一、引言

接着上一文章【数据可视化01】matplotlib实例介绍1继续介绍matplotlib的实例。

二、实例介绍

  在matplotlib中，常用的图形类型包括：

1. 箱线图（Box plot）：用于展示连续变量的分布情况、异常值和离群点。
2. 热力图（Heatmap）：用于展示两个类别型变量之间的关系，通过颜色深浅来表示不同数值的大小。
3. 3D图（3D plot）：用于展示三维数据的分布情况，可以通过不同颜色或形状来表示不同数值或类别。

  下面介绍集中常用的图形绘制方法。

1.箱线图

  该图说明了如何创建两种类型的框图(矩形和缺口)，以及如何通过访问框图的艺术家的属性来使用自定义颜色填充它们。此外，labels参数用于为每个样本提供x-tick标签。。

import matplotlib.pyplot as plt
import numpy as np# Random test data
np.random.seed(19680801)
all_data = [np.random.normal(0, std, size=100) for std in range(1, 4)]
labels = ['x1', 'x2', 'x3']fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))# rectangular box plot
bplot1 = ax1.boxplot(all_data,vert=True,  # vertical box alignmentpatch_artist=True,  # fill with colorlabels=labels)  # will be used to label x-ticks
ax1.set_title('Rectangular box plot')# notch shape box plot
bplot2 = ax2.boxplot(all_data,notch=True,  # notch shapevert=True,  # vertical box alignmentpatch_artist=True,  # fill with colorlabels=labels)  # will be used to label x-ticks
ax2.set_title('Notched box plot')# fill with colors
colors = ['pink', 'lightblue', 'lightgreen']
for bplot in (bplot1, bplot2):for patch, color in zip(bplot['boxes'], colors):patch.set_facecolor(color)# adding horizontal grid lines
for ax in [ax1, ax2]:ax.yaxis.grid(True)ax.set_xlabel('Three separate samples')ax.set_ylabel('Observed values')plt.show()

2.热力图

  通常需要将依赖于两个独立变量的数据显示为彩色编码图像图。这通常被称为热图。如果数据是分类的，则称为分类热图。Matplotlib的imshow函数使得生成这样的图特别容易。下面的示例展示了如何创建带有注释的热图。我们将从一个简单的示例开始，并将其扩展为可用的通用函数。

import matplotlib.pyplot as plt
import numpy as npimport matplotlib
import matplotlib as mplvegetables = ["cucumber", "tomato", "lettuce", "asparagus","potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening","Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],[2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],[1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],[0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],[0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],[1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],[0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])fig, ax = plt.subplots()
im = ax.imshow(harvest)# Show all ticks and label them with the respective list entries
ax.set_xticks(np.arange(len(farmers)), labels=farmers)
ax.set_yticks(np.arange(len(vegetables)), labels=vegetables)# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",rotation_mode="anchor")# Loop over data dimensions and create text annotations.
for i in range(len(vegetables)):for j in range(len(farmers)):text = ax.text(j, i, harvest[i, j],ha="center", va="center", color="w")ax.set_title("Harvest of local farmers (in tons/year)")
fig.tight_layout()
plt.show()

3.3D图

  演示一个基本的3D散点图。

import matplotlib.pyplot as plt
import numpy as np# Fixing random state for reproducibility
np.random.seed(19680801)def randrange(n, vmin, vmax):"""Helper function to make an array of random numbers having shape (n, )with each number distributed Uniform(vmin, vmax)."""return (vmax - vmin)*np.random.rand(n) + vminfig = plt.figure()
ax = fig.add_subplot(projection='3d')n = 100# For each set of style and range settings, plot n random points in the box
# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
for m, zlow, zhigh in [('o', -50, -25), ('^', -30, -5)]:xs = randrange(n, 23, 32)ys = randrange(n, 0, 100)zs = randrange(n, zlow, zhigh)ax.scatter(xs, ys, zs, marker=m)ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')plt.show()

$$\mathbf{E}\mathbf{N}\mathbf{D}!$$