matplotlib 带趋势线的散点图

bihw5rsg  于 2023-05-18  发布在  其他
关注(0)|答案(3)|浏览(170)

我有一个python散点图,我想在上面画一条趋势线。从我在互联网上找到的各种例子中学习如何绘制趋势线,我的代码如下:

import matplotlib.pyplot as plt
import numpy as np

x=np.array([9.80,13.20,13.46,14.09,13.96,10.77,8.79,8.61,8.83,11.08,10.13,12.40,9.90,10.96,12.75,11.79,11.79,12.38,12.78,13.08,12.83,12.57,12.96,12.90,12.91,13.67,12.83,12.50,12.42,12.83,12.82,12.70,12.60,12.90,13.20])
y=np.array([0.0706,0.0969,0.0997,0.1031,0.0848,0.1044,0.0815,0.1030,0.0783,0.0970,0.1193,0.0796,0.0697,0.0738,0.0895,0.0912,0.0887,0.0973,0.0942,0.1052,0.0984,0.0965,0.0903,0.0876,0.1071,0.0872,0.0857,0.0967,0.0926,0.0837,0.0967,0.0935,0.0946,0.0930,0.0758
])

plt.scatter(x, y)
fit = np.polyfit(x, y, deg=4)
p = np.poly1d(fit)
plt.plot(x,p(x),"r--")
plt.show()

但是结果线不是曲线,而是线的混合。有人能解释一下我的错误吗?

0yg35tkg

0yg35tkg1#

我认为这是因为x值没有排序。看看这段代码:

import matplotlib.pyplot as plt 
import numpy as np

x=np.array([9.80,13.20,13.46,14.09,13.96,10.77,8.79,8.61,8.83,11.08,10.13,12.40,9.90,10.96,12.75,11.79,11.79,12.38,12.78,
            13.08,12.83,12.57,12.96,12.90,12.91,13.67,12.83,12.50,12.42,12.83,12.82,12.70,12.60,12.90,13.20]) 

y=np.array([0.0706,0.0969,0.0997,0.1031,0.0848,0.1044,0.0815,0.1030,0.0783,0.0970,0.1193,0.0796,0.0697,0.0738,
            0.0895,0.0912,0.0887,0.0973,0.0942,0.1052,0.0984,0.0965,0.0903,0.0876,0.1071,0.0872,0.0857,0.0967,0.0926,0.0837,0.0967,0.0935,0.0946,0.0930,0.0758 ])

# Here I sort x values and their corresponding y values
args = np.argsort(x)
x = x[args]
y = y[args]

plt.scatter(x, y) 
fit = np.polyfit(x, y, deg=4) 
p = np.poly1d(fit) 
plt.plot(x,p(x),"r--") 
plt.show()

结果:

xpcnnkqh

xpcnnkqh2#

这将:

plt.scatter(x,y)
x = sorted(x) 
plt.plot(x,p(x),"r--")

诀窍是在x值上绘制线之前对它们进行排序。

bmvo0sr5

bmvo0sr53#

2018年12月18日,2018年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019年12月,2019日,2019年11月,2019年11月,2019年11日,2019年11月,2019年11日,2019年11月,2019年11月,2019年11日,2019年11月,2019年11
df_entries = df. groupby(["Year"]). count(). reset_index()moving_avg = df_entries. rolling(window = 5). mean()
cum_sum [:5]
sns.散点图(x ="Year",y ="ID",size ="Medal",hue ="Medal",data = df_medals [df_medals ["Medal"]!="None"])sns. linepot(x ="Year",y ="ID",data = cum_sum)

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