Python 使用 pandas.to_datetime 转换时指定日期格式
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Specifying date format when converting with pandas.to_datetime
提问by cms_mgr
I have data in a csv file with dates stored as strings in a standard UK format - %d/%m/%Y- meaning they look like:
我在 csv 文件中有数据，日期以标准英国格式存储为字符串%d/%m/%Y- 意味着它们看起来像：
12/01/2012
30/01/2012
The examples above represent 12 January 2012 and 30 January 2012.
上述示例分别代表 2012 年 1 月 12 日和 2012 年 1 月 30 日。
When I import this data with pandas version 0.11.0 I applied the following transformation:
当我使用 Pandas 0.11.0 版导入此数据时，我应用了以下转换：
import pandas as pd
...
cpts.Date = cpts.Date.apply(pd.to_datetime)
but it converted dates inconsistently. To use my existing example, 12/01/2012 would convert as a datetime object representing 1 December 2012 but 30/01/2012 converts as 30 January 2012, which is what I want.
但它转换日期不一致。使用我现有的示例，12/01/2012 将转换为表示 2012 年 12 月 1 日的日期时间对象，但 30/01/2012 转换为 2012 年 1 月 30 日，这正是我想要的。
After looking at this questionI tried:
看了这个问题后，我试过：
cpts.Date = cpts.Date.apply(pd.to_datetime, format='%d/%m/%Y')
but the results are exactly the same. The source codesuggests I'm doing things right so I'm at a loss. Does anyone know what I'm doing wrong?
但结果是完全一样的。该源代码表明我正在做正确的事情，所以我无所适从。有谁知道我做错了什么？
采纳答案by joris
You can use the parse_datesoption from read_csvto do the conversion directly while reading you data.
The trick here is to use dayfirst=Trueto indicate your dates start with the day and not with the month. See here for more information: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.parsers.read_csv.html
您可以在读取数据时使用parse_datesfrom 选项read_csv直接进行转换。
这里的技巧是用来dayfirst=True指示您的日期以日期而不是月份开头。有关更多信息，请参见此处：http: //pandas.pydata.org/pandas-docs/dev/generated/pandas.io.parsers.read_csv.html
When your dates have to be the index:
当您的日期必须是索引时：
>>> import pandas as pd
>>> from StringIO import StringIO
>>> s = StringIO("""date,value
... 12/01/2012,1
... 12/01/2012,2
... 30/01/2012,3""")
>>>
>>> pd.read_csv(s, index_col=0, parse_dates=True, dayfirst=True)
value
date
2012-01-12 1
2012-01-12 2
2012-01-30 3
Or when your dates are just in a certain column:
或者当您的日期仅在某一列中时：
>>> s = StringIO("""date
... 12/01/2012
... 12/01/2012
... 30/01/2012""")
>>>
>>> pd.read_csv(s, parse_dates=[0], dayfirst=True)
date
0 2012-01-12 00:00:00
1 2012-01-12 00:00:00
2 2012-01-30 00:00:00
回答by Andy Hayden
I think you are calling it correctly, and I posted this as an issue on github.
我认为您正确地调用了它，并且我将此作为问题发布在 github 上。
You can just specify the format to to_datetimedirectly, for example:
您可以直接指定格式to_datetime，例如：
In [1]: s = pd.Series(['12/1/2012', '30/01/2012'])
In [2]: pd.to_datetime(s, format='%d/%m/%Y')
Out[2]:
0 2012-01-12 00:00:00
1 2012-01-30 00:00:00
dtype: datetime64[ns]
Update: As OP correctly points out this doesn't work with NaN, if you are happy with dayfirst=True(which works with NaN too):
更新：正如 OP 正确指出这不适用于 NaN，如果您满意dayfirst=True（也适用于 NaN）：
s.apply(pd.to_datetime, dayfirst=True)
Worth noting that have to be careful using dayfirst(which is easier than specifying the exact format), since dayfirstisn't strict.
值得注意的是必须小心使用dayfirst（这比指定确切格式更容易），因为dayfirst不是严格的。
