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White Night
散点图是一种用于显示两个变量之间关系的图表，每个点代表数据集中的一个观测值。以下是散点图的主要作用和适用场景：
展示变量之间的关系： 散点图适用于展示两个变量之间的关系，帮助观察者了解它们之间的趋势、相关性或模式
识别趋势： 通过观察散点图中的点的分布，可以判断是否存在线性或非线性的趋势。这有助于理解变量之间的关联性
发现异常值： 散点图可以用于识别数据集中的异常值，即与其他数据点明显不同的观测值
比较不同群体或类别的关系： 如果数据可以按照不同的群体或类别划分，散点图可以用于比较这些群体或类别之间的变量关系
显示数据的分布： 在散点图中，密集的点集中通常表示较高的数据密度，反之表示较低的密度，从而有助于观察数据的分布情况
用于回归分析： 散点图是回归分析的重要工具，可以用于评估两个变量之间是否存在趋势，并用最佳拟合线（回归线）来描述这种趋势
scatter(x,y) 绘制散点图
xxxxxxxxxxfrom matplotlib import pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False
x = [161.2, 167.5, 159.5, 157.0, 155.8, 170.0, 159.1, 166.0, 176.2, 160.2, 172.5, 170.9, 172.9, 153.4, 160.0, 147.2, 168.2, 175.0, 157.0, 167.6, 159.5, 175.0, 166.8, 176.5, 170.2, 174.0, 173.0, 179.9, 170.5, 160.0, 154.4, 162.0, 176.5, 160.0, 152.0, 162.1, 170.0, 160.2, 161.3, 166.4, 168.9, 163.8, 167.6, 160.0, 161.3, 167.6, 165.1, 160.0, 170.0, 157.5, 167.6, 160.7, 163.2, 152.4, 157.5, 168.3, 180.3, 165.5, 165.0, 164.5, 156.0, 160.0, 163.0, 165.7, 161.0, 162.0, 166.0, 174.0, 172.7, 167.6, 151.1, 164.5, 163.5, 152.0, 169.0, 164.0, 161.2, 155.0, 170.0, 176.2, 170.0, 162.5, 170.3, 164.1, 169.5, 163.2, 154.5, 159.8, 173.2, 170.0, 161.4, 169.0, 166.2, 159.4, 162.5, 159.0, 162.8, 159.0, 179.8, 162.9, 161.0, 151.1, 168.2, 168.9, 173.2, 171.8, 178.0, 164.3, 163.0, 168.5, 166.8, 172.7, 163.5, 169.4, 167.8, 159.5, 167.6, 161.2, 160.0, 163.2, 162.2, 161.3, 149.5, 157.5, 163.2, 172.7, 155.0, 156.5, 164.0, 160.9, 162.8, 167.0, 160.0, 160.0, 168.9, 158.2, 156.0, 160.0, 167.1, 158.0, 167.6, 156.0, 162.1, 173.4, 159.8, 170.5, 159.2, 157.5, 161.3, 162.6, 160.0, 168.9, 165.1, 162.6, 165.1, 166.4, 160.0, 152.4, 170.2, 162.6, 170.2, 158.8, 172.7, 167.6, 162.6, 167.6, 156.2, 175.2, 172.1, 162.6, 160.0, 165.1, 182.9, 166.4, 165.1, 177.8, 165.1, 175.3, 154.9, 158.8, 172.7, 168.9, 161.3, 167.6, 165.1, 175.3, 157.5, 163.8, 167.6, 165.1, 165.1, 168.9, 162.6, 164.5, 176.5, 168.9, 175.3, 159.4, 160.0, 170.2, 162.6, 167.6, 162.6, 160.7, 160.0, 157.5, 162.6, 152.4, 170.2, 165.1, 172.7, 165.1, 170.2, 170.2, 170.2, 161.3, 167.6, 167.6, 165.1, 162.6, 152.4, 168.9, 170.2, 175.2, 175.2, 160.0, 165.1, 174.0, 170.2, 160.0, 167.6, 167.6, 