從日期中刪除秒Pandas

Question

我有一個數據框，其中包含具有以下格式的日期（StartTime）的列：28-7-2015 0:09:00同一個數據幀還包含一個包含數字秒（SetupDuration1）。

我想創建減去從的DateField秒數新列，

dftask['Start'] = dftask['StartTime'] - dftask['SetupDuration1'] 

的SetupDuration1列是一個數值列，因爲我在這個專欄做不同的操作必須保持數值列，取絕對值等。

那麼我應該如何以正確的方式減去秒數。 ？

Answer 1

apply拉姆達轉換爲timedelta然後減去：

In [88]: 
df = pd.DataFrame({'StartTime':pd.date_range(start=dt.datetime(2015,1,1), end = dt.datetime(2015,2,1)), 'SetupDuration1':np.random.randint(0, 59, size=32)}) 
df 

Out[88]: 
    SetupDuration1 StartTime 
0    14 2015-01-01 
1    55 2015-01-02 
2    21 2015-01-03 
3    50 2015-01-04 
4    21 2015-01-05 
5    6 2015-01-06 
6    6 2015-01-07 
7    2 2015-01-08 
8    10 2015-01-09 
9    3 2015-01-10 
10    11 2015-01-11 
11    32 2015-01-12 
12    53 2015-01-13 
13    45 2015-01-14 
14    48 2015-01-15 
15    23 2015-01-16 
16    7 2015-01-17 
17    5 2015-01-18 
18    18 2015-01-19 
19    26 2015-01-20 
20    48 2015-01-21 
21    8 2015-01-22 
22    58 2015-01-23 
23    24 2015-01-24 
24    47 2015-01-25 
25    10 2015-01-26 
26    32 2015-01-27 
27    26 2015-01-28 
28    36 2015-01-29 
29    36 2015-01-30 
30    40 2015-01-31 
31    18 2015-02-01 

In [94]: 
df['Start'] = df['StartTime'] - df['SetupDuration1'].apply(lambda x: pd.Timedelta(x, 's')) 
df 

Out[94]: 
    SetupDuration1 StartTime    Start 
0    14 2015-01-01 2014-12-31 23:59:46 
1    55 2015-01-02 2015-01-01 23:59:05 
2    21 2015-01-03 2015-01-02 23:59:39 
3    50 2015-01-04 2015-01-03 23:59:10 
4    21 2015-01-05 2015-01-04 23:59:39 
5    6 2015-01-06 2015-01-05 23:59:54 
6    6 2015-01-07 2015-01-06 23:59:54 
7    2 2015-01-08 2015-01-07 23:59:58 
8    10 2015-01-09 2015-01-08 23:59:50 
9    3 2015-01-10 2015-01-09 23:59:57 
10    11 2015-01-11 2015-01-10 23:59:49 
11    32 2015-01-12 2015-01-11 23:59:28 
12    53 2015-01-13 2015-01-12 23:59:07 
13    45 2015-01-14 2015-01-13 23:59:15 
14    48 2015-01-15 2015-01-14 23:59:12 
15    23 2015-01-16 2015-01-15 23:59:37 
16    7 2015-01-17 2015-01-16 23:59:53 
17    5 2015-01-18 2015-01-17 23:59:55 
18    18 2015-01-19 2015-01-18 23:59:42 
19    26 2015-01-20 2015-01-19 23:59:34 
20    48 2015-01-21 2015-01-20 23:59:12 
21    8 2015-01-22 2015-01-21 23:59:52 
22    58 2015-01-23 2015-01-22 23:59:02 
23    24 2015-01-24 2015-01-23 23:59:36 
24    47 2015-01-25 2015-01-24 23:59:13 
25    10 2015-01-26 2015-01-25 23:59:50 
26    32 2015-01-27 2015-01-26 23:59:28 
27    26 2015-01-28 2015-01-27 23:59:34 
28    36 2015-01-29 2015-01-28 23:59:24 
29    36 2015-01-30 2015-01-29 23:59:24 
30    40 2015-01-31 2015-01-30 23:59:20 
31    18 2015-02-01 2015-01-31 23:59:42 

時序

其實它看起來更快地只是構建一個Timedeltaindex就地：

In [99]: 
%timeit df['Start'] = df['StartTime'] - pd.TimedeltaIndex(df['SetupDuration1'], unit='s') 
1000 loops, best of 3: 837 µs per loop 

In [100]: 
%timeit df['Start'] = df['StartTime'] - df['SetupDuration1'].apply(lambda x: pd.Timedelta(x, 's')) 
100 loops, best of 3: 1.97 ms per loop 

所以我d只是做：

In [101]: 
df['Start'] = df['StartTime'] - pd.TimedeltaIndex(df['SetupDuration1'], unit='s') 
df 

Out[101]: 
    SetupDuration1 StartTime    Start 
0    14 2015-01-01 2014-12-31 23:59:46 
1    55 2015-01-02 2015-01-01 23:59:05 
2    21 2015-01-03 2015-01-02 23:59:39 
3    50 2015-01-04 2015-01-03 23:59:10 
4    21 2015-01-05 2015-01-04 23:59:39 
5    6 2015-01-06 2015-01-05 23:59:54 
6    6 2015-01-07 2015-01-06 23:59:54 
7    2 2015-01-08 2015-01-07 23:59:58 
8    10 2015-01-09 2015-01-08 23:59:50 
9    3 2015-01-10 2015-01-09 23:59:57 
10    11 2015-01-11 2015-01-10 23:59:49 
11    32 2015-01-12 2015-01-11 23:59:28 
12    53 2015-01-13 2015-01-12 23:59:07 
13    45 2015-01-14 2015-01-13 23:59:15 
14    48 2015-01-15 2015-01-14 23:59:12 
15    23 2015-01-16 2015-01-15 23:59:37 
16    7 2015-01-17 2015-01-16 23:59:53 
17    5 2015-01-18 2015-01-17 23:59:55 
18    18 2015-01-19 2015-01-18 23:59:42 
19    26 2015-01-20 2015-01-19 23:59:34 
20    48 2015-01-21 2015-01-20 23:59:12 
21    8 2015-01-22 2015-01-21 23:59:52 
22    58 2015-01-23 2015-01-22 23:59:02 
23    24 2015-01-24 2015-01-23 23:59:36 
24    47 2015-01-25 2015-01-24 23:59:13 
25    10 2015-01-26 2015-01-25 23:59:50 
26    32 2015-01-27 2015-01-26 23:59:28 
27    26 2015-01-28 2015-01-27 23:59:34 
28    36 2015-01-29 2015-01-28 23:59:24 
29    36 2015-01-30 2015-01-29 23:59:24 
30    40 2015-01-31 2015-01-30 23:59:20 
31    18 2015-02-01 2015-01-31 23:59:42