import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.seasonal import seasonal_decompose

df = pd.read_csv('microsoft_stock.csv')

更改数据类型

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

将日期设置为索引

df = df.set_index('Date')

季节性分解

result = seasonal_decompose( df['Close'], model='additive', period=365, extrapolate_trend='freq' )

绘图

fig = result.plot(observed=False)

保存并关闭图像

fig.savefig("output/output.png") plt.close(fig)

import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.seasonal import seasonal_decompose

df = pd.read_csv('microsoft_stock.csv')

更改数据类型

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

将日期设置为索引

df = df.set_index('Date')

季节性分解

result = seasonal_decompose( df['Close'], model='additive', period=365, extrapolate_trend='freq' )

print(result.trend)