使用matplotlib同时绘制两个直方图

Question

我使用文件中的数据创建了直方图,没有问题.现在我想在同一个直方图中叠加来自另一个文件的数据,所以我做了类似的事情

n,bins,patchs = ax.hist(mydata1,100)
n,bins,patchs = ax.hist(mydata2,100)

但问题是,对于每个区间,只显示具有最高值的条,而另一个区间是隐藏的.我想知道如何用不同的颜色同时绘制两个直方图.

Answer 1

这里有一个有效的例子:

import random
import numpy
from matplotlib import pyplot

x = [random.gauss(3,1) for _ in range(400)]
y = [random.gauss(4,2) for _ in range(400)]

bins = numpy.linspace(-10, 10, 100)

pyplot.hist(x, bins, alpha=0.5, label='x')
pyplot.hist(y, bins, alpha=0.5, label='y')
pyplot.legend(loc='upper right')
pyplot.show()

Answer 2

接受的答案给出了具有重叠条形的直方图的代码,但是如果您希望每个条形并排(就像我一样),请尝试以下变体:

import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-deep')

x = np.random.normal(1, 2, 5000)
y = np.random.normal(-1, 3, 2000)
bins = np.linspace(-10, 10, 30)

plt.hist([x, y], bins, label=['x', 'y'])
plt.legend(loc='upper right')
plt.show()

参考:http://matplotlib.org/examples/statistics/histogram_demo_multihist.html

编辑[2018/03/16]:已更新,允许绘制不同大小的数组,如@stochastic_zeitgeist所示

Answer 3

如果您的样本量不同,则可能难以将分布与单个y轴进行比较.例如:

import numpy as np
import matplotlib.pyplot as plt

#makes the data
y1 = np.random.normal(-2, 2, 1000)
y2 = np.random.normal(2, 2, 5000)
colors = ['b','g']

#plots the histogram
fig, ax1 = plt.subplots()
ax1.hist([y1,y2],color=colors)
ax1.set_xlim(-10,10)
ax1.set_ylabel("Count")
plt.tight_layout()
plt.show()

在这种情况下,您可以在不同的轴上绘制两个数据集.为此,您可以使用matplotlib获取直方图数据,清除轴,然后在两个不同的轴上重新绘制它(移动bin边缘,使它们不重叠):

#sets up the axis and gets histogram data
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.hist([y1, y2], color=colors)
n, bins, patches = ax1.hist([y1,y2])
ax1.cla() #clear the axis

#plots the histogram data
width = (bins[1] - bins[0]) * 0.4
bins_shifted = bins + width
ax1.bar(bins[:-1], n[0], width, align='edge', color=colors[0])
ax2.bar(bins_shifted[:-1], n[1], width, align='edge', color=colors[1])

#finishes the plot
ax1.set_ylabel("Count", color=colors[0])
ax2.set_ylabel("Count", color=colors[1])
ax1.tick_params('y', colors=colors[0])
ax2.tick_params('y', colors=colors[1])
plt.tight_layout()
plt.show()

Answer 4

As a completion to Gustavo Bezerra's answer:

If you want each histogram to be normalized (normed for mpl<=2.1 and density for mpl>=3.1) you cannot just use normed/density=True, you need to set the weights for each value instead:

import numpy as np
import matplotlib.pyplot as plt

x = np.random.normal(1, 2, 5000)
y = np.random.normal(-1, 3, 2000)
x_w = np.empty(x.shape)
x_w.fill(1/x.shape[0])
y_w = np.empty(y.shape)
y_w.fill(1/y.shape[0])
bins = np.linspace(-10, 10, 30)

plt.hist([x, y], bins, weights=[x_w, y_w], label=['x', 'y'])
plt.legend(loc='upper right')
plt.show()

As a comparison, the exact same x and y vectors with default weights and density=True:

Answer 5

下面是一种简单的方法,当数据具有不同的大小时,在同一个图上绘制两个直方图,并将它们并排绘制:

def plotHistogram(p, o):
    """
    p and o are iterables with the values you want to 
    plot the histogram of
    """
    plt.hist([p, o], color=['g','r'], alpha=0.8, bins=50)
    plt.show()

Answer 6

您应该使用bins以下方法返回的值hist：

import numpy as np
import matplotlib.pyplot as plt

foo = np.random.normal(loc=1, size=100) # a normal distribution
bar = np.random.normal(loc=-1, size=10000) # a normal distribution

_, bins, _ = plt.hist(foo, bins=50, range=[-6, 6], normed=True)
_ = plt.hist(bar, bins=bins, alpha=0.5, normed=True)

Answer 7

绘制两个重叠的直方图（或更多）可能会导致绘图相当混乱。我发现使用阶梯直方图（又名空心直方图）可以大大提高可读性。唯一的缺点是，在 matplotlib 中，步进直方图的默认图例格式不正确，因此可以像以下示例一样进行编辑：

import numpy as np                   # v 1.19.2
import matplotlib.pyplot as plt      # v 3.3.2
from matplotlib.lines import Line2D

rng = np.random.default_rng(seed=123)

# Create two normally distributed random variables of different sizes
# and with different shapes
data1 = rng.normal(loc=30, scale=10, size=500)
data2 = rng.normal(loc=50, scale=10, size=1000)

# Create figure with 'step' type of histogram to improve plot readability
fig, ax = plt.subplots(figsize=(9,5))
ax.hist([data1, data2], bins=15, histtype='step', linewidth=2,
        alpha=0.7, label=['data1','data2'])

# Edit legend to get lines as legend keys instead of the default polygons
# and sort the legend entries in alphanumeric order
handles, labels = ax.get_legend_handles_labels()
leg_entries = {}
for h, label in zip(handles, labels):
    leg_entries[label] = Line2D([0], [0], color=h.get_facecolor()[:-1],
                                alpha=h.get_alpha(), lw=h.get_linewidth())
labels_sorted, lines = zip(*sorted(leg_entries.items()))
ax.legend(lines, labels_sorted, frameon=False)

# Remove spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Add annotations
plt.ylabel('Frequency', labelpad=15)
plt.title('Matplotlib step histogram', fontsize=14, pad=20)
plt.show()

正如您所看到的，结果看起来非常干净。当重叠两个以上直方图时，这尤其有用。根据变量的分布方式，这最多适用于大约 5 个重叠分布。不仅如此，还需要使用另一种类型的绘图，例如此处介绍的其中一种。

Answer 8

听起来您可能只想要一个条形图：

• http://matplotlib.sourceforge.net/examples/pylab_examples/bar_stacked.html
• http://matplotlib.sourceforge.net/examples/pylab_examples/barchart_demo.html

或者，您可以使用子图。