram-like categorical dot-plots through sns.swarmplot() and jittered categorical dot-plots via sns.stripplot():

import seaborn as sns

sns.set(style='ticks', context='talk')
iris = sns.load_dataset('iris')

sns.swarmplot('species', 'sepal_length', data=iris)
sns.despine()

sns.stripplot('species', 'sepal_length', data=iris, jitter=0.2)
sns.despine()

回答6:

Seaborn's swarmplot seems like the most apt fit for what you have in mind, but you can also jitter with Seaborn's regplot:

import seaborn as sns
iris = sns.load_dataset('iris')

sns.regplot(x='sepal_length',
            y='sepal_width',
            data=iris,
            fit_reg=False,  # do not fit a regression line
            x_jitter=0.1,  # could also dynamically set this with range of data
            y_jitter=0.1,
            scatter_kws={'alpha': 0.5})  # set transparency to 50%