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如何在pyplot中绘制垂直偏移

matplotlib python

A.E · 技术社区 · 6 年前

%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

house_dic={'sqf':[700,950,1100, 1400, 2000],'price': [780, 800, 950, 1500, 1900]}

house=pd.DataFrame(house_dic)
plt.scatter(house['sqf'],house['price'])
plt.xlabel('Square Feet')
plt.ylabel('Price $K')

w=1
y_fit=house['sqf']*w
plt.plot(house['sqf'], y_fit)

最后的图形应该是这样的(我在keynote中手动绘制了箭头) 谢谢

1 回复 | 直到 6 年前

andrew_reece 6 年前

plt.arrow() .

前四个参数是:从x开始( sqf ),从y开始( y_fit ),x的变化(这里是0,因为我们需要垂直线),y的变化(我已经计算了剩余距离的一部分)。这个 arrow_dim 值更改箭头的大小。

resid = house['price'] - y_fit
shrink = -.2
arrow_dim = (20, 20)

for i in range(len(house)):
    plt.arrow(house.loc[i, 'sqf'], y_fit[i], 0, resid[i] + resid[i]*shrink , 
              head_width=arrow_dim[0], head_length=arrow_dim[1], fc='r', ec='r')

Diazonic Labs 3 年前

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(32)
exp = np.arange(1,11)
salary = np.random.randint(20000,100000,10)
salary = salary[0]*0.1*exp + salary
salary = np.sort(salary)
x = exp.reshape(-1,1) # To change 1 Dimesion to 2 Dimension
y = salary
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x,y)
y_pred = model.predict(x)
plt.scatter(x,y)
plt.plot(x,y_pred,c='r')
for i in range(len(x)):
  plt.plot([x[i],x[i]],[y[i],y_pred[i]],c='k')

enter image description here