多个直方图，每个直方图对应一个 x 轴的标签，在同一个图 matplotlib 上 [英] Multiple Histograms, each for a label of x-axis, on the same graph matplotlib

问题描述

我正在尝试绘制一个图表，以显示男性和女性针对不同年龄组的特定活动的不同行为.

所以，如果年龄组是:['1-10','11-20','21-30'...]我想为每个年龄组(每个年龄范围将是 x 轴上的标签)绘制直方图，用于男性和女性进行活动.我知道如何在一个图中绘制两个直方图，但我不知道如何并行绘制多个直方图，尤其是当每个直方图都针对给定的 x 标签时.

有人可以帮忙吗?

解决方案

我不确定您是否希望在一个图中两个直方图，但如果我生成一些随机数据:

将 numpy 导入为 np导入 matplotlib.pyplot 作为 pltage = ['{}-{}'.format(i*10, (i+1)*10) for i in range(10)]男性 = np.random.randint(0,100,10)女性 = np.random.randint(0,100,10)

如果您需要根据某些数据手动创建直方图，您可以使用 numpy 而不是 matplotlib 直方图(male_data 和 female_data 是什么你会插入 plt.hist()):

bins = [i*10 for i in range(11)] # = [0,10,20,30,40,50,60,70,80,90,100]男性，_ = np.histogram(male_data, bins=bins)女性，_ = np.histogram(female_data, bins=bins)

然后将其绘制为 bar 图(我从

或者你想用共享 y 轴来分隔图?

fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)ax1.bar(np.arange(10)-0.3, 100*males/np.sum(males), width=0.6, color='b', label='male')ax2.bar(np.arange(10)-0.3, 100*females/np.sum(females), width=0.6, color='r', label='female')对于 i in (ax1, ax2):getattr(i, 'set_xticks')(np.arange(10))getattr(i, 'set_xticklabels')(age)getattr(i, 'set_xlabel')('年龄范围')getattr(i, 'set_ylabel')('做这件事的人(百分比)')getattr(i, 'set_xlim')(-0.5,9.5)plt.show()

在第二个示例中，您可能需要减小文本大小，以便正确显示年龄范围...

I am trying to plot a graph to show different behaviors by males and females with regards to a certain activity, for different age-groups.

So, if the age groups are: ['1-10','11-20','21-30'...] I would like a to plot a histogram for each age-group (every age-range would be a label on x-axis), for males and females doing the activity. I know how to plot two histograms together in one graph, but I don't know how to plot many in parallel, especially when each histogram is for a given x-label.

Could someone please help?

解决方案

I'm not sure if you want both of the histograms in one plot, but if I generate some random data:

import numpy as np
import matplotlib.pyplot as plt
age = ['{}-{}'.format(i*10, (i+1)*10) for i in range(10)]
males = np.random.randint(0,100,10)
females = np.random.randint(0,100,10)

If you need to create your histograms manually from some data you can use numpy instead of matplotlib histogram (the male_data and female_data is what you would have inserted into plt.hist()):

bins = [i*10 for i in range(11)] # = [0,10,20,30,40,50,60,70,80,90,100]
males , _ = np.histogram(male_data, bins=bins)
females , _ = np.histogram(female_data, bins=bins)

and then plot it as bar plot (I've adapted some of it from the matplotlib examples page)I get something that might be what you want:

fig, ax = plt.subplots()
# Normalize the counts by dividing it by the sum:
ax.bar(np.arange(10)-0.15, males/np.sum(males), width=0.1, color='b', label='male')
ax.bar(np.arange(10)+0.05, females/np.sum(females), width=0.1, color='r', label='female')
ax.set_xticks(np.arange(10))
ax.set_xticklabels(age)
ax.legend()
ax.set_xlim(-0.5,9.5)
plt.show()

or do you want to seperate plots with a shares y-axis?

fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.bar(np.arange(10)-0.3, 100*males/np.sum(males), width=0.6, color='b', label='male')
ax2.bar(np.arange(10)-0.3, 100*females/np.sum(females), width=0.6, color='r', label='female')
for i in (ax1, ax2):
    getattr(i, 'set_xticks')(np.arange(10))
    getattr(i, 'set_xticklabels')(age)
    getattr(i, 'set_xlabel')('Age range')
    getattr(i, 'set_ylabel')('People doing it (in percent)')
    getattr(i, 'set_xlim')(-0.5,9.5)
plt.show()

In the second example you might need to decrease the text size so that the age ranges are properly shown...

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