Pandas：减去两个日期列，结果为整数 [英] Pandas: Subtracting two date columns and the result being an integer

问题描述

我在Pandas数据框中有两列是日期。

I have two columns in a Pandas data frame that are dates.

我希望从另一列中减去一列，结果是天数作为整数的差异。

I am looking to subtract one column from another and the result being the difference in numbers of days as an integer.

查看数据：

df_test.head(10)
Out[20]: 
  First_Date Second Date
0 2016-02-09  2015-11-19
1 2016-01-06  2015-11-30
2        NaT  2015-12-04
3 2016-01-06  2015-12-08
4        NaT  2015-12-09
5 2016-01-07  2015-12-11
6        NaT  2015-12-12
7        NaT  2015-12-14
8 2016-01-06  2015-12-14
9        NaT  2015-12-15

我已成功创建了一个新列，区别于：

I have created a new column successfully with the difference:

df_test['Difference'] = df_test['First_Date'].sub(df_test['Second Date'], axis=0)
df_test.head()         
Out[22]: 
  First_Date Second Date  Difference
0 2016-02-09  2015-11-19     82 days
1 2016-01-06  2015-11-30     37 days
2        NaT  2015-12-04         NaT
3 2016-01-06  2015-12-08     29 days
4        NaT  2015-12-09         NaT

但是我无法获得结果的数字版本：

However I am unable to get a numeric version of the result:

df_test['Difference'] = df_test[['Difference']].apply(pd.to_numeric)     

df_test.head()
Out[25]: 
  First_Date Second Date    Difference
0 2016-02-09  2015-11-19  7.084800e+15
1 2016-01-06  2015-11-30  3.196800e+15
2        NaT  2015-12-04           NaN
3 2016-01-06  2015-12-08  2.505600e+15
4        NaT  2015-12-09           NaN

推荐答案

您可以划分 dtype timedelta by np.timedelta64（1，'D'），但输出不是 int ，但 float ，因为 NaN 值：

You can divide column of dtype timedelta by np.timedelta64(1, 'D'), but output is not int, but float, because NaN values:

df_test['Difference'] = df_test['Difference'] / np.timedelta64(1, 'D')
print (df_test)
  First_Date Second Date  Difference
0 2016-02-09  2015-11-19        82.0
1 2016-01-06  2015-11-30        37.0
2        NaT  2015-12-04         NaN
3 2016-01-06  2015-12-08        29.0
4        NaT  2015-12-09         NaN
5 2016-01-07  2015-12-11        27.0
6        NaT  2015-12-12         NaN
7        NaT  2015-12-14         NaN
8 2016-01-06  2015-12-14        23.0
9        NaT  2015-12-15         NaN

频率转换。

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