我有一個數據框，其中包含日期時間格式的“Date_and_time”列。不幸的是，當到達午夜時（第 15235 行：2020-08-02 00:00:00.000000），日期不會相應更改。因此，到達午夜 (00:00:00.000000) 時，2020-08-02 00:00:00.000000 應該轉到 2020-08-03 00:00:00.00000。在第 15235 行已到達午夜，但日期直到第 16000 行才更改。如何更改此設置，以便在到達午夜時日期是正確的？謝謝。#Load filedf = pd.read_csv(file, sep=";", names=["Date", "Time", "ID1","ID2","ID3","MP","ET"], skiprows = 1 ,float_precision='round_trip')df1 = df['Time'].str.split(expand=True)#Use columns 'Date' and 'Time' to create column 'Date_and_time' in datetime formatdf['Date_and_time'] = (pd.to_datetime(df['Date']) + pd.to_timedelta(df1[0]) + pd.to_timedelta(df1[1].str.replace('ms','').astype(int), unit='us'))Out[45]: Date ... Date_and_time0 2020/08/02 ... 2020-08-02 21:21:46.0000001 2020/08/02 ... 2020-08-02 21:21:46.0821912 2020/08/02 ... 2020-08-02 21:21:46.1643833 2020/08/02 ... 2020-08-02 21:21:46.2465754 2020/08/02 ... 2020-08-02 21:21:46.328767 ... ... ...15235 2020/08/02 ... 2020-08-02 00:00:00.00000015236 2020/08/02 ... 2020-08-02 00:00:00.08219115237 2020/08/02 ... 2020-08-02 00:00:00.16438315238 2020/08/02 ... 2020-08-02 00:00:00.246575 ... ... ...16000 2020/08/03 ... 2020-08-03 00:00:16.082191 ... ... ...330404 2020/08/03 ... 2020-08-03 03:00:33.000000330405 2020/08/03 ... 2020-08-03 03:00:33.040513330406 2020/08/03 ... 2020-08-03 03:00:33.081026330407 2020/08/03 ... 2020-08-03 03:00:33.121539330408 2020/08/03 ... 2020-08-03 03:00:33.162052[330409 rows x 8 columns]
1 回答
倚天杖
TA貢獻1828條經驗 獲得超3個贊
這本質上是另一個需要“diff-cumsum”技巧來累積負面變化數量的問題。然而，在這種情況下，.diff()不支持日期時間差異，因此做起來會更加棘手。
這是一個快速而骯臟的展示df["Date_and_time"]。您應該對自己涉及的其他列執行類似操作。
from datetime import timedelta
#df = pd.read_clipboard(sep=r"\s{2,}")
df["Date_and_time"] = pd.to_datetime(df["Date_and_time"])
# get timestamp in nanoseconds
df["ns"] = df["Date_and_time"].values.astype(np.int64)
# detect reversed time change and accumulate days
df["days"] = (df["ns"].diff() < 0).cumsum()
# add the days found
df["Date_and_time_new"] = df.apply(lambda row: row["Date_and_time"] + timedelta(days=row["days"]), axis=1)
df
Out[76]:
Date Date_and_time ... days Date_and_time_new
0 2020/08/02 2020-08-02 21:21:46.000000 ... 0 2020-08-02 21:21:46.000000
1 2020/08/02 2020-08-02 21:21:46.082191 ... 0 2020-08-02 21:21:46.082191
2 2020/08/02 2020-08-02 21:21:46.164383 ... 0 2020-08-02 21:21:46.164383
3 2020/08/02 2020-08-02 21:21:46.246575 ... 0 2020-08-02 21:21:46.246575
4 2020/08/02 2020-08-02 21:21:46.328767 ... 0 2020-08-02 21:21:46.328767
5 2020/08/02 2020-08-02 00:00:00.000000 ... 1 2020-08-03 00:00:00.000000
6 2020/08/02 2020-08-02 00:00:00.082191 ... 1 2020-08-03 00:00:00.082191
7 2020/08/02 2020-08-02 00:00:00.164383 ... 1 2020-08-03 00:00:00.164383
8 2020/08/02 2020-08-02 00:00:00.246575 ... 1 2020-08-03 00:00:00.246575
9 2020/08/03 2020-08-03 00:00:16.082191 ... 1 2020-08-04 00:00:16.082191
10 2020/08/03 2020-08-03 03:00:33.000000 ... 1 2020-08-04 03:00:33.000000
11 2020/08/03 2020-08-03 03:00:33.040513 ... 1 2020-08-04 03:00:33.040513
12 2020/08/03 2020-08-03 03:00:33.081026 ... 1 2020-08-04 03:00:33.081026
13 2020/08/03 2020-08-03 03:00:33.121539 ... 1 2020-08-04 03:00:33.121539
14 2020/08/03 2020-08-03 03:00:33.162052 ... 1 2020-08-04 03:00:33.162052
[15 rows x 5 columns]
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