在熊猫中两个特定日期时间范围之间出现数字

Question

我有2个CSV文件，如下。

1. 我想要一个新列Difference，在这里...
   • 如果手机号码出现在Book_date ... App_date的日期范围内：Difference =差额App_date和Occur_date
   • 或NaN（如果未在该日期范围内发生）。
2. 我也想根据唯一的类别和mobile_number对其进行过滤

csv_1

Mobile_Number    Book_Date       App_Date

503477334    2018-10-12       2018-10-18
506002884    2018-10-12       2018-10-19
501022162    2018-10-12       2018-10-16
503487338    2018-10-13       2018-10-13
506012887    2018-10-13       2018-10-21
503427339    2018-10-14       2018-10-17

csv_2

Mobile_Number    Occur_Date    

503477334        2018-10-16
506002884        2018-10-21
501022162        2018-10-15
503487338        2018-10-13
501428449        2018-10-18
506012887        2018-10-14

我想在csv_1中添加一个新列，如果移动电话号码出现在csv_2中Book_date和App_date的日期范围内，则App_date与Occur_date或NaN之间的差异（如果该日期范围内没有出现）。输出应为

输出

Mobile_Number    Book_Date       App_Date   Difference

503477334    2018-10-12       2018-10-18       2
506002884    2018-10-12       2018-10-19      -2
501022162    2018-10-12       2018-10-16       1
503487338    2018-10-13       2018-10-13       0
506012887    2018-10-13       2018-10-21       7 
503427339    2018-10-14       2018-10-17       NaN

编辑：

如果要根据上述两个csv文件中的唯一类别和mobile_number对其进行过滤。怎么做？

csv_1

Category     Mobile_Number   Book_Date       App_Date

A              503477334    2018-10-12       2018-10-18
B              503477334    2018-10-07       2018-10-16
C              501022162    2018-10-12       2018-10-16
A              503487338    2018-10-13       2018-10-13
C              506012887    2018-10-13       2018-10-21
E              503427339    2018-10-14       2018-10-17

csv_2

Category     Mobile_Number    Occur_Date    

A              503477334        2018-10-16
B              503477334        2018-10-13
A              501022162        2018-10-15
A              503487338        2018-10-13
F              501428449        2018-10-18
C              506012887        2018-10-14

我希望根据移动电话号码和类别对输出进行过滤

输出

Category     Mobile_Number    Book_Date       App_Date   Difference

A              503477334    2018-10-12       2018-10-18       2
B              503477334    2018-10-07       2018-10-16       3
C              501022162    2018-10-12       2018-10-16       NaN
A              503487338    2018-10-13       2018-10-13       0
C              506012887    2018-10-13       2018-10-21       7 
E              503427339    2018-10-14       2018-10-17       NaN

Answer 1

将Series.map用于与Series.map匹配的新Series，并将列之间的测试值使用Mobile_Number，然后使用Series.between通过掩码分配值：

Series.between

编辑：

您可以使用numpy.where代替numpy.where进行2列联接：

df1['Book_Date'] = pd.to_datetime(df1['Book_Date'])
df1['App_Date'] = pd.to_datetime(df1['App_Date'])
df2['Occur_Date'] = pd.to_datetime(df2['Occur_Date'])

s1 = df2.drop_duplicates('Mobile_Number').set_index('Mobile_Number')['Occur_Date']
s2 = df1['Mobile_Number'].map(s1)

m = s2.between(df1['Book_Date'], df1['App_Date'])

#solution with no mask
df1['Difference1'] = df1['App_Date'].sub(s2).dt.days
#solution with test between
df1['Difference2'] = np.where(m, df1['App_Date'].sub(s2).dt.days, np.nan)
print (df1)
   Mobile_Number  Book_Date   App_Date Difference  Difference1  Difference2
0      503477334 2018-10-12 2018-10-18 2018-10-16          2.0          2.0
1      506002884 2018-10-12 2018-10-19 2018-10-21         -2.0          NaN
2      501022162 2018-10-12 2018-10-16 2018-10-15          1.0          1.0
3      503487338 2018-10-13 2018-10-13 2018-10-13          0.0          0.0
4      506012887 2018-10-13 2018-10-21 2018-10-14          7.0          7.0
5      503427339 2018-10-14 2018-10-17        NaT          NaN          NaN