[英]Handle multiple date formats in pandas dataframe
I have a dataframe (imported from Excel) which looks like this: 我有一个数据框(从Excel导入),看起来像这样:
Date Period
0 2017-03-02 2017-03-01 00:00:00
1 2017-03-02 2017-04-01 00:00:00
2 2017-03-02 2017-05-01 00:00:00
3 2017-03-02 2017-06-01 00:00:00
4 2017-03-02 2017-07-01 00:00:00
5 2017-03-02 2017-08-01 00:00:00
6 2017-03-02 2017-09-01 00:00:00
7 2017-03-02 2017-10-01 00:00:00
8 2017-03-02 2017-11-01 00:00:00
9 2017-03-02 2017-12-01 00:00:00
10 2017-03-02 Q217
11 2017-03-02 Q317
12 2017-03-02 Q417
13 2017-03-02 Q118
14 2017-03-02 Q218
15 2017-03-02 Q318
16 2017-03-02 Q418
17 2017-03-02 2018
I am trying to convert all the 'Period' column into a consistent format. 我正在尝试将所有“期间”列转换为一致的格式。 Some elements look already in the datetime format, others are converted to string (ex. Q217), others to int (ex 2018).
有些元素已经以日期时间格式显示,另一些元素转换为字符串(例如Q217),另一些元素转换为int(例如2018年)。 Which is the fastest way to convert everything in a datetime?
在日期时间转换所有内容的最快方法是什么? I was trying with some masking, like this:
我正在尝试进行一些遮罩,如下所示:
mask = df['Period'].str.startswith('Q', na = False)
list_quarter = df_final[mask]['Period'].tolist()
quarter_convert = {'1':'31/03', '2':'30/06', '3':'31/08', '4':'30/12'}
counter = 0
for element in list_quarter:
element = element[1:]
quarter = element[0]
year = element[1:]
daymonth = ''.join(str(quarter_convert.get(word, word)) for word in quarter)
final = daymonth+'/'+year
list_quarter[counter] = final
counter+=1
However it fails when I try to substitute the modified elements in the original column: 但是,当我尝试替换原始列中的已修改元素时,它会失败:
df_nwe_final['Period'] = np.where(mask, pd.Series(list_quarter), df_nwe_final['Period'])
Of course I would need to do more or less the same with the 2018 type formats. 当然,我将需要对2018年的字体类型做更多或更少的事情。 However, I am sure I am missing something here, and there should be a much faster solution.
但是,我确定我在这里遗漏了一些东西,应该有一个更快的解决方案。 Some fresh ideas from you would help!
您的一些新想法会有所帮助! Thank you.
谢谢。
Reusing the code you show, let's first write a function that converts the Q
-string to a datetime format (I adjusted to final format a little bit): 重用显示的代码,让我们首先编写一个将
Q
字符串转换为日期时间格式的函数(我将其稍微调整为最终格式):
def convert_q_string(element):
quarter_convert = {'1':'03-31', '2':'06-30', '3':'08-31', '4':'12-30'}
element = element[1:]
quarter = element[0]
year = element[1:]
daymonth = ''.join(str(quarter_convert.get(word, word)) for word in quarter)
final = '20' + year + '-' + daymonth
return final
We can now use this to first convert all 'Q'-strings, and then pd.to_datetime
to convert all elements to proper datetime values: 现在,我们可以使用它首先转换所有“
pd.to_datetime
”字符串,然后使用pd.to_datetime
将所有元素转换为正确的datetime值:
In [2]: s = pd.Series(['2017-03-01 00:00:00', 'Q217', '2018'])
In [3]: mask = s.str.startswith('Q')
In [4]: s[mask] = s[mask].map(convert_q_string)
In [5]: s
Out[5]:
0 2017-03-01 00:00:00
1 2017-06-30
2 2018
dtype: object
In [6]: pd.to_datetime(s)
Out[6]:
0 2017-03-01
1 2017-06-30
2 2018-01-01
dtype: datetime64[ns]
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.