Python可视化函数plt.scatter详解
作者：无水先生
这篇文章主要介绍了Python可视化函数plt.scatter详解, 关于matplotlib的scatter函数有许多活动参数，如果不专门注解，是无法掌握精髓的，本文专门针对scatter的参数和调用说起，并配有若干案例，需要的朋友可以参考下
一、说明
关于matplotlib的scatter函数有许多活动参数，如果不专门注解，是无法掌握精髓的，本文专门针对scatter的参数和调用说起，并配有若干案例。
二、函数和参数详解
2.1 scatter函数原型
matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)
2.2 参数详解
| 属性 | 参数 | 意义 |
| 坐标 | x，y | 输入点列的数组，长度都是size |
| 点大小 | s | 点的直径数组，默认直径20，长度最大size |
| 点颜色 | c | 点的颜色，默认蓝色 'b'，也可以是个 RGB 或 RGBA 二维行数组。 |
| 点形状 | marker | 点的样式，默认小圆圈 'o'。 |
| 调色板 | cmap | Colormap，默认 None，标量或者是一个 colormap 的名字，只有 c 是一个浮点数数组时才使用。如果没有申明就是 image.cmap。 |
| 亮度（1） | norm | Normalize，默认 None，数据亮度在 0-1 之间，只有 c 是一个浮点数的数组的时才使用。 |
| 亮度（2） | vmin，vmax | 亮度设置，在 norm 参数存在时会忽略。 |
| 透明度 | alpha | 透明度设置，0-1 之间，默认 None，即不透明 |
| 线 | linewidths | 标记点的长度 |
| 颜色 | edgecolors | 颜色或颜色序列，默认为 'face'，可选值有 'face', 'none', None。 |
plotnonfinite | 布尔值，设置是否使用非限定的 c ( inf, -inf 或 nan) 绘制点。 | |
**kwargs | 其他参数。 |
2.3 其中散点的形状参数marker如下：
2.4 其中颜色参数c如下:
三、画图示例
3.1 关于坐标x，y和s，c
import numpy as np import matplotlib.pyplot as plt # Fixing random state for reproducibility np.random.seed(19680801) N = 50 x = np.random.rand(N) y = np.random.rand(N) colors = np.random.rand(N) # 颜色可以随机 area = (30 * np.random.rand(N))**2 # 点的宽度30，半径15 plt.scatter(x, y, s=area, c=colors, alpha=0.5) plt.show()
注意：以上核心语句是：
plt.scatter(x, y, s=area, c=colors, alpha=0.5, marker=",")
其中：x，y，s，c维度一样就能成。
3.2 多元高斯的情况
import numpy as np import matplotlib.pyplot as plt fig=plt.figure(figsize=(8,6)) #Generating a Gaussion dataset: #creating random vectors from the multivariate normal distribution #given mean and covariance mu_vec1=np.array([0,0]) cov_mat1=np.array([[1,0],[0,1]]) X=np.random.multivariate_normal(mu_vec1,cov_mat1,500) R=X**2 R_sum=R.sum(axis=1) plt.scatter(X[:,0],X[:,1],color='green',marker='o', =32.*R_sum,edgecolor='black',alpha=0.5) plt.show()
3.3 绘制例子
from matplotlib import pyplot as plt
import numpy as np
# Generating a Gaussion dTset:
#Creating random vectors from the multivaritate normal distribution
#givem mean and covariance
mu_vecl = np.array([0, 0])
cov_matl = np.array([[2,0],[0,2]])
x1_samples = np.random.multivariate_normal(mu_vecl, cov_matl,100)
x2_samples = np.random.multivariate_normal(mu_vecl+0.2, cov_matl +0.2, 100)
x3_samples = np.random.multivariate_normal(mu_vecl+0.4, cov_matl +0.4, 100)
plt.figure(figsize = (8, 6))
plt.scatter(x1_samples[:,0], x1_samples[:, 1], marker='x',
color = 'blue', alpha=0.7, label = 'x1 samples')
plt.scatter(x2_samples[:,0], x1_samples[:,1], marker='o',
color ='green', alpha=0.7, label = 'x2 samples')
plt.scatter(x3_samples[:,0], x1_samples[:,1], marker='^',
color ='red', alpha=0.7, label = 'x3 samples')
plt.title('Basic scatter plot')
plt.ylabel('variable X')
plt.xlabel('Variable Y')
plt.legend(loc = 'upper right')
plt.show()
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
ax.plot([0],[0], marker="o", markersize=10)
ax.plot([0.07,0.93],[0,0], linewidth=10)
ax.scatter([1],[0], s=100)
ax.plot([0],[1], marker="o", markersize=22)
ax.plot([0.14,0.86],[1,1], linewidth=22)
ax.scatter([1],[1], s=22**2)
plt.show()
import matplotlib.pyplot as plt
for dpi in [72,100,144]:
fig,ax = plt.subplots(figsize=(1.5,2), dpi=dpi)
ax.set_title("fig.dpi={}".format(dpi))
ax.set_ylim(-3,3)
ax.set_xlim(-2,2)
ax.scatter([0],[1], s=10**2,
marker="s", linewidth=0, label="100 points^2")
ax.scatter([1],[1], s=(10*72./fig.dpi)**2,
marker="s", linewidth=0, label="100 pixels^2")
ax.legend(loc=8,framealpha=1, fontsize=8)
fig.savefig("fig{}.png".format(dpi), bbox_inches="tight")
plt.show()
3.4 绘图例3
import matplotlib.pyplot as plt
for dpi in [72,100,144]:
fig,ax = plt.subplots(figsize=(1.5,2), dpi=dpi)
ax.set_title("fig.dpi={}".format(dpi))
ax.set_ylim(-3,3)
ax.set_xlim(-2,2)
ax.scatter([0],[1], s=10**2,
marker="s", linewidth=0, label="100 points^2")
ax.scatter([1],[1], s=(10*72./fig.dpi)**2,
