如何繪制多個每日時間序列，在指定的觸發時間對齊？

問題：

我有一個df看起來像這樣的資料框：

                                  value  msg_type
date        
2022-03-15 08:15:10 00:00         122    None
2022-03-15 08:25:10 00:00         125    None
2022-03-15 08:30:10 00:00         126    None
2022-03-15 08:30:26.542134 00:00  127    ANNOUNCEMENT
2022-03-15 08:35:10 00:00         128    None
2022-03-15 08:40:10 00:00         122    None
2022-03-15 08:45:09 00:00         127    None
2022-03-15 08:50:09 00:00         133    None
2022-03-15 08:55:09 00:00         134    None
....
2022-03-16 09:30:09 00:00         132    None
2022-03-16 09:30:13.234425 00:00  135    ANNOUNCEMENT
2022-03-16 09:35:09 00:00         130    None
2022-03-16 09:40:09 00:00         134    None
2022-03-16 09:45:09 00:00         135    None
2022-03-16 09:50:09 00:00         134    None

資料以大約 5 分鐘的value間隔出現，但訊息可以隨時出現。我正在嘗試values每天繪制一條線，其中 x 軸的范圍從 t=-2 小時到 t= 8 小時，并且ANNOUNCEMENT發生在 t=0（見下圖）。

因此，例如，如果ANNOUNCEMENT3/15 上午 8:30 和 3/16 上午 9:30 再次發生，則應該有兩行：

• 3/15 的一行繪制從上午 6:30 到下午 4:30 的資料，以及
• 3/16 的一行繪制從上午 7:30 到下午 5:30 的資料，

兩者共享相同的 x 軸，范圍從 -2 到 8，ANNOUNCEMENTt=0。

我試過的：

I am able to do this currently by finding the index position of an announcement (e.g. say it occurs at row 298 -> announcement_index = 298), generating an array of 120 numbers from -24 to 96 (representing 10 hours at 5 minutes per number -> x = np.arange(-24, 96, 1)), then plotting

sns.lineplot(x, y=df['value'].iloc[announcement_index-24:announcement_index 96])

While this does mostly work (see image below), I suspect it's not the correct way to go about it. Specifically, trying to add more info to the plot (like a different set of 'value' markers) at specific times is difficult because I would need to convert the timestamp into this arbitrary 24-96 value range.

How can I make this same plot but by utilizing the datetime index instead? Thank you so much!

uj5u.com熱心網友回復：

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完整代碼：

import io
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

s = '''
date,value,msg_type
2022-03-15 08:15:10 00:00,122,None
2022-03-15 08:25:10 00:00,125,None
2022-03-15 08:30:10 00:00,126,None
2022-03-15 08:30:26.542134 00:00,127,ANNOUNCEMENT
2022-03-15 08:35:10 00:00,128,None
2022-03-15 08:40:10 00:00,122,None
2022-03-15 08:45:09 00:00,127,None
2022-03-15 08:50:09 00:00,133,None
2022-03-15 08:55:09 00:00,134,None
2022-03-16 09:30:09 00:00,132,None
2022-03-16 09:30:13.234425 00:00,135,ANNOUNCEMENT
2022-03-16 09:35:09 00:00,130,None
2022-03-16 09:40:09 00:00,134,None
2022-03-16 09:45:09 00:00,135,None
2022-03-16 09:50:09 00:00,134,None
'''
df = pd.read_csv(io.StringIO(s), index_col=0, parse_dates=['date'])

# create intervals from -1H to  2H of the index
dl, dr = -1, 2
left = df.index   pd.Timedelta(f'{dl}H')
right = df.index   pd.Timedelta(f'{dr}H')
df['interval'] = pd.arrays.IntervalArray.from_arrays(left, right)

# plot each announcement's interval.left to interval.right
fig, ax = plt.subplots()
for ann in df.loc[df['msg_type'] == 'ANNOUNCEMENT')].itertuples():
    window = df.loc[ann.interval.left:ann.interval.right] # extract interval.left to interval.right
    window.index -= ann.Index                             # compute time since announcement
    window.index = window.index.total_seconds()           # convert to seconds since announcement

    window.plot(ax=ax, y='value', label=ann.Index.date())
    deltas = np.arange(dl, dr   1)
    ax.set(xticks=deltas * 3600, xticklabels=deltas)      # set tick labels to hours since announcement

ax.grid()
ax.legend()

標籤：python pandas datetime matplotlib seaborn

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