🍦 Matplotlib是Python的一个低级图形绘制库，由John D.Hunter创建，大部分用Python编写，用作可视化实用程序。

1 Matplotlib

Matplotlib
- 命令窗口下使用pip install Matplotlib进行安装。
- 通过import关键字将其导入应用程序：import matplotlib。
- 检查Matplotlib版本，版本字符串存储在__version__属性下。
- 大多数实用程序位于Pyplot子模块下，通常使用别名plt导入：import matplotlib.pyplot as plt。

import matplotlib
import numpy as np
import matplotlib.pyplot as plt                 # Pyplot子模块

print(matplotlib.__version__)                   # 检查Matplotlib版本

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])
plt.plot(xpoints, ypoints)
plt.show()

2 绘图操作

绘图操作
- plot()函数用于在图表中绘制点，默认情况下，是从一点到另一点绘制一条线。
- 采用参数指定图中的点，参数一包含x轴上点的数组，参数二包含y轴上点的数组。
- x轴是水平轴，y轴是垂直轴。

import numpy as np
import matplotlib.pyplot as plt

xpoints = np.array([1, 8])                       # 在图中从位置(1, 3)到位置(8, 10)画一条线
ypoints = np.array([3, 10])

plt.plot(xpoints, ypoints)
plt.show()

2-1 无线绘图

import numpy as np
import matplotlib.pyplot as plt

xpoints = np.array([1, 8])                       # 在图中从位置(1, 3)到位置(8, 10)画一条线
ypoints = np.array([3, 10])

plt.plot(xpoints, ypoints, "o")                  # 仅绘制标记，可以使用快捷字符串符号参数“o”
plt.show()

2-2 多点绘图

import numpy as np
import matplotlib.pyplot as plt

xpoints = np.array([1, 2, 6, 8])                 # 绘制任意多的点，确保两个轴上的点数相同
ypoints = np.array([3, 8, 1, 10])                # 在图中画一条线，从位置(1, 3)到(2, 8)，(6, 1)，最后到(8, 10)

plt.plot(xpoints, ypoints)
plt.show()

2-3 默认X轴点

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10, 5, 7])

plt.plot(ypoints)                                # x轴上的点默认值取决于y点的长度
plt.show()

3 使用标记

使用标记
- 使用关键字参数marker来强调带有指定标记的每个点。
- 标记参考
  - '*'、','、'x'、'X'、'+'、'P'、's'、'D'、'd'、'p'、'H'
  - 'h'、'v'、'^'、'<'、'>'、'1'、'2'、'3'、'4'、'|'、'_'

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o")                    # 关键字参数marker来强调带有指定标记的每个点
plt.show()

3-1 fmt

fmt
- 快捷字符串表示法参数，marker|line|color。
- 画线参考：'-'即solid、':'即dotted、'--'即dashed、'-.'即dashdot。
- 关键字参数linestyle或ls更改绘制线的样式，若在fmt中省略线值，则不会绘制任何线。
- 颜色参考：'r'、'g'、'b'、'c'、'm'、'y'、'k'、'w'。
- 关键字参数color或c设置线条的颜色，支持使用十六进制颜色值和140种支持的颜色名。
- 使用关键字参数linewidth或lw来更改设置线条的宽度，该值是一个浮点数，以点为单位。

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, "o:r")                         # 颜色标记
plt.show()

(1) 点虚线

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, linestyle="dotted")            # 关键字参数linestyle或ls更改绘制线的样式
plt.show()

(2) 长虚线

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, linestyle="dashed")            # 关键字参数linestyle或ls更改绘制线的样式
plt.show()

(3) 线条颜色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, color="g")                     # 关键字参数color或c设置线条的颜色
plt.show()

(4) 十六进制颜色值

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, c="#4CAF50")                   # 十六进制颜色值
plt.show()

(5) 140种支持的颜色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, c="hotpink")                   # 140种支持的颜色
plt.show()

(6) 更改设置线条行宽

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, linewidth="20.5")              # 关键字参数linewidth或lw更改设置线条的宽度
plt.show()

3-2 标记大小

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20)             # 关键字参数markersize或ms来设置标记的大小
plt.show()

3-3 标记颜色

标记颜色
- 使用关键字参数markeredgecolor或mec来设置标记的边缘外颜色。
- 使用关键字参数markerfacecolor或mfc来设置标记的边缘内颜色。
- 参见：140种支持的颜色名称。

(1) 边缘外颜色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20, mec="r")    # 关键字参数markeredgecolor或mec来设置标记的颜色
plt.show()

(2) 边缘内颜色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20, mfc="r")    # 关键字参数markerfacecolor或mfc来设置标记的颜色
plt.show()

(3) mec和mfc着色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20, mec="r", mfc="r")
plt.show()

(4) 十六进制颜色值

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20, mec="#4CAF50", mfc="#4CAF50")
plt.show()

