更改 pandas 列的日期格式(月日年至日月年) [英] Change date format of pandas column (month-day-year to day-month-year)

问题描述

解决了以下问题.

我的熊猫中有一列，其中有一些日期和一些空值.

I have an column in my pandas with some dates and some empty values.

示例:

    1 - 3-20-2019
    2 - 
    3 - 2-25-2019 

等

我想将格式从month-day-year年转换为day-month-year年，当它为空时，我只想保留它为空.

I want to convert the format from month-day-year to day-month-year, and when its empty, i just want to keep it empty.

最快的方法是什么?

谢谢！

推荐答案

一个人可以使用字符串初始化当天的数据，然后将字符串转换为日期时间.然后，打印件可以按所需格式交付对象.

One can initialize the data for the days using strings, then convert the strings to datetimes. A print can then deliver the objects in the needed format.

我将使用其他格式(以点作为分隔符)，以便在步骤之间进行清晰的转换.

I will use an other format (with dots as separators), so that the conversion is clear between the steps.

首先提供示例代码:

import pandas as pd
data = {'day': ['3-20-2019', None, '2-25-2019'] }
df = pd.DataFrame( data )

df['day'] = pd.to_datetime(df['day'])
df['day'] = df['day'].dt.strftime('%d.%m.%Y')
df[ df == 'NaT' ] = '' 

---

对以上内容的评论. df 的第一个实例在ipython解释器中:

---

Comments on the above. The first instance of df is in the ipython interpreter:

In [56]: df['day']                                                  
Out[56]: 
0    3-20-2019
1         None
2    2-25-2019
Name: day, dtype: object

转换为日期时间后:

In [58]: df['day']                                               
Out[58]: 
0   2019-03-20
1          NaT
2   2019-02-25
Name: day, dtype: datetime64[ns]

这样我们就拥有

In [59]: df['day'].dt.strftime('%d.%m.%Y')
Out[59]: 
0    20.03.2019
1           NaT
2    25.02.2019
Name: day, dtype: object

那 NaT 出了问题.因此，我们将所有出现的内容替换为空字符串.

That NaT makes problems. So we replace all its occurrences with the empty string.

In [73]: df[ df=='NaT' ] = ''

In [74]: df
Out[74]: 
          day
0  20.03.2019
1            
2  25.02.2019

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