pandas datetime64列的中位数 [英] median of panda datetime64 column

问题描述

是否有一种方法可以计算并以datetime格式返回datetime列的中位数? 我想计算datetime64 [ns]格式的python列的中位数.下面是该列的示例:

Is there a way to compute and return in datetime format the median of a datetime column? I want to calculate the median of a column in python which is in datetime64[ns] format. Below is a sample to the column:

df['date'].head()

0   2017-05-08 13:25:13.342
1   2017-05-08 16:37:45.545
2   2017-01-12 11:08:04.021
3   2016-12-01 09:06:29.912
4   2016-06-08 03:16:40.422

名称:新近度，dtype:datetime64 [ns]

Name: recency, dtype: datetime64[ns]

我的目标是使中位数与上面的日期列具有相同的日期时间格式:

My aim is to have the median in same datetime format as the date column above:

尝试转换为np.array:

Tried converting to np.array:

median_ = np.median(np.array(df['date']))

但这会引发错误:

TypeError: ufunc add cannot use operands with types dtype('<M8[ns]') and dtype('<M8[ns]')

转换为int64，然后计算中位数，然后尝试将格式返回给datetime无效

Converting to int64 and then calculating the median and attempt to the return format to datetime does not work

df['date'].astype('int64').median().astype('datetime64[ns]')

推荐答案

仅取中间值怎么样?

dates = list(df.sort('date')['date'])
print dates[len(dates)//2]

如果表格已排序，您甚至可以跳过一行.

If the table is sorted you can even skip a line.

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