我不清楚您想要什么，所以我想在这里…
您希望“概率/百分比”值是累积直方图吗？
因此，对于一个地块，您会有类似的东西吗？（如上所示，用标记绘制它，而不是更传统的阶梯图…）
import scipy.stats
import numpy as np
import matplotlib.pyplot as plt
# 100 values from a normal distribution with a std of 3 and a mean of 0.5
data = 3.0 * np.random.randn(100) + 0.5
counts, start, dx, _ = scipy.stats.cumfreq(data, numbins=20)
x = np.arange(counts.size) * dx + start
plt.plot(x, counts, 'ro')
plt.xlabel('Value')
plt.ylabel('Cumulative Frequency')
plt.show()
如果这大致是单个图所需要的，则有多种方法可以在一个图形上绘制多个图。最简单的就是使用子图。
在这里，我们将生成一些数据集，并将它们绘制在具有不同符号的不同子图中。
import itertools
import scipy.stats
import numpy as np
import matplotlib.pyplot as plt
# Generate some data... (Using a list to hold it so that the datasets don't
# have to be the same length...)
numdatasets = 4
stds = np.random.randint(1, 10, size=numdatasets)
means = np.random.randint(-5, 5, size=numdatasets)
values = [std * np.random.randn(100) + mean for std, mean in zip(stds, means)]
# Set up several subplots
fig, axes = plt.subplots(nrows=1, ncols=numdatasets, figsize=(12,6))
# Set up some colors and markers to cycle through...
colors = itertools.cycle(['b', 'g', 'r', 'c', 'm', 'y', 'k'])
markers = itertools.cycle(['o', '^', 's', r'$\Phi$', 'h'])
# Now let's actually plot our data...
for ax, data, color, marker in zip(axes, values, colors, markers):
counts, start, dx, _ = scipy.stats.cumfreq(data, numbins=20)
x = np.arange(counts.size) * dx + start
ax.plot(x, counts, color=color, marker=marker,
markersize=10, linestyle='none')
# Next we'll set the varIoUs labels...
axes[0].set_ylabel('Cumulative Frequency')
labels = ['This', 'That', 'The Other', 'And Another']
for ax, label in zip(axes, labels):
ax.set_xlabel(label)
plt.show()
如果我们希望它看起来像一个连续的图，则可以将子图挤压在一起并关闭一些边界。只需在调用之前添加以下内容plt.show()
# Because we want this to look like a continuous plot, we need to hide the
# boundaries (a.k.a. "spines") and yticks on most of the subplots
for ax in axes[1:]:
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.yaxis.set_ticks([])
axes[0].spines['right'].set_color('none')
# To reduce clutter, let's leave off the first and last x-ticks.
for ax in axes:
xticks = ax.get_xticks()
ax.set_xticks(xticks[1:-1])
# Now, we'll "scrunch" all of the subplots together, so that they look like one
fig.subplots_adjust(wspace=0)
希望这可以有所帮助！
编辑：如果您想要百分位值，而不是累积直方图（我真的不应该使用100作为样本量！），这很容易做到。
只需执行以下操作即可（使用numpy.percentile而不是手动进行标准化）：
# Replacing the for loop from before...
plot_percentiles = range(0, 110, 10)
for ax, data, color, marker in zip(axes, values, colors, markers):
x = np.percentile(data, plot_percentiles)
ax.plot(x, plot_percentiles, color=color, marker=marker,
markersize=10, linestyle='none')
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