[英]How to find and extract the last reading of recorded days/weeks/months from one column to new ones in a DataFrame using pandas?
在大型 DataFrame 中，我在一列中有讀數，在另一列中有讀數的“DateTime”格式的本地日期和時間。 我想在同一 DateFrame 中生成新列，這些新列僅包含單獨列中記錄的天、周或月的最后讀數。 能夠選擇一周的最后一天也很重要。 這是我擁有的數據示例（在這里，我選擇星期三作為一周的最后一天）：
local_date_time weekday readings
0 2022-04-29 17:03:25 Friday 468
1 2022-04-29 23:42:06 Friday 638
2 2022-04-30 00:22:06 Saturday 649
3 2022-04-30 16:42:07 Saturday 650
4 2022-04-30 19:42:06 Saturday 641
5 2022-04-30 23:59:06 Saturday 1301
6 2022-05-01 00:42:07 Sunday 1240
7 2022-05-01 04:12:07 Sunday 927
8 2022-05-01 09:52:07 Sunday 810
9 2022-05-01 16:42:07 Sunday 1024
10 2022-05-01 23:52:07 Sunday 551
11 2022-05-02 09:02:07 Monday 534
12 2022-05-02 13:42:07 Monday 684
13 2022-05-02 22:32:08 Monday 952
14 2022-05-02 23:59:07 Monday 628
15 2022-05-03 00:02:07 Tuesday 640
16 2022-05-03 06:12:08 Tuesday 762
17 2022-05-03 11:22:08 Tuesday 707
18 2022-05-03 14:12:08 Tuesday 623
19 2022-05-03 21:02:08 Tuesday 713
20 2022-05-03 23:42:08 Tuesday 606
21 2022-05-04 01:02:09 Wednesday 565
22 2022-05-04 05:32:09 Wednesday 495
23 2022-05-04 20:22:09 Wednesday 565
24 2022-05-04 23:59:09 Wednesday 693
25 2022-05-05 00:02:09 Thursday 723
26 2022-05-05 04:12:08 Thursday 534
27 2022-05-05 10:22:09 Thursday 464
28 2022-05-05 15:42:09 Thursday 479
29 2022-05-05 23:59:09 Thursday 478
為此，我嘗試使用條件“df.loc”函數來解決問題。 這是我寫的代碼：
df['Day'] = df['local_date_time'].dt.day
df['Hour'] = df['local_date_time'].dt.hour
df['Min'] = df['local_date_time'].dt.minute
df.loc[(df['Hour'] == 23) & (df['Min'] >= 59),'end_day'] = df['readings']
df.loc[(df['Hour'] == 23) & (df['Min'] >= 59) & (df['Weekday'] == 'Wednesday'),'end_week'] = df['readings']
df.loc[(df['Hour'] == 23) & (df['Min'] >= 59) & (df['Day'] == 30),'end_month'] = df['readings']
在我每天 23:59 閱讀之前，代碼運行良好。 但是，如果每天 23:59 不存在讀取，則代碼不起作用。 另外，在這種方法中，我可以只選擇一個特定日期（例如示例中的 30 日）作為該月的最后一天，這不適用於其他天數更多或更少的月份。 這是我希望看到的結果。
local_date_time weekday readings end_day end_week end_month 2022-04-29 17:03:25 Friday 468 2022-04-29 23:42:06 Friday 638 638 2022-04-30 00:22:06 Saturday 649 2022-04-30 16:42:07 Saturday 650 2022-04-30 19:42:06 Saturday 641 2022-04-30 23:59:06 Saturday 1301 1301 1301 2022-05-01 00:42:07 Sunday 1240 2022-05-01 04:12:07 Sunday 927 2022-05-01 09:52:07 Sunday 810 2022-05-01 16:42:07 Sunday 1024 2022-05-01 23:52:07 Sunday 551 551 2022-05-02 09:02:07 Monday 534 2022-05-02 13:42:07 Monday 684 2022-05-02 22:32:08 Monday 952 2022-05-02 23:59:07 Monday 628 628 2022-05-03 00:02:07 Tuesday 640 2022-05-03 06:12:08 Tuesday 762 2022-05-03 11:22:08 Tuesday 707 