python - 时间序列数据python的Lowess平滑

Question

我正在尝试使用 LOWESS 来平滑以下数据:



我想获得一条平滑的线来过滤掉数据中的尖峰。我的代码如下:

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import HourLocator, DayLocator, DateFormatter
from statsmodels.nonparametric.smoothers_lowess import lowess

file = r'C:...'
df = pd.read_csv(file) # reads data file   

df['Date'] = pd.to_datetime(df['Time Local'], format='%d/%m/%Y  %H:%M')     

x = df['Date']  
y1 = df['CTk2 Level'] 

filtered = lowess(y1, x, is_sorted=True, frac=0.025, it=0)

plt.plot(x, y1, 'r')
plt.plot(filtered[:,0], filtered[:,1], 'b')

plt.show()

当我运行此代码时，出现以下错误:

ValueError: view limit minimum -7.641460199922635e+16 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units

我的数据中的日期格式为 07/05/2018 00:07:00。我认为问题在于 LOWESS 正在努力处理日期时间数据，但不确定？

你能帮我么？

Answer 1

Lowess 不尊重 DateTimeIndex 类型，而是仅将日期作为自纪元以来的纳秒返回。幸运的是，很容易转换回来:

smoothedx, smoothedy = lowess(y1, x, is_sorted=True, frac=0.025, it=0)
smoothedx = smoothedx.astype('datetime64[s]')