如何在Python中从.xlsx读取时间？

我正在尝试使用Pandas读取一个.xlsx文件，该文件包含两列：时间持续时间（mm:ss.cs）和日期（mm/dd/yyyy）：

我只是简单地调用了

read\u excel（）

：

执行后，df为：

Time    Date
0   1900-02-02 12:01:28.010000  1/11/2020
1   1900-02-02 12:01:47.250000  5/19/2019
2   1900-02-02 12:01:57.160000  1/11/2020
3   1900-02-02 12:01:20.820000  1/12/2020
4   1900-02-02 12:01:25.180000  3/2/2019
5   1900-02-02 12:01:52     12/17/2017
6   1900-02-02 12:01:34.820000  5/19/2019
7   1900-02-02 12:01:29.020000  2/29/2020
8   1900-02-02 12:02:14.420000  11/17/2017
9   1900-02-02 12:01:44.160000  6/30/2019
10  1900-02-02 12:01:38.900000  6/29/2019
11  1900-02-02 12:01:35.830000  11/16/2019
12  1900-02-02 12:01:39.990000  1/13/2019
13  1900-02-02 12:01:43.040000  3/3/2019
14  1900-02-02 12:01:51     10/13/2018
15  1900-02-02 12:02:52.830000  1/12/2020
16  33.50048090277778   3/1/2020
17  33.50048275462963   11/16/2019
18  33.50049525462963   4/13/2019
19  33.50051261574074   10/13/2019
20  33.50052291666667   1/13/2019
21  33.50052708333333   12/16/2018
22  33.50053125     3/3/2019
23  33.500586921296296  10/14/2018
24  1900-02-02 12:01:04.010000  1/21/2018
25  1900-02-02 12:01:04.580000  10/15/2017
26  1900-02-02 12:01:06.100000  11/18/2017
27  33.500560416666666  6/29/2019
28  33.50057731481481   6/1/2019
29  33.5005806712963    7/7/2019
30  1900-02-02 12:01:00.750000  6/2/2018
31  33.500622800925925  1/12/2020
32  33.500644791666666  10/13/2019
33  33.50069386574074   1/12/2019
34  1900-02-02 12:01:06.200000  7/7/2019
35  1900-02-02 12:01:08.090000  5/19/2019
36  33.50058518518519   12/15/2018
37  1900-02-02 12:01:14.410000  12/17/2017
38  33.50064733796296   6/1/2019
39  1900-02-02 12:01:16.870000  6/2/2018
40  33.50038888888889   2/29/2020
41  33.50040439814815   1/11/2020
42  33.50040833333333   11/16/2019
43  33.50044074074074   1/12/2019
44  33.50044988425926   3/3/2019
45  33.50046435185185   12/16/2018
46  33.50046759259259   10/13/2018
47  33.50056875     12/16/2017
48  33.500570949074074  1/20/2018
49  33.50060983796296   11/17/2017
50  33.50061643518519   10/14/2017
51  33.50050231481482   7/7/2019
52  33.500507407407405  6/1/2019
53  33.500539699074075  6/2/2018
54  33.500544328703704  6/30/2019

有些时间是通过添加前缀“1900-02-02 12:”转换的，有些时间被更改为“33.5xxxxxxx”，这看起来完全是一团糟

我尝试将时间作为字符串来读取，但得到了相同的df：

df = pd.read_excel(r'test.xlsx', dtype={'Time':str})

---

因此，我的问题是：如何正确读取时间值-保持其原始格式，将其转换为毫秒或Python识别的任何时间格式？

我将使用

Time

像您一样读取数据，并解析字符串：

df = pd.read_excel(r'test.xlsx', dtype={'Time':str})

# parse the string time
min_sec = ('0:' + df.Time).str.extract('(?P<min>\d+):(?P<sec>[\d\.]+)$').astype(float)

# convert to timedelta
df['Time'] = pd.to_timedelta(min_sec['min'] * 60 + min_sec['sec'], unit='s')

---

我会像你一样用

时间

字符串读取数据，并解析字符串：

df = pd.read_excel(r'test.xlsx', dtype={'Time':str})

# parse the string time
min_sec = ('0:' + df.Time).str.extract('(?P<min>\d+):(?P<sec>[\d\.]+)$').astype(float)

# convert to timedelta
df['Time'] = pd.to_timedelta(min_sec['min'] * 60 + min_sec['sec'], unit='s')

---

相关的：相关的：按预期工作。谢谢，一切正常。非常感谢。

df = pd.read_excel(r'test.xlsx', dtype={'Time':str})

# parse the string time
min_sec = ('0:' + df.Time).str.extract('(?P<min>\d+):(?P<sec>[\d\.]+)$').astype(float)

# convert to timedelta
df['Time'] = pd.to_timedelta(min_sec['min'] * 60 + min_sec['sec'], unit='s')

# min_sec
    min    sec
0   1.0  28.01
1   1.0  29.02
2   2.0  14.42
3   1.0  44.16
4   1.0  47.25
5   1.0  57.16
6   1.0  20.82
7   1.0  25.18
8   1.0  52.00
9   1.0  34.82
10  1.0  38.90
11  1.0  35.83
12  1.0  39.99
13  1.0  43.04
14  1.0  51.00
15  2.0  52.83
16  0.0  41.55
17  0.0  41.71
18  0.0  42.79
19  0.0  44.29

# df
             Time        Date
0 00:01:28.010000   1/11/2020
1 00:01:29.020000   2/29/2020
2 00:02:14.420000  11/17/2017
3 00:01:44.160000   6/30/2019
4 00:01:47.250000   5/19/2019