Attempting to find the 5 largest values per month using groupby

问题

I am attempting to show the top three values of nc_type for each month. I tried using n_largest but that doesn't do it by date.

Original Data:

     area                                     nc_type    occurred_date  
0     Filling                                 x          12/23/2015 0:00   
1     Filling                                 f          12/22/2015 0:00   
2     Filling                                 s          9/11/2015 0:00   
3     Filling                                 f          2/17/2016 0:00   
4     Filling                                 s          5/3/2016 0:00   
5     Filling                                 g          8/29/2016 0:00   
6     Filling                                 f          9/9/2016 0:00   
7     Filling                                 a          6/1/2016 0:00

Transformed with:

df.groupby([df.occurred_date.dt.month, "nc_type"])["rand"].count()

Transformed Data:

occurred_date  nc_type                                   
1              x                            3
               y                            4
               z                           13
               w                           24
               f                           34
                                           ..
12             d                           18
               g                           10
               w                           44
               a                           27
               g                           42

回答1:

Scenario 1
MultiIndex series

occurred_date  nc_type
1.0            x           3
               y           4
               z          13
               w          24
               f          34
12.0           d          18
               g          10
               w          44
               a          27
               g          42
Name: test, dtype: int64

Call sort_values + groupby + head:

df.sort_values(ascending=False).groupby(level=0).head(2)

occurred_date  nc_type
12.0           w          44
               g          42
1.0            f          34
               w          24
Name: test, dtype: int64

Change head(2) to head(5) for your situation.

Or, expanding upon my comment with nlargest, you could do:

df.groupby(level=0).nlargest(2).reset_index(level=0, drop=1)

occurred_date  nc_type
1.0            f          34
               w          24
12.0           w          44
               g          42
Name: test, dtype: int64

---

Scenario 2
3-col dataframe

   occurred_date nc_type  value
0            1.0       x      3
1            1.0       y      4
2            1.0       z     13
3            1.0       w     24
4            1.0       f     34
5           12.0       d     18
6           12.0       g     10
7           12.0       w     44
8           12.0       a     27
9           12.0       g     42

You can use sort_values + groupby + head:

df.sort_values(['occurred_date', 'value'], 
        ascending=[True, False]).groupby('occurred_date').head(2)

   occurred_date nc_type  value
4            1.0       f     34
3            1.0       w     24
7           12.0       w     44
9           12.0       g     42

Change head(2) to head(5) for your scenario.

---

Scenario 3
MultiIndex Dataframe

                       test
occurred_date nc_type      
1.0           x           3
              y           4
              z          13
              w          24
              f          34
12.0          d          18
              g          10
              w          44
              a          27
              g          42

Or, with nlargest.

df.groupby(level=0).test.nlargest(2)\
              .reset_index(level=0, drop=1)

occurred_date  nc_type
1.0            f          34
               w          24
12.0           w          44
               g          42
Name: test, dtype: int64

回答2:

I'd include group_keys=False

df.groupby('occurred_date', group_keys=False).nlargest(3)

occurred_date  nc_type
1.0            f          34
               w          24
               z          13
12.0           w          44
               g          42
               a          27
Name: value, dtype: int64

来源：https://stackoverflow.com/questions/46880050/attempting-to-find-the-5-largest-values-per-month-using-groupby