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python - ValueError: 时间数据与格式 '%Y/%m/%d %H:%M:%S' 不匹配

问题描述

我需要将数千个日期时间记录转换为时间戳。当我运行以下代码时：

from time import strptime
import pandas as pd
import numpy as np
import datetime

cepo = str(pd.read_csv('test.csv',sep = ',')) 
print(cepo)
element = datetime.datetime.strptime(cepo,'%Y/%m/%d %H:%M:%S')
tuple = element.timetuple()
timestamp = time.mktime(tuple)
print(timestamp)

我收到以下错误：

     Date      Time
0  2018/02/11  02:36:59
1  2018/02/12  00:47:11
2  2018/02/12  01:36:36
3  2018/02/12  03:27:51
4  2018/02/12  03:48:29
5  2018/02/12  03:50:49
Traceback (most recent call last):
  File "epoch.py", line 9, in <module>
    element = datetime.datetime.strptime(cepo,'%Y/%m/%d %H:%M:%S')
  File "/Users/joaofontiela/opt/anaconda3/envs/pywork/lib/python3.8/_strptime.py", line 568, in _strptime_datetime
    tt, fraction, gmtoff_fraction = _strptime(data_string, format)
  File "/Users/joaofontiela/opt/anaconda3/envs/pywork/lib/python3.8/_strptime.py", line 349, in _strptime
    raise ValueError("time data %r does not match format %r" %
ValueError: time data '         Date      Time\n0  2018/02/11  02:36:59\n1  2018/02/12  00:47:11\n2  2018/02/12  01:36:36\n3  2018/02/12  03:27:51\n4  2018/02/12  03:48:29\n5  2018/02/12  03:50:49' does not match format '%Y/%m/%d %H:%M:%S'

输入文件（test.csv）看起来像：

Date,Time
2018/02/11,02:36:59
2018/02/12,00:47:11
2018/02/12,01:36:36
2018/02/12,03:27:51
2018/02/12,03:48:29
2018/02/12,03:50:49

如何修复错误“时间数据与格式'%Y/%m/%d %H:%M:%S”不匹配？

标签： pythonpandasdatetimetimestampstrptime

解决方案

您可以轻松地组合 csv 中的“日期”和“时间”列，例如

# load the csv file content into a dataframe
df = pd.read_csv(filename)

# combine date and time columns and parse to datetime
df['datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])

print(df['datetime'])
0   2018-02-11 02:36:59
1   2018-02-12 00:47:11
2   2018-02-12 01:36:36
3   2018-02-12 03:27:51
4   2018-02-12 03:48:29
5   2018-02-12 03:50:49
Name: datetime, dtype: datetime64[ns]

您可以计算 Unix 时间（自 1970-01-01 UTC 以来的 Posix / 秒），例如

df['posix[s]'] = df['datetime'].astype(int) / 1e9

print(df['posix[s]'])
0    1.518317e+09
1    1.518396e+09
2    1.518399e+09
3    1.518406e+09
4    1.518407e+09
5    1.518407e+09
Name: posix[s], dtype: float64

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