2022-05-03 14:12:08 Tuesday 623 2022-05-03 21:02:08 Tuesday 713 2022-05-03 23:42:08 Tuesday 606 606 2022-05-04 01:02:09 Wednesday 565 2022-05-04 05:32:09 Wednesday 495 2022-05-04 20:22:09 Wednesday 565 2022-05-04 23:59:09 Wednesday 693 693 693 2022-05-05 00:02:09 Thursday 723 2022-05-05 04:12:08 Thursday 534 2022-05-05 10:22:09 Thursday 464 2022-05-05 15:42:09 Thursday 479 2022-05-05 23:59:09 Thursday 478 478
在這里使用適當的 DatetimeIndex 重新采樣將很有用。
# Make it a datetime index:
df.local_date_time = pd.to_datetime(df.local_date_time)
df = df.set_index('local_date_time')
# Do the resampling:
# End of Day:
df['end_day'] = df.resample('D')['readings'].transform(lambda x: x.tail(1))
# Weekly, Wednesdays:
df['end_week'] = df.resample('W-Wed')['readings'].transform(lambda x: x.tail(1))
# End of Month:
df['end_month'] = df.resample('M')['readings'].transform(lambda x: x.tail(1))
# Some corrections for the very end:
# Clear non-wednesdays:
df['end_week'] = df['end_week'].where(df.index.to_series().dt.weekday.eq(2), np.nan)
# clear non-end-of-months:
df['end_month'] = df['end_month'].where(df.index.to_series().dt.is_month_end, np.nan)
輸出：
weekday readings end_day end_week end_month
local_date_time
2022-04-29 17:03:25 Friday 468 NaN NaN NaN
2022-04-29 23:42:06 Friday 638 638.0 NaN NaN
2022-04-30 00:22:06 Saturday 649 NaN NaN NaN
2022-04-30 16:42:07 Saturday 650 NaN NaN NaN
2022-04-30 19:42:06 Saturday 641 NaN NaN NaN
2022-04-30 23:59:06 Saturday 1301 1301.0 NaN 1301.0
2022-05-01 00:42:07 Sunday 1240 NaN NaN NaN
2022-05-01 04:12:07 Sunday 927 NaN NaN NaN
2022-05-01 09:52:07 Sunday 810 NaN NaN NaN
2022-05-01 16:42:07 Sunday 1024 NaN NaN NaN
2022-05-01 23:52:07 Sunday 551 551.0 NaN NaN
2022-05-02 09:02:07 Monday 534 NaN NaN NaN
2022-05-02 13:42:07 Monday 684 NaN NaN NaN
2022-05-02 22:32:08 Monday 952 NaN NaN NaN
2022-05-02 23:59:07 Monday 628 628.0 NaN NaN
2022-05-03 00:02:07 Tuesday 640 NaN NaN NaN
2022-05-03 06:12:08 Tuesday 762 NaN NaN NaN
2022-05-03 11:22:08 Tuesday 707 NaN NaN NaN
2022-05-03 14:12:08 Tuesday 623 NaN NaN NaN
2022-05-03 21:02:08 Tuesday 713 NaN NaN NaN
2022-05-03 23:42:08 Tuesday 606 606.0 NaN NaN
2022-05-04 01:02:09 Wednesday 565 NaN NaN NaN
2022-05-04 05:32:09 Wednesday 495 NaN NaN NaN
2022-05-04 20:22:09 Wednesday 565 NaN NaN NaN
2022-05-04 23:59:09 Wednesday 693 693.0 693.0 NaN
2022-05-05 00:02:09 Thursday 723 NaN NaN NaN
2022-05-05 04:12:08 Thursday 534 NaN NaN NaN
2022-05-05 10:22:09 Thursday 464 NaN NaN NaN
2022-05-05 15:42:09 Thursday 479 NaN NaN NaN
2022-05-05 23:59:09 Thursday 478 478.0 NaN NaN
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