Select One Row Per Index Value with Max Column Value

Select one row per index value with max column value

From recent versions of DB2, you can use the analytical function ROW_NUMBER()

SELECT * 
FROM (
    SELECT 
        tablename.*, 
        ROW_NUMBER() OVER (PARTITION BY sku 
                           ORDER BY eventstartdate DESC) As RowNum
        FROM tablename) X 
WHERE X.RowNum=1

For each Partition (group of SKU), the data is row numbered following the order by eventstartdate desc, so 1,2,3,...starting from 1 for the latest EventStartDate. The WHERE clause then picks up only the latest per SKU.

SQL select only rows with max value on a column

At first glance...

All you need is a GROUP BY clause with the MAX aggregate function:

SELECT id, MAX(rev)
FROM YourTable
GROUP BY id

It's never that simple, is it?

I just noticed you need the content column as well.

This is a very common question in SQL: find the whole data for the row with some max value in a column per some group identifier. I heard that a lot during my career. Actually, it was one the questions I answered in my current job's technical interview.

It is, actually, so common that Stack Overflow community has created a single tag just to deal with questions like that: greatest-n-per-group.

Basically, you have two approaches to solve that problem:

Joining with simple group-identifier, max-value-in-group Sub-query

In this approach, you first find the group-identifier, max-value-in-group (already solved above) in a sub-query. Then you join your table to the sub-query with equality on both group-identifier and max-value-in-group:

SELECT a.id, a.rev, a.contents
FROM YourTable a
INNER JOIN (
    SELECT id, MAX(rev) rev
    FROM YourTable
    GROUP BY id
) b ON a.id = b.id AND a.rev = b.rev

Left Joining with self, tweaking join conditions and filters

In this approach, you left join the table with itself. Equality goes in the group-identifier. Then, 2 smart moves:

1. The second join condition is having left side value less than right value
2. When you do step 1, the row(s) that actually have the max value will have NULL in the right side (it's a LEFT JOIN, remember?). Then, we filter the joined result, showing only the rows where the right side is NULL.

So you end up with:

SELECT a.*
FROM YourTable a
LEFT OUTER JOIN YourTable b
    ON a.id = b.id AND a.rev < b.rev
WHERE b.id IS NULL;

Conclusion

Both approaches bring the exact same result.

If you have two rows with max-value-in-group for group-identifier, both rows will be in the result in both approaches.

Both approaches are SQL ANSI compatible, thus, will work with your favorite RDBMS, regardless of its "flavor".

Both approaches are also performance friendly, however your mileage may vary (RDBMS, DB Structure, Indexes, etc.). So when you pick one approach over the other, benchmark. And make sure you pick the one which make most of sense to you.

Find row where values for column is maximal in a pandas DataFrame

Use the pandas idxmax function. It's straightforward:

>>> import pandas
>>> import numpy as np
>>> df = pandas.DataFrame(np.random.randn(5,3),columns=['A','B','C'])
>>> df
          A         B         C
0  1.232853 -1.979459 -0.573626
1  0.140767  0.394940  1.068890
2  0.742023  1.343977 -0.579745
3  2.125299 -0.649328 -0.211692
4 -0.187253  1.908618 -1.862934
>>> df['A'].idxmax()
3
>>> df['B'].idxmax()
4
>>> df['C'].idxmax()
1

• Alternatively you could also use numpy.argmax, such as numpy.argmax(df['A']) -- it provides the same thing, and appears at least as fast as idxmax in cursory observations.

• idxmax() returns indices labels, not integers.

• Example': if you have string values as your index labels, like rows 'a' through 'e', you might want to know that the max occurs in row 4 (not row 'd').

• if you want the integer position of that label within the Index you have to get it manually (which can be tricky now that duplicate row labels are allowed).

---

HISTORICAL NOTES:

• idxmax() used to be called argmax() prior to 0.11
• argmax was deprecated prior to 1.0.0 and removed entirely in 1.0.0
• back as of Pandas 0.16, argmax used to exist and perform the same function (though appeared to run more slowly than idxmax).
• argmax function returned the integer position within the index of the row location of the maximum element.
• pandas moved to using row labels instead of integer indices. Positional integer indices used to be very common, more common than labels, especially in applications where duplicate row labels are common.

For example, consider this toy DataFrame with a duplicate row label:

In [19]: dfrm
Out[19]: 
          A         B         C
a  0.143693  0.653810  0.586007
b  0.623582  0.312903  0.919076
c  0.165438  0.889809  0.000967
d  0.308245  0.787776  0.571195
e  0.870068  0.935626  0.606911
f  0.037602  0.855193  0.728495
g  0.605366  0.338105  0.696460
h  0.000000  0.090814  0.963927
i  0.688343  0.188468  0.352213
i  0.879000  0.105039  0.900260

In [20]: dfrm['A'].idxmax()
Out[20]: 'i'

In [21]: dfrm.iloc[dfrm['A'].idxmax()]  # .ix instead of .iloc in older versions of pandas
Out[21]: 
          A         B         C
i  0.688343  0.188468  0.352213
i  0.879000  0.105039  0.900260

So here a naive use of idxmax is not sufficient, whereas the old form of argmax would correctly provide the positional location of the max row (in this case, position 9).