(5) 140种支持的颜色

import numpy as np
import matplotlib.pyplot as plt

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker="o", ms=20, mec="hotpink", mfc="hotpink")
plt.show()

3-4 多线绘制

多线绘制
- 通过plt.plot()函数简单地绘制任意多条线。
- 通过同一函数中为每条线添加x和y轴的点绘制。

(1) 指定绘制

import numpy as np
import matplotlib.pyplot as plt

y1 = np.array([3, 8, 1, 10])
y2 = np.array([6, 2, 7, 11])

plt.plot(y1)                                    # 为每条线指定一个函数进行绘制
plt.plot(y2)

plt.show()

(2) x和y轴点

import numpy as np
import matplotlib.pyplot as plt

x1 = np.array([0, 1, 2, 3])                     # 指定两条线的x和y点值进行绘制
y1 = np.array([3, 8, 1, 10])
x2 = np.array([0, 1, 2, 3])
y2 = np.array([6, 2, 7, 11])

plt.plot(x1, y1, x2, y2)
plt.show()

4 添加标签

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.plot(x, y)

plt.xlabel("Average Pulse")                     # 向x轴和y轴添加标签
plt.ylabel("Calorie Burnage")

plt.show()

4-1 标题

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.plot(x, y)

plt.title("Sports Watch Data")                  # 为x轴和y轴添加标题
plt.xlabel("Average Pulse")                     # 向x轴和y轴添加标签
plt.ylabel("Calorie Burnage")

plt.show()

4-2 字体属性

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

font1 = {"family": "serif", "color": "blue", "size": 20}
font2 = {"family": "serif", "color": "darkred", "size": 15}

plt.title("Sports Watch Data", fontdict=font1)  # 设置标题和标签的字体属性
plt.xlabel("Average Pulse", fontdict=font2)
plt.ylabel("Calorie Burnage", fontdict=font2)

plt.plot(x, y)
plt.show()

4-3 定位标题

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Sports Watch Data", loc="left")      # 合法值为left、right和center，默认值为center
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.plot(x, y)
plt.show()

5 添加网格

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.plot(x, y)
plt.grid()                                      # 添加网格线
plt.show()

5-1 x轴网格

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.plot(x, y)
plt.grid(axis="x")                              # x轴网格线
plt.show()

5-2 y轴网格

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.plot(x, y)
plt.grid(axis="y")                              # y轴网格线
plt.show()

5-3 网格线属性

import numpy as np
import matplotlib.pyplot as plt

x = np.array([80, 85, 90, 95, 100, 105, 110, 115, 120, 125])
y = np.array([240, 250, 260, 270, 280, 290, 300, 310, 320, 330])

plt.title("Sports Watch Data")
plt.xlabel("Average Pulse")
plt.ylabel("Calorie Burnage")

plt.plot(x, y)
plt.grid(color="green", linestyle="--", linewidth=0.5)
plt.show()

6 绘制子图

import numpy as np
import matplotlib.pyplot as plt

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(1, 2, 1)
plt.plot(x, y)                                  # 绘制2个图，plot 1，布局按行和列组织

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(1, 2, 2)
plt.plot(x, y)                                  # 绘制2个图，plot 2，第三个参数指当前图的索引

plt.show()

6-1 彼此绘制

import numpy as np
import matplotlib.pyplot as plt

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(2, 1, 1)
plt.plot(x, y)                                  # plot 1

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(2, 1, 2)
plt.plot(x, y)                                  # plot 2

plt.show()

6-2 绘制6个图

import numpy as np
import matplotlib.pyplot as plt

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(2, 3, 1)
plt.plot(x, y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(2, 3, 2)
plt.plot(x, y)

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(2, 3, 3)
plt.plot(x, y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(2, 3, 4)
plt.plot(x, y)

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(2, 3, 5)
plt.plot(x, y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(2, 3, 6)
plt.plot(x, y)

plt.show()

6-3 每图添加标题

import numpy as np
import matplotlib.pyplot as plt

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(1, 2, 1)
plt.plot(x, y)                                  # plot 1
plt.title("SALES")

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(1, 2, 2)
plt.plot(x, y)                                  # plot 2
plt.title("INCOME")

plt.show()

6-4 添加超极标题

import numpy as np
import matplotlib.pyplot as plt

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])
plt.subplot(1, 2, 1)
plt.plot(x, y)                                  # plot 1
plt.title("SALES")

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])
plt.subplot(1, 2, 2)
plt.plot(x, y)                                  # plot 2
plt.title("INCOME")

plt.suptitle("MY SHOP")                         # 超级标题
plt.show()

7 绘制图形

绘制图形
- 散点图：scatter()函数，需要两个相同长度的数组，颜色图参考Available ColorMaps。
- 条形图：bar()函数，使用width和height设置垂直条和水平条的宽度，默认值为0.8。
- 直方图：hist()函数，使用一个数字数组来创建直方图，该数组作为参数发送到函数中。
- 饼状图：pie()函数，第一个楔形的绘制从x轴开始(即起始角为0°开始)，并逆时针移动。
  - 楔形颜色可以使用十六进制颜色值、也可以使用140种支持的颜色名称。
  - 或'r'、'g'、'b'、'c'、'm'、'y'、'k'、'w'等快捷方式。