167.6, 154.9, 162.6, 175.3, 171.4, 157.5, 165.1, 160.0, 174.0, 162.6, 174.0, 162.6, 161.3, 156.2, 149.9, 169.5, 160.0, 175.3, 169.5, 160.0, 172.7, 162.6, 157.5, 176.5, 164.4, 160.7, 174.0, 163.8]y = [51.6, 59.0, 49.2, 63.0, 53.6, 59.0, 47.6, 69.8, 66.8, 75.2, 55.2, 54.2, 62.5, 42.0, 50.0, 49.8, 49.2, 73.2, 47.8, 68.8, 50.6, 82.5, 57.2, 87.8, 72.8, 54.5, 59.8, 67.3, 67.8, 47.0, 46.2, 55.0, 83.0, 54.4, 45.8, 53.6, 73.2, 52.1, 67.9, 56.6, 62.3, 58.5, 54.5, 50.2, 60.3, 58.3, 56.2, 50.2, 72.9, 59.8, 61.0, 69.1, 55.9, 46.5, 54.3, 54.8, 60.7, 60.0, 62.0, 60.3, 52.7, 74.3, 62.0, 73.1, 80.0, 54.7, 53.2, 75.7, 61.1, 55.7, 48.7, 52.3, 50.0, 59.3, 62.5, 55.7, 54.8, 45.9, 70.6, 67.2, 69.4, 58.2, 64.8, 71.6, 52.8, 59.8, 49.0, 50.0, 69.2, 55.9, 63.4, 58.2, 58.6, 45.7, 52.2, 48.6, 57.8, 55.6, 66.8, 59.4, 53.6, 73.2, 53.4, 69.0, 58.4, 56.2, 70.6, 59.8, 72.0, 65.2, 56.6, 105.2, 51.8, 63.4, 59.0, 47.6, 63.0, 55.2, 45.0, 54.0, 50.2, 60.2, 44.8, 58.8, 56.4, 62.0, 49.2, 67.2, 53.8, 54.4, 58.0, 59.8, 54.8, 43.2, 60.5, 46.4, 64.4, 48.8, 62.2, 55.5, 57.8, 54.6, 59.2, 52.7, 53.2, 64.5, 51.8, 56.0, 63.6, 63.2, 59.5, 56.8, 64.1, 50.0, 72.3, 55.0, 55.9, 60.4, 69.1, 84.5, 55.9, 55.5, 69.5, 76.4, 61.4, 65.9, 58.6, 66.8, 56.6, 58.6, 55.9, 59.1, 81.8, 70.7, 56.8, 60.0, 58.2, 72.7, 54.1, 49.1, 75.9, 55.0, 57.3, 55.0, 65.5, 65.5, 48.6, 58.6, 63.6, 55.2, 62.7, 56.6, 53.9, 63.2, 73.6, 62.0, 63.6, 53.2, 53.4, 55.0, 70.5, 54.5, 54.5, 55.9, 59.0, 63.6, 54.5, 47.3, 67.7, 80.9, 70.5, 60.9, 63.6, 54.5, 59.1, 70.5, 52.7, 62.7, 86.3, 66.4, 67.3, 63.0, 73.6, 62.3, 57.7, 55.4, 104.1, 55.5, 77.3, 80.5, 64.5, 72.3, 61.4, 58.2, 81.8, 63.6, 53.4, 54.5, 53.6, 60.0, 73.6, 61.4, 55.5, 63.6, 60.9, 60.0, 46.8, 57.3, 64.1, 63.6, 67.3, 75.5, 68.2, 61.4, 76.8, 71.8, 55.5, 48.6, 66.4, 67.3]plt.scatter(x, y,color='tab:blue',alpha=0.5)xxxxxxxxxxfrom matplotlib import pyplot as pltfrom random import randint
plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False
x = []y = [randint(1,100) for i in range(len(x))]size = [i//10 for i in y]color = [randint(1,100) for i in range(len(x))]plt.scatter(x, y,c=color,s=y,alpha=0.5)实时学习反馈
1. 散点图最适合用于展示以下哪种情况？
A 时间序列数据的趋势变化
B 比较不同类别之间的大小关系
C 两个变量之间的关系和趋势
D 数据的分布情况
答案
1=>C