This is exactly one of those nasty kinds of bug-prone behaviors in dynamically typed languages that makes this sort of thing so unfortunate, and worth beating a dead horse over. If you are writing systems code and your system suddenly gets used on some data sets that are not cleaned properly before being joined, it's very easy to end up with duplicate row labels, especially string labels like a CUSIP or SEDOL identifier for financial assets. You can't easily use the type system to help you out, and you may not be able to enforce uniqueness on the index without running into unexpectedly missing data.

So you're left with hoping that your unit tests covered everything (they didn't, or more likely no one wrote any tests) -- otherwise (most likely) you're just left waiting to see if you happen to smack into this error at runtime, in which case you probably have to go drop many hours worth of work from the database you were outputting results to, bang your head against the wall in IPython trying to manually reproduce the problem, finally figuring out that it's because idxmax can only report the label of the max row, and then being disappointed that no standard function automatically gets the positions of the max row for you, writing a buggy implementation yourself, editing the code, and praying you don't run into the problem again.

Find the column name which has the maximum value for each row

You can use idxmax with axis=1 to find the column with the greatest value on each row:

>>> df.idxmax(axis=1)
0    Communications
1          Business
2    Communications
3    Communications
4          Business
dtype: object

To create the new column 'Max', use df['Max'] = df.idxmax(axis=1).

To find the row index at which the maximum value occurs in each column, use df.idxmax() (or equivalently df.idxmax(axis=0)).

How can I SELECT rows with MAX(Column value), PARTITION by another column in MYSQL?

You are so close! All you need to do is select BOTH the home and its max date time, then join back to the topten table on BOTH fields:

SELECT tt.*
FROM topten tt
INNER JOIN
    (SELECT home, MAX(datetime) AS MaxDateTime
    FROM topten
    GROUP BY home) groupedtt 
ON tt.home = groupedtt.home 
AND tt.datetime = groupedtt.MaxDateTime

Get the row(s) which have the max value in groups using groupby

In [1]: df
Out[1]:
    Sp  Mt Value  count
0  MM1  S1     a      3
1  MM1  S1     n      2
2  MM1  S3    cb      5
3  MM2  S3    mk      8
4  MM2  S4    bg     10
5  MM2  S4   dgd      1
6  MM4  S2    rd      2
7  MM4  S2    cb      2
8  MM4  S2   uyi      7

In [2]: df.groupby(['Mt'], sort=False)['count'].max()
Out[2]:
Mt
S1     3
S3     8
S4    10
S2     7
Name: count

To get the indices of the original DF you can do:

In [3]: idx = df.groupby(['Mt'])['count'].transform(max) == df['count']

In [4]: df[idx]
Out[4]:
    Sp  Mt Value  count
0  MM1  S1     a      3
3  MM2  S3    mk      8
4  MM2  S4    bg     10
8  MM4  S2   uyi      7

Note that if you have multiple max values per group, all will be returned.

Update

On a hail mary chance that this is what the OP is requesting:

In [5]: df['count_max'] = df.groupby(['Mt'])['count'].transform(max)

In [6]: df
Out[6]:
    Sp  Mt Value  count  count_max
0  MM1  S1     a      3          3
1  MM1  S1     n      2          3
2  MM1  S3    cb      5          8
3  MM2  S3    mk      8          8
4  MM2  S4    bg     10         10
5  MM2  S4   dgd      1         10
6  MM4  S2    rd      2          7
7  MM4  S2    cb      2          7
8  MM4  S2   uyi      7          7

Get records with max value for each group of grouped SQL results

There's a super-simple way to do this in mysql:

select * 
from (select * from mytable order by `Group`, age desc, Person) x
group by `Group`

This works because in mysql you're allowed to not aggregate non-group-by columns, in which case mysql just returns the first row. The solution is to first order the data such that for each group the row you want is first, then group by the columns you want the value for.

You avoid complicated subqueries that try to find the max() etc, and also the problems of returning multiple rows when there are more than one with the same maximum value (as the other answers would do)

Note: This is a mysql-only solution. All other databases I know will throw an SQL syntax error with the message "non aggregated columns are not listed in the group by clause" or similar. Because this solution uses undocumented behavior, the more cautious may want to include a test to assert that it remains working should a future version of MySQL change this behavior.

Version 5.7 update:

Since version 5.7, the sql-mode setting includes ONLY_FULL_GROUP_BY by default, so to make this work you must not have this option (edit the option file for the server to remove this setting).

Get maximum occurance of one specific value per row with pandas

The following code should do the job.

the function longest_streak will count the number of consecutive zeros and return the max, and you can use apply on your df.

from itertools import groupby
    def longest_streak(l):
      lst = []
      for n,c in groupby(l):
        num,count = n,sum(1 for i in c)
        if num==0:
          lst.append((num,count))

  maxx = max([y for x,y in lst])
  return(maxx)

df.apply(lambda x: longest_streak(x),axis=1)

Get the row corresponding to the max in pandas GroupBy

Check with sort_values +drop_duplicates

df.sort_values('B').drop_duplicates(['A'],keep='last')
Out[127]: 
   A  B  C
1  1  1  b
3  2  3  d

---

Related Topics