如何在Pandas Python中将字符串转换为日期时间格式? [英] How to convert string to datetime format in pandas python?

问题描述

我在名为train的数据框中有一个string(object)类型的I_DATE列，如下所示.

I have a column I_DATE of type string(object) in a dataframe called train as show below.

I_DATE
28-03-2012  2:15:00 PM
28-03-2012  2:17:28 PM
28-03-2012  2:50:50 PM

如何将I_DATE从字符串转换为数据时间格式&指定输入字符串的格式.我看到了一些答案，但不是AM/PM格式.

How to convert I_DATE from string to datatime format & specify the format of input string. I saw some answers to this but its not for AM/PM format.

此外，如何根据熊猫中的日期范围过滤行?

Also, how to filter rows based on a range of dates in pandas?

推荐答案

使用

Use to_datetime, there is no need for a format string the parser is man/woman enough to handle it:

In [51]:
pd.to_datetime(df['I_DATE'])

Out[51]:
0   2012-03-28 14:15:00
1   2012-03-28 14:17:28
2   2012-03-28 14:50:50
Name: I_DATE, dtype: datetime64[ns]

要访问日期/日期/时间组件，请使用 dt 访问者:

To access the date/day/time component use the dt accessor:

In [54]:
df['I_DATE'].dt.date

Out[54]:
0    2012-03-28
1    2012-03-28
2    2012-03-28
dtype: object

In [56]:    
df['I_DATE'].dt.time

Out[56]:
0    14:15:00
1    14:17:28
2    14:50:50
dtype: object

您可以使用字符串进行过滤:

You can use strings to filter as an example:

In [59]:
df = pd.DataFrame({'date':pd.date_range(start = dt.datetime(2015,1,1), end = dt.datetime.now())})
df[(df['date'] > '2015-02-04') & (df['date'] < '2015-02-10')]

Out[59]:
         date
35 2015-02-05
36 2015-02-06
37 2015-02-07
38 2015-02-08
39 2015-02-09

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