ChatGPT解决这个技术问题 Extra ChatGPT

Plot two histograms on single chart with matplotlib

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 this

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.


j
joaquin

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()

https://i.stack.imgur.com/acUlv.png


Wouldn't it be a good idea to set pyplot.hold(True) before plotting, just in case?
Not sure if hold(True) is set in my matplotlib config params or pyplot behaves like this by default, but for me the code works as it is. The code is extracted from a bigger application which is not giving any problem so far. Anyway, good question I already made to myself when writing the code
@joaquin: how could I specify x to be blue and y to be red?
When I reproduced the plot with the edgecolor of the bars is None by default. If you want the same design as shown in the graph you can set the edgecolor parameter in both for example to k (black). The procedure is similar for the legend.
Even easier: pyplot.hist([x, y], bins, alpha=0.5, label=['x', 'y']).
G
Gustavo Bezerra

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()

https://i.stack.imgur.com/D8Ojb.png

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


@GustavoBezerra, how to use plt.hist to produce one pdf file for each histogram? I loaded my data using pandas.read_csv and the file has 36 columns and 100 lines. So I'd like 100 pdf files.
@Sigur That is quite off topic. Please Google or ask a new question. This seems to be related: stackoverflow.com/questions/11328958/…
@stochastic_zeitgeist I agree with @pasbi. I used your comment with a pandas dataframe because I needed different weights due to nans. with x=np.array(df.a) and y=np.array(df.b.dropna()) it basically ended up being plt.hist([x, y], weights=[np.ones_like(x)/len(x), np.ones_like(y)/len(y)])
In case your sample sizes are drastically different, you might want to plot using twin axes to better compare the distributions. See below.
@AgapeGal'lo Please refer to Andrew's answer.
A
Andrew

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()

https://i.stack.imgur.com/6iOYj.png

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()

https://i.stack.imgur.com/iayQ1.png


This is a nice brief answer except you should also add how to center the bars on each tick label
j
jojo

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()

https://i.stack.imgur.com/pEXV5.png

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

https://i.stack.imgur.com/gDJsL.png


A
Adrien Renaud

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)

https://i.stack.imgur.com/ATCHO.png


W
Ward Muylaert

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()

c
carl

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.


the difference is that with hist you get a frequency plotted. maybe you should show how to do it. frequency with pandas + bar plot = hist()
P
PV8

Also an option which is quite similar to joaquin answer:

import random
from matplotlib import pyplot

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

#plot both histograms(range from -10 to 10), bins set to 100
pyplot.hist([x,y], bins= 100, range=[-10,10], alpha=0.5, label=['x', 'y'])
#plot legend
pyplot.legend(loc='upper right')
#show it
pyplot.show()

Gives the following output:

https://i.stack.imgur.com/xzJl7.png


P
Patrick FitzGerald

Plotting two overlapping histograms (or more) can lead to a rather cluttered plot. I find that using step histograms (aka hollow histograms) improves the readability quite a bit. The only downside is that in matplotlib the default legend for a step histogram is not properly formatted, so it can be edited like in the following example:

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()

https://i.stack.imgur.com/OpYk5.png

As you can see, the result looks quite clean. This is especially useful when overlapping even more than two histograms. Depending on how the variables are distributed, this can work for up to around 5 overlapping distributions. More than that would require the use of another type of plot, such as one of those presented here.


s
serv-inc

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()

I believe using pandas will not work if the histograms to be compared have different sample sizes. This is also often the context in which normalized histograms are used.
S
Solomon Vimal

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.


S
Sarah

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.


A useful addition!
黄锐铭

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()

https://i.stack.imgur.com/k4kk3.png