How to Get the Maximum Value by Group

SQL to get max value from each group

select * from [table] t1
inner join
(
select track_id, user_id, max(rating) maxRating
from [table]
group by track_id, user_id
) tmp
on t1.track_id = tmp.track_id
and t1.user_id = tmp.user_id
and t1.rating = tmp.maxRating;

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 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

How to get the maximum value by group

If you know sql this is easier to understand

library(sqldf)
sqldf('select year, max(score) from mydata group by year')

Update (2016-01): Now you can also use dplyr

library(dplyr)
mydata %>% group_by(year) %>% summarise(max = max(score))

Select the row with the maximum value in each group

Here's a data.table solution:

require(data.table) ## 1.9.2
group <- as.data.table(group)

If you want to keep all the entries corresponding to max values of pt within each group:

group[group[, .I[pt == max(pt)], by=Subject]$V1]
#    Subject pt Event
# 1:       1  5     2
# 2:       2 17     2
# 3:       3  5     2

If you'd like just the first max value of pt:

group[group[, .I[which.max(pt)], by=Subject]$V1]
#    Subject pt Event
# 1:       1  5     2
# 2:       2 17     2
# 3:       3  5     2

In this case, it doesn't make a difference, as there aren't multiple maximum values within any group in your data.

How to find the maximum value within each group and then recode all other values in the group as zero?

You can try this

df %>%
  group_by(Id) %>%
  mutate(maxByGroup = (which.max(value) == seq_along(value)) * value) %>%
  ungroup()

which gives

      Id value maxByGroup
   <dbl> <dbl>      <dbl>
 1     1   500        500
 2     1   500          0
 3     1   500          0
 4     2   250        250
 5     2   250          0
 6     2   250          0
 7     3   300        300
 8     3   300          0
 9     3   300          0
10     4   400        400
11     4   400          0
12     4   400          0

Extract the maximum value within each group in a dataframe

There are many possibilities to do this in R. Here are some of them:

df <- read.table(header = TRUE, text = 'Gene   Value
A      12
A      10
B      3
B      5
B      6
C      1
D      3
D      4')

# aggregate
aggregate(df$Value, by = list(df$Gene), max)
aggregate(Value ~ Gene, data = df, max)

# tapply
tapply(df$Value, df$Gene, max)

# split + lapply
lapply(split(df, df$Gene), function(y) max(y$Value))

# plyr
require(plyr)
ddply(df, .(Gene), summarise, Value = max(Value))

# dplyr
require(dplyr)
df %>% group_by(Gene) %>% summarise(Value = max(Value))

# data.table
require(data.table)
dt <- data.table(df)
dt[ , max(Value), by = Gene]

# doBy
require(doBy)
summaryBy(Value~Gene, data = df, FUN = max)

# sqldf
require(sqldf)
sqldf("select Gene, max(Value) as Value from df group by Gene", drv = 'SQLite')

# ave
df[as.logical(ave(df$Value, df$Gene, FUN = function(x) x == max(x))),]

How to find maximum value per group for all rows in SQL?

You need MAX() window function:

SELECT *, 
       MAX(value) OVER (PARTITION BY name) "max"
FROM out_pumptable;

See the demo.

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