7-1 散点图

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])

plt.scatter(x, y)
plt.show()

(1) 比较图

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
plt.scatter(x, y)

x = np.array([2, 2, 8, 1, 15, 8, 12, 9, 7, 3, 11, 4, 7, 14, 12])
y = np.array([100, 105, 84, 105, 90, 99, 90, 95, 94, 100, 79, 112, 91, 80, 85])
plt.scatter(x, y)

plt.show()

(2) 设置颜色

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
plt.scatter(x, y, color="hotpink")

x = np.array([2, 2, 8, 1, 15, 8, 12, 9, 7, 3, 11, 4, 7, 14, 12])
y = np.array([100, 105, 84, 105, 90, 99, 90, 95, 94, 100, 79, 112, 91, 80, 85])
plt.scatter(x, y, color="#88c999")

plt.show()

(3) 给点上色

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
colors = np.array(
    ["red", "green", "blue", "yellow", "pink", "black", "orange",
     "purple", "beige", "brown", "gray", "cyan", "magenta"])

plt.scatter(x, y, c=colors)
plt.show()

(4) 使用颜色图

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])

plt.scatter(x, y, c=colors, cmap="viridis")
plt.show()

(5) 包含颜色图例

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
colors = np.array([0, 10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])

plt.scatter(x, y, c=colors, cmap="viridis")
plt.colorbar()
plt.show()

(6) 更改点的大小

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
sizes = np.array([20, 50, 100, 200, 500, 1000, 60, 90, 10, 300, 600, 800, 75])

plt.scatter(x, y, s=sizes)
plt.show()

(7) 调整点的透明度

import numpy as np
import matplotlib.pyplot as plt

x = np.array([5, 7, 8, 7, 2, 17, 2, 9, 4, 11, 12, 9, 6])
y = np.array([99, 86, 87, 88, 111, 86, 103, 87, 94, 78, 77, 85, 86])
sizes = np.array([20, 50, 100, 200, 500, 1000, 60, 90, 10, 300, 600, 800, 75])

plt.scatter(x, y, s=sizes, alpha=0.5)
plt.show()

(8) 颜色结合透明度

import numpy as np
import matplotlib.pyplot as plt

x = np.random.randint(100, size=100)
y = np.random.randint(100, size=100)
colors = np.random.randint(100, size=100)
sizes = 10 * np.random.randint(100, size=100)

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap="nipy_spectral")
plt.colorbar()
plt.show()

7-2 条形图

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.bar(x, y)
plt.show()

(1) 水平显示

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.barh(x, y)
plt.show()

(2) 条形颜色

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.bar(x, y, color="red")
plt.show()

(3) 十六进制颜色值

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.bar(x, y, color="#4CAF50")
plt.show()

(4) 140种支持的颜色

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.bar(x, y, color="hotpink")
plt.show()

(5) 设置垂直条的宽度

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.bar(x, y, width=0.1)
plt.show()

(6) 设置水平条的高度

import numpy as np
import matplotlib.pyplot as plt

x = np.array(["A", "B", "C", "D"])
y = np.array([3, 8, 1, 10])

plt.barh(x, y, height=0.1)
plt.show()

7-3 直方图

import numpy as np
import matplotlib.pyplot as plt

x = np.random.normal(170, 10, 250)

plt.hist(x)
plt.show()

7-4 饼状图

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])

plt.pie(y)
plt.show()

(1) 标签

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]

plt.pie(y, labels=labels)
plt.show()

(2) 起始角

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]

plt.pie(y, labels=labels, startangle=90)
plt.show()

(3) 楔形脱离

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]
explode = [0.2, 0, 0, 0]

plt.pie(y, labels=labels, explode=explode)
plt.show()

(4) 饼图阴影

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]
explode = [0.2, 0, 0, 0]

plt.pie(y, labels=labels, explode=explode, shadow=True)
plt.show()

(5) 楔形颜色

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]
colors = ["black", "hotpink", "b", "#4CAF50"]

plt.pie(y, labels=labels, colors=colors)
plt.show()

(6) 添加图例

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]

plt.pie(y, labels=labels)
plt.legend()
plt.show()

(7) 图例标题

import numpy as np
import matplotlib.pyplot as plt

y = np.array([35, 25, 25, 15])
labels = ["Apples", "Bananas", "Cherries", "Dates"]

plt.pie(y, labels=labels)
plt.legend(title="Four Fruits:")
plt.show()

Python

#NumPy #Matplotlib #图形绘制

Python Matplotlib

https://stitch-top.github.io/2021/08/02/python/python09-python-matplotlib/

作者

Dr.626

发布于

2021年8月2日 23:45:26

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