pandas -将数据框多索引转换为日期时间对象 [英] Pandas - convert dataframe multi-index to datetime object

问题描述

考虑输入文件b.dat:

string,date,number
a string,2/5/11 9:16am,1.0
a string,3/5/11 10:44pm,2.0
a string,4/22/11 12:07pm,3.0
a string,4/22/11 12:10pm,4.0
a string,4/29/11 11:59am,1.0
a string,5/2/11 1:41pm,2.0
a string,5/2/11 2:02pm,3.0
a string,5/2/11 2:56pm,4.0
a string,5/2/11 3:00pm,5.0
a string,5/2/14 3:02pm,6.0
a string,5/2/14 3:18pm,7.0

我可以像这样对每月总计进行分组:

I can group monthly totals like so:

b=pd.read_csv('b.dat')
b['date']=pd.to_datetime(b['date'],format='%m/%d/%y %I:%M%p')
b.index=b['date']
bg=pd.groupby(b,by=[b.index.year,b.index.month])
bgs=bg.sum()

分组总数的索引如下:

bgs

            number
2011 2       1
     3       2
     4       8
     5      14
2014 5      13

bgs.index

MultiIndex(levels=[[2011, 2014], [2, 3, 4, 5]],
       labels=[[0, 0, 0, 0, 1], [0, 1, 2, 3, 3]])

我想将索引重新格式化为日期时间格式(天可以是一个月的第一天).

I'd like to reformat the index into date time format (days can be first of month).

我尝试了以下操作:

bgs.index = pd.to_datetime(bgs.index)

和

bgs.index = pd.DatetimeIndex(bgs.index)

均失败.有人知道我该怎么做吗?

Both fail. Does anyone know how I can do this?

推荐答案

考虑通过"M"进行重采样，而不是按照DatetimeIndex的属性进行分组:

Consider resample by 'M' rather than grouping by attributes of the DatetimeIndex:

In [11]: b.resample('M', how='sum').dropna()
Out[11]:
            number
date
2011-02-28       1
2011-03-31       2
2011-04-30       8
2011-05-31      14
2014-05-31      13

注意:如果您不想在两个月之间输入月份，则必须删除NaN.

Note: you have to drop the NaN if you don't want the months in between.

这篇关于 pandas -将数据框多索引转换为日期时间对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！