如何将以下字符串转换为datetime对象?
"Jun 1 2005 1:33PM"
如何将以下字符串转换为datetime对象?
"Jun 1 2005 1:33PM"
当前回答
Use:
emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv")
emp.info()
它显示“开始日期时间”列和“上次登录时间”都是数据帧中的“对象=字符串”:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name 933 non-null object
Gender 855 non-null object
Start Date 1000 non-null object
Last Login Time 1000 non-null object
Salary 1000 non-null int64
Bonus % 1000 non-null float64
Senior Management 933 non-null object
Team 957 non-null object
dtypes: float64(1), int64(1), object(6)
memory usage: 62.6+ KB
通过使用read_csv中的parse_dates选项,可以将字符串datetime转换为panda datetime格式。
emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv", parse_dates=["Start Date", "Last Login Time"])
emp.info()
输出:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name 933 non-null object
Gender 855 non-null object
Start Date 1000 non-null datetime64[ns]
Last Login Time 1000 non-null datetime64[ns]
Salary 1000 non-null int64
Bonus % 1000 non-null float64
Senior Management 933 non-null object
Team 957 non-null object
dtypes: datetime64[ns](2), float64(1), int64(1), object(4)
memory usage: 62.6+ KB
其他回答
如果您不想明确指定字符串相对于日期时间格式的格式,可以使用此黑客绕过该步骤:
from dateutil.parser import parse
# Function that'll guess the format and convert it into the python datetime format
def update_event(start_datetime=None, end_datetime=None, description=None):
if start_datetime is not None:
new_start_time = parse(start_datetime)
return new_start_time
# Sample input dates in different formats
d = ['06/07/2021 06:40:23.277000', '06/07/2021 06:40', '06/07/2021']
new = [update_event(i) for i in d]
for date in new:
print(date)
# Sample output dates in Python datetime object
# 2014-04-23 00:00:00
# 2013-04-24 00:00:00
# 2014-04-25 00:00:00
如果要将其转换为其他日期时间格式,只需使用您喜欢的格式修改最后一行,例如date.strftime(“%Y/%m/%d%H:%m:%S.%f”):
from dateutil.parser import parse
def update_event(start_datetime=None, end_datetime=None, description=None):
if start_datetime is not None:
new_start_time = parse(start_datetime)
return new_start_time
# Sample input dates in different formats
d = ['06/07/2021 06:40:23.277000', '06/07/2021 06:40', '06/07/2021']
# Passing the dates one by one through the function
new = [update_event(i) for i in d]
for date in new:
print(date.strftime('%Y/%m/%d %H:%M:%S.%f'))
# Sample output dates in required Python datetime object
# 2021/06/07 06:40:23.277000
# 2021/06/07 06:40:00.000000
# 2021/06/07 00:00:00.000000
尝试运行上面的代码段以获得更好的清晰度。
datetime.strptime将用户指定格式的输入字符串解析为时区原始日期时间对象:
>>> from datetime import datetime
>>> datetime.strptime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')
datetime.datetime(2005, 6, 1, 13, 33)
要使用现有的datetime对象获取日期对象,请使用.date()对其进行转换:
>>> datetime.strptime('Jun 1 2005', '%b %d %Y').date()
date(2005, 6, 1)
链接:
strptime文档:Python 2、Python 3strptime/strftime格式字符串文档:Python 2,Python 3strftime.org格式字符串备忘单
笔记:
strptime=“字符串解析时间”strftime=“字符串格式时间”
创建一个小的实用程序函数,如:
def date(datestr="", format="%Y-%m-%d"):
from datetime import datetime
if not datestr:
return datetime.today().date()
return datetime.strptime(datestr, format).date()
这是足够多功能的:
如果不传递任何参数,它将返回今天的日期。有一个日期格式作为默认值,您可以覆盖它。您可以轻松地修改它以返回日期时间。
您可以使用easy_date简化操作:
import date_converter
converted_date = date_converter.string_to_datetime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')
Use:
emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv")
emp.info()
它显示“开始日期时间”列和“上次登录时间”都是数据帧中的“对象=字符串”:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name 933 non-null object
Gender 855 non-null object
Start Date 1000 non-null object
Last Login Time 1000 non-null object
Salary 1000 non-null int64
Bonus % 1000 non-null float64
Senior Management 933 non-null object
Team 957 non-null object
dtypes: float64(1), int64(1), object(6)
memory usage: 62.6+ KB
通过使用read_csv中的parse_dates选项,可以将字符串datetime转换为panda datetime格式。
emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv", parse_dates=["Start Date", "Last Login Time"])
emp.info()
输出:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name 933 non-null object
Gender 855 non-null object
Start Date 1000 non-null datetime64[ns]
Last Login Time 1000 non-null datetime64[ns]
Salary 1000 non-null int64
Bonus % 1000 non-null float64
Senior Management 933 non-null object
Team 957 non-null object
dtypes: datetime64[ns](2), float64(1), int64(1), object(4)
memory usage: 62.6+ KB