marker="s", linewidth=0, label="100 pixels^2")
ax.legend(loc=8,framealpha=1, fontsize=8)
fig.savefig("fig{}.png".format(dpi), bbox_inches="tight")
plt.show() 
3.5 同心绘制
plt.scatter(2, 1, s=4000, c='r') plt.scatter(2, 1, s=1000 ,c='b') plt.scatter(2, 1, s=10, c='g')
3.6 有标签绘制
import matplotlib.pyplot as plt
x_coords = [0.13, 0.22, 0.39, 0.59, 0.68, 0.74,0.93]
y_coords = [0.75, 0.34, 0.44, 0.52, 0.80, 0.25,0.55]
fig = plt.figure(figsize = (8,5))
plt.scatter(x_coords, y_coords, marker = 's', s = 50)
for x, y in zip(x_coords, y_coords):
plt.annotate('(%s,%s)'%(x,y), xy=(x,y),xytext = (0, -10), textcoords = 'offset points',ha = 'center', va = 'top')
plt.xlim([0,1])
plt.ylim([0,1])
plt.show()
3.7 直线划分
# 2-category classfication with random 2D-sample data
# from a multivariate normal distribution
import numpy as np
from matplotlib import pyplot as plt
def decision_boundary(x_1):
"""Calculates the x_2 value for plotting the decision boundary."""
# return 4 - np.sqrt(-x_1**2 + 4*x_1 + 6 + np.log(16))
return -x_1 + 1
# Generating a gaussion dataset:
# creating random vectors from the multivariate normal distribution
# given mean and covariance
mu_vec1 = np.array([0,0])
cov_mat1 = np.array([[2,0],[0,2]])
x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1,100)
mu_vec1 = mu_vec1.reshape(1,2).T # TO 1-COL VECTOR
mu_vec2 = np.array([1,2])
cov_mat2 = np.array([[1,0],[0,1]])
x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)
mu_vec2 = mu_vec2.reshape(1,2).T # to 2-col vector
# Main scatter plot and plot annotation
f, ax = plt.subplots(figsize = (7, 7))
ax.scatter(x1_samples[:, 0], x1_samples[:,1], marker = 'o',color = 'green', s=40)
ax.scatter(x2_samples[:, 0], x2_samples[:,1], marker = '^',color = 'blue', s =40)
plt.legend(['Class1 (w1)', 'Class2 (w2)'], loc = 'upper right')
plt.title('Densities of 2 classes with 25 bivariate random patterns each')
plt.ylabel('x2')
plt.xlabel('x1')
ftext = 'p(x|w1) -N(mu1=(0,0)^t, cov1 = I)\np.(x|w2) -N(mu2 = (1, 1)^t), cov2 =I'
plt.figtext(.15,.8, ftext, fontsize = 11, ha ='left')
#Adding decision boundary to plot
x_1 = np.arange(-5, 5, 0.1)
bound = decision_boundary(x_1)
plt.plot(x_1, bound, 'r--', lw = 3)
x_vec = np.linspace(*ax.get_xlim())
x_1 = np.arange(0, 100, 0.05)
plt.show()
3.8 曲线划分
# 2-category classfication with random 2D-sample data
# from a multivariate normal distribution
import numpy as np
from matplotlib import pyplot as plt
def decision_boundary(x_1):
"""Calculates the x_2 value for plotting the decision boundary."""
return 4 - np.sqrt(-x_1**2 + 4*x_1 + 6 + np.log(16))
# Generating a gaussion dataset:
# creating random vectors from the multivariate normal distribution
# given mean and covariance
mu_vec1 = np.array([0,0])
cov_mat1 = np.array([[2,0],[0,2]])
x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1,100)
mu_vec1 = mu_vec1.reshape(1,2).T # TO 1-COL VECTOR
mu_vec2 = np.array([1,2])
cov_mat2 = np.array([[1,0],[0,1]])
x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)
mu_vec2 = mu_vec2.reshape(1,2).T # to 2-col vector
# Main scatter plot and plot annotation
f, ax = plt.subplots(figsize = (7, 7))
ax.scatter(x1_samples[:, 0], x1_samples[:,1], marker = 'o',color = 'green', s=40)
ax.scatter(x2_samples[:, 0], x2_samples[:,1], marker = '^',color = 'blue', s =40)
plt.legend(['Class1 (w1)', 'Class2 (w2)'], loc = 'upper right')
plt.title('Densities of 2 classes with 25 bivariate random patterns each')
plt.ylabel('x2')
plt.xlabel('x1')
ftext = 'p(x|w1) -N(mu1=(0,0)^t, cov1 = I)\np.(x|w2) -N(mu2 = (1, 1)^t), cov2 =I'
plt.figtext(.15,.8, ftext, fontsize = 11, ha ='left')
#Adding decision boundary to plot
x_1 = np.arange(-5, 5, 0.1)
bound = decision_boundary(x_1)
plt.plot(x_1, bound, 'r--', lw = 3)
x_vec = np.linspace(*ax.get_xlim())
x_1 = np.arange(0, 100, 0.05)
plt.show()
到此这篇关于Python可视化函数plt.scatter详解的文章就介绍到这了,更多相关Python plt.scatter内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！