Version 3.1.1
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演示如何使用matplotlib绘制柱状图。
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
# Fixing random state for reproducibility
np.random.seed(19680801)
要生成一维柱状图，我们只需要一个数字向量。对于二维柱状图，我们需要第二个向量。我们将在下面生成这两个，并显示每个向量的柱状图。
N_points = 100000
n_bins = 20
# Generate a normal distribution, center at x=0 and y=5
x = np.random.randn(N_points)
y = .4 * x + np.random.randn(100000) + 5
fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)
# We can set the number of bins with the `bins` kwarg
axs[0].hist(x, bins=n_bins)
axs[1].hist(y, bins=n_bins)
柱状图方法返回 patches 对象。这使我们能够访问所绘制对象的属性。使用这个，我们可以根据自己的喜好编辑柱状图。让我们根据每个条形图的Y值更改其颜色。
fig, axs = plt.subplots(1, 2, tight_layout=True)
# N is the count in each bin, bins is the lower-limit of the bin
N, bins, patches = axs[0].hist(x, bins=n_bins)
# We'll color code by height, but you could use any scalar
fracs = N / N.max()
# we need to normalize the data to 0..1 for the full range of the colormap
norm = colors.Normalize(fracs.min(), fracs.max())
# Now, we'll loop through our objects and set the color of each accordingly
for thisfrac, thispatch in zip(fracs, patches):
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
# We can also normalize our inputs by the total number of counts
axs[1].hist(x, bins=n_bins, density=True)
# Now we format the y-axis to display percentage
axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
要绘制二维柱状图，一个只需要两个相同长度的向量，对应于柱状图的每个轴。
fig, ax = plt.subplots(tight_layout=True)
hist = ax.hist2d(x, y)
自定义二维柱状图类似于一维情况，可以控制可视组件，如纸槽大小或颜色规格化。
fig, axs = plt.subplots(3, 1, figsize=(5, 15), sharex=True, sharey=True,
tight_layout=True)
# We can increase the number of bins on each axis
axs[0].hist2d(x, y, bins=40)
# As well as define normalization of the colors
axs[1].hist2d(x, y, bins=40, norm=colors.LogNorm())
# We can also define custom numbers of bins for each axis
axs[2].hist2d(x, y, bins=(80, 10), norm=colors.LogNorm())
plt.show()
脚本的总运行时间： （0分1.428秒）