如何使用Matplotlib在Python中实现第Ⅹ次图像绘制？

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缩写+颜色名+缩写+颜色名+缩写+颜色名+缩写+颜色名+b+蓝色+g+绿色+r+红色+c+青色+m+青色+y+洋红色+k+黄色+w+白色+import+matplotlib+as+mpl+import+matplotlib.pyplot+as+plt+import+numpy+as+np+通过属性字典r

缩写

颜色名

缩写

颜色名

缩写

颜色名

缩写

颜色名

b

蓝色

g

绿色

r

红色

c

青色

m

洋红色

y

黄色

k

黑色

w

白色

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

通过属性字典rcParams调整字体属性值和文本属性值

def no1():
"""
通过属性字典rcParams调整字体属性值和文本属性值
:return:
"""
# line properties in change
plt.rcParams["lines.linewidth"] = 8.0
plt.rcParams["lines.linestyle"] = "--"

# font properties in change
plt.rcParams["font.family"] = "serif"
plt.rcParams["font.serif"] = "New Century Schoolbook"
plt.rcParams["font.style"] = "normal"
plt.rcParams["font.variant"] = "small-caps"
plt.rcParams["font.weight"] = "black"
plt.rcParams["font.size"] = 12.0

# text properties in change
plt.rcParams["text.color"] = "blue"

plt.axes([0.1, 0.1, .8, .8], frameon=True, fc='y', aspect='equal')
plt.plot(2 + np.arange(3), [0, 1, 0])
plt.title("Line Chart")

plt.text(2.25, .8, "FONT")

plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no1.png")
plt.show()

字体主要属性的可视化展示

def no2():
"""
字体主要属性的可视化展示
:return:
"""
fig = plt.figure()
ax = fig.add_subplot(111)
families = ["serif", "sans-serif", "fantasy", "monospace"]

ax.text(-1, 1, "family", fontsize=18, horizontalalignment='center')

pi = (0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1)

for i, family in enumerate(families):
ax.text(-1, pi[i], family, family=family, horizontalalignment='center')

sizes = ["xx-small", "x-small", "small", "medium", "large", "x-large",
"xx-large"]

ax.text(-0.5, 1, "size", fontsize=18, horizontalalignment="center")

for i, size in enumerate(sizes):
ax.text(-0.5, pi[i], size, size=size, horizontalalignment="center")

styles = ["normal", "italic", "oblique"]

ax.text(0, 1, "style", fontsize=18, horizontalalignment="center")

for i, style in enumerate(styles):
ax.text(0, pi[i], style, family="sans-serif", style=style,
horizontalalignment='center')

variants = ["normal", "small-caps"]

ax.text(0.5, 1, "variant", fontsize=18, horizontalalignment='center')

for i, variant in enumerate(variants):
ax.text(0.5, pi[i], variant, family="serif", variant=variant,
horizontalalignment='center')

weights = ["light", "normal", "semibold", "bold", "black"]
ax.text(1, 1, "weight", fontsize=18, horizontalalignment='center')

for i, weight in enumerate(weights):
ax.text(1, pi[i], weight, weight=weight,
horizontalalignment='center')

ax.axis([-1.5, 1.5, 0.1, 1.1])
ax.set_xticks([])
ax.set_yticks([])

plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no2.png")
plt.show()

模拟图的颜色使用模式

def no3():
"""
模拟图的颜色使用模式
:return:
"""
rd = np.random.rand(10, 10)
plt.pcolor(rd, cmap="BuPu")
plt.colorbar()
plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no3.png")
plt.show()

散点图的颜色使用模式

def no4():
"""
散点图的颜色使用模式
:return:
"""
a = np.random.randn(100)
b = np.random.randn(100)
exponent = 2

plt.subplot(131)
plt.scatter(
a,
b,
np.sqrt(
np.power(
a,
exponent) +
np.power(
b,
exponent)) *
100,
c=np.random.rand(100),
cmap=mpl.cm.jet,
marker='o',
zorder=1)
plt.subplot(132)
plt.scatter(a, b, 50, marker='o', zorder=10)

plt.subplot(133)
plt.scatter(a, b, 50, c=np.random.rand(100),
cmap=mpl.cm.BuPu,
marker='+',
zorder=100)

plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no4.png")
plt.show()

极区图的颜色使用模式

def no5():
"""
极区图的颜色使用模式
:return:
"""
barSlices = 12
theta = np.linspace(0.0, 2 * np.pi, barSlices, endpoint=False)
radii = 30 * np.random.rand(barSlices)
width = np.pi / 4 * np.random.rand(barSlices)

fig = plt.figure()
ax = fig.add_subplot(111, polar=True)

bars = ax.bar(theta, radii, width=width, bottom=0.0)

for r, bar in zip(radii, bars):
bar.set_facecolor(mpl.cm.Accent(r / 30.))
bar.set_alpha(r / 30.)

plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no5.png")
plt.show()

等高线的颜色使用模式

def no6():
"""
等高线的颜色使用模式
:return:
"""
s = np.linspace(-0.5, 0.5, 1000)

x, y = np.meshgrid(s, s)

fig, ax = plt.subplots(1, 1)

z = x**2 + y**2 + np.power(x**2 + y**2, 2)

cs = plt.contour(x, y, z, cmap=mpl.cm.hot)

plt.clabel(cs, fmt="%3.2f")

plt.colorbar(cs)

plt.savefig(r"E:\Programmer\PYTHON\Matplotlib实践\figure\Figure(Unit "
r"10)\no6.png")
plt.show()

• 本篇博文特别感谢刘大成的《Python数据可视化之matplotlib实践》