[英]Plotting a linear regression with dates in matplotlib.pyplot
我將如何在 pyplot 中繪制帶有日期的線性回歸? 我無法找到這個問題的明確答案。 這是我嘗試過的(由 w3school 的線性回歸教程提供)。
import matplotlib.pyplot as plt
from scipy import stats
x = ['01/01/2019', '01/02/2019', '01/03/2019', '01/04/2019', '01/05/2019', '01/06/2019', '01/07/2019', '01/08/2019', '01/09/2019', '01/10/2019', '01/11/2019', '01/12/2019', '01/01/2020']
y = [12050, 17044, 14066, 16900, 19979, 17593, 14058, 16003, 15095, 12785, 12886, 20008]
slope, intercept, r, p, std_err = stats.linregress(x, y)
def myfunc(x):
return slope * x + intercept
mymodel = list(map(myfunc, x))
plt.scatter(x, y)
plt.plot(x, mymodel)
plt.show()
您首先必須將日期轉換為數字才能進行回歸(並為此繪圖)。 然后您可以指示 matplotlib 將 x 值解釋為日期以獲得格式良好的軸:
import matplotlib.pyplot as plt
from scipy import stats
import datetime
x = ['01/01/2019', '01/02/2019', '01/03/2019', '01/04/2019', '01/05/2019', '01/06/2019', '01/07/2019', '01/08/2019', '01/09/2019', '01/10/2019', '01/11/2019', '01/12/2019']
y = [12050, 17044, 14066, 16900, 19979, 17593, 14058, 16003, 15095, 12785, 12886, 20008]
# convert the dates to a number, using the datetime module
x = [datetime.datetime.strptime(i, '%M/%d/%Y').toordinal() for i in x]
slope, intercept, r, p, std_err = stats.linregress(x, y)
def myfunc(x):
return slope * x + intercept
mymodel = list(map(myfunc, x))
fig, ax = plt.subplots()
ax.scatter(x, y)
ax.plot(x, mymodel)
# instruct matplotlib on how to convert the numbers back into dates for the x-axis
l = matplotlib.dates.AutoDateLocator()
f = matplotlib.dates.AutoDateFormatter(l)
ax.xaxis.set_major_locator(l)
ax.xaxis.set_major_formatter(f)
plt.show()
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