Plot two histograms at the same time with matplotlib

问题

I created a histogram plot using data from a file and no problem. Now I wanted to superpose data from another file in the same histogram, so I do something like

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

but the problem is that for each interval, only the bar with the highest value appears, and the other is hidden. I wonder how could I plot both histograms at the same time with different colors.

回答1:

Here you have a working example:

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()

回答2:

The accepted answers gives the code for a histogram with overlapping bars, but in case you want each bar to be side-by-side (as I did), try the variation below:

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()

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

EDIT [2018/03/16]: Updated to allow plotting of arrays of different sizes, as suggested by @stochastic_zeitgeist

回答3:

In the case you have different sample sizes, it may be difficult to compare the distributions with a single y-axis. For example:

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()

In this case, you can plot your two data sets on different axes. To do so, you can get your histogram data using matplotlib, clear the axis, and then re-plot it on two separate axes (shifting the bin edges so that they don't overlap):

#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()

回答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:

回答5:

Here is a simple method to plot two histograms, with their bars side-by-side, on the same plot when the data has different sizes:

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()

回答6:

You should use bins from the values returned by 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)

回答7:

It sounds like you might want just a bar graph:

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

Alternatively, you can use subplots.

回答8:

Just in case you have pandas (import pandas as pd) or are ok with using it:

test = pd.DataFrame([[random.gauss(3,1) for _ in range(400)], 
                     [random.gauss(4,2) for _ in range(400)]])
plt.hist(test.values.T)
plt.show()

回答9:

There is one caveat when you want to plot the histogram from a 2-d numpy array. You need to swap the 2 axes.

import numpy as np
import matplotlib.pyplot as plt

data = np.random.normal(size=(2, 300))
# swapped_data.shape == (300, 2)
swapped_data = np.swapaxes(x, axis1=0, axis2=1)
plt.hist(swapped_data, bins=30, label=['x', 'y'])
plt.legend()
plt.show()

回答10:

This question has been answered before, but wanted to add another quick/easy workaround that might help other visitors to this question.

import seasborn as sns 
sns.kdeplot(mydata1)
sns.kdeplot(mydata2)

Some helpful examples are here for kde vs histogram comparison.

回答11:

Inspired by Solomon's answer, but to stick with the question, which is related to histogram, a clean solution is:

sns.distplot(bar)
sns.distplot(foo)
plt.show()

Make sure to plot the taller one first, otherwise you would need to set plt.ylim(0,0.45) so that the taller histogram is not chopped off.

来源：https://stackoverflow.com/questions/6871201/plot-two-histograms-at-the-same-time-with-matplotlib