我有如下所示的代碼，我試圖在 7 月 - 12 月顯示的繪圖上為 df“ltyc”中的數據畫一條黑線。我的錯誤是在 plt.legend 行之前的最后.import warningsimport itertoolsimport numpy as npimport matplotlib.pyplot as pltwarnings.filterwarnings("ignore")plt.style.use('fivethirtyeight')import pandas as pdimport statsmodels.api as smimport matplotlibmatplotlib.rcParams['axes.labelsize'] = 14matplotlib.rcParams['xtick.labelsize'] = 12matplotlib.rcParams['ytick.labelsize'] = 12matplotlib.rcParams['text.color'] = 'k'from sys import exitdf = pd.read_excel("MOSDailyWindSpeed.xlsx")wspdBH1 = df.groupby('Date')[' Simulated WS BH1PI'].sum().reset_index()wspdHOO = df.groupby('Date')[' Simulated WS HOO801'].sum().reset_index()wspdBH1 = wspdBH1.set_index('Date')wspdHOO = wspdHOO.set_index('Date')wspdBH1.indexwspdHOO.indexy = wspdHOO[' Simulated WS HOO801'].resample('MS').mean()#monthly mean --> change site here 'MS' is month start for 'Date' coly['2017':]#look at daily data starting 2017 -view data y.plot(figsize=(15, 6))plt.show()from pylab import rcParamsrcParams['figure.figsize'] = 18, 8decomposition = sm.tsa.seasonal_decompose(y, model='additive')fig = decomposition.plot()plt.show()p = d = q = range(0, 2)pdq = list(itertools.product(p, d, q))seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]print('Examples of parameter combinations for Seasonal ARIMA...')print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))for param in pdq: for param_seasonal in seasonal_pdq: try: mod = sm.tsa.statespace.SARIMAX(y, order=param, seasonal_order=param_seasonal, enforce_stationarity=False, enforce_invertibility=False)
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holdtom
TA貢獻1805條經驗 獲得超10個贊
我用它來將整數日期轉換為 DF 中的 yyyy-mm-dd 格式。ltyc['Date'] = pd.to_datetime(ltyc["Date"], format='%m').apply(lambda dt: dt.replace(year=2020))
然后，我將 DF (ltyc) 轉換成這樣的系列： ltycs = pd.Series(ltyc['LT Mean'].values, index=ltyc['Date'])#convert to Series 因為其他圖都在系列格式
ltycs.plot(label='LT Mean',ax=ax,color='k')
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