Python | 散点图

- 日理万妓 2023-03-05 08:30 246阅读 0赞

Inherited from the [Dot Plots][], **Scatter plots** are of very similar types. It provides a power of different features for every individual point. We are allowed to vary the size, color, and other properties of each data point and which makes data more friendly to visualize.

从继承[点图][Dot Plots] ， **散点图**是非常相似的类型。 它为每个单独的点提供了多种功能。 我们可以更改每个数据点的大小，颜色和其他属性，这使数据更易于可视化。

## 1)常规散点图 **(**1) Normal Scatter Plot **)** ##

**Syntax:**

**句法：**

plt.scatter(x, y, alpha=0.8)

**Parameter(s):**

**参数：**

*  x, y \- data points in an array
    
    x ， y-数组中的数据点
 *  Alpha = 0.8 \- opaque value (0 for transparent and 1 for opaque)
    
    Alpha = 0.8-不透明值(0表示透明，1表示不透明)

![scatter plot (1)][scatter plot _1]

## 2)可变大小的散点图 **(**2) Scatter Plot with variable size**)** ##

**Syntax:**

**句法：**

plt.scatter(x, y, s=area, alpha=0.8)

**Parameter(s):**

**参数：**

*  x, y \- data points in an array
    
    x ， y-数组中的数据点
 *  Alpha = 0.8 \- opaque value (0 for transparent and 1 for opaque)
    
    Alpha = 0.8-不透明值(0表示透明，1表示不透明)
 *  s = area \- the size of each point in terms of area
    
    s =面积 -每个点的面积(面积)

![scatter plot (2)][scatter plot _2]

## 3)带有颜色代码绿色，红色，蓝色黄色的短划线点图 **(**3) Dash Point Plots with color code Green, Red, Blue Yellow**)** ##

**Syntax:**

**句法：**

plt.scatter(x, y, c=colors, alpha=0.8)

**Parameter(s):**

**参数：**

*  x, y \- data points in an array
    
    x ， y-数组中的数据点
 *  Alpha = 0.8 \- opaque value (0 for transparent and 1 for opaque)
    
    Alpha = 0.8-不透明值(0表示透明，1表示不透明)
 *  c = colors \- colors of each point
    
    c =颜色 -每个点的颜色

![scatter plot (3)][scatter plot _3]

## 4)星点图，颜色代码为蓝色，绿色，红色，黄色 **(**4) Star Point Plot with Color Code Blue, Green, Red, Yellow**)** ##

**Syntax:**

**句法：**

plt.scatter(x, y, c=colors, s=area, alpha=0.8)

**Parameter(s):**

**参数：**

![scatter plot (4)][scatter plot _4]

**Applications:**

**应用范围：**

1.  Machine Learning
    
    机器学习
2.  Monte Carlo Simulation
    
    蒙特卡罗模拟
3.  Case Studies
    
    实例探究
4.  Probabilistic Models
    
    概率模型

## Python代码演示散点图示例 **(**Python code to demonstrate example of scatter plot**)** ##

import numpy as np
    import matplotlib.pyplot as plt
    
    N = 40
    x = np.random.rand(N)
    y = np.random.rand(N)*10
    
    # random colour for points, vector of length N
    colors = np.random.rand(N)
    
    # area of the circle, vectoe of length N
    area = (30 * np.random.rand(N))**2  
    # 0 to 15 point radii
    
    # a normal scatter plot with default features
    plt.scatter(x, y, alpha=0.8)
    plt.xlabel('Numbers')
    plt.ylabel('Values')
    plt.title('Normal Scatter Plot')
    plt.show()
    
    # a scater plot with different size
    plt.figure()
    plt.scatter(x, y, s=area, alpha=0.8)
    plt.xlabel('Numbers')
    plt.ylabel('Values')
    plt.title('Different Size')
    plt.show()
    
    # a scatter plot with different collour
    plt.figure()
    plt.scatter(x, y, c=colors, alpha=0.8)
    plt.xlabel('Numbers')
    plt.ylabel('Values')
    plt.title('Different Colour')
    plt.show()
    
    # A combined Scatter Plot
    plt.figure()
    plt.scatter(x, y, s=area, c=colors, alpha=0.8)
    plt.xlabel('Numbers')
    plt.ylabel('Values')
    plt.title('Combined')
    plt.show()

**Output:**

**输出：**

Output is as figure

> 翻译自: [https://www.includehelp.com/python/scatter-plot.aspx][https_www.includehelp.com_python_scatter-plot.aspx]

[Dot Plots]: https://www.includehelp.com/python/dot-plot.aspx
[scatter plot _1]: /images/20230208/bf7580c3f7a64487bd4442f3b78f5331.png
[scatter plot _2]: /images/20230208/2ee3f9a12390429f9cbd98d579d73929.png
[scatter plot _3]: /images/20230208/ac9f7bd4bf5940d5b89f3124e2893253.png
[scatter plot _4]: /images/20230208/6215f0e9a226490ab28a66e63c126084.png
[https_www.includehelp.com_python_scatter-plot.aspx]: https://www.includehelp.com/python/scatter-plot.aspx