Row with Minimum Value of a Column

Select a pandas dataframe row where column has minimum value

A column name of df.S works but a column name of df.T doesn't work. df.T invokes the Transpose operation because it takes namespace precedence. Here's @ansev answer using literals instead.

df[df['S']==df['S'].min()]

row with minimum value of a column

Try this -

 select top 1 * from table where N_UM = (select min(N_UM) from table);

Pandas GroupBy and select rows with the minimum value in a specific column

I feel like you're overthinking this. Just use groupby and idxmin:

df.loc[df.groupby('A').B.idxmin()]

   A  B   C
2  1  2  10
4  2  4   4

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df.loc[df.groupby('A').B.idxmin()].reset_index(drop=True)

   A  B   C
0  1  2  10
1  2  4   4

Group Rows Based On Column Value And Keep Row With Minimum Value In R

You may check with order and duplicated all from base R

data = data[order(data$Mean),]
output = data[!duplicated(data[c("U","D")]),]
output
      A    B   C       U   D E        F    G     H  I       Mean        Min        Max
12   NA 2.00 Yes PQR-001 PQR B 11200077 -0.1   1.2  V  0.6914235  0.6907286  0.6919283
2  0.18 0.33 Yes ABC-001 ABC B 22000031  0.0 100.0 us 37.5211111 33.2500000 42.1200000

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If you want dplyr

library(dplyr)
data %>% group_by(U, D) %>% slice(which.min(Mean))

how to subset rows in specific columns based on minimum values in individual columns in a dataframe using R

We can use map to loop over the columns 'X0', 'V2', grouped by 'ID', slice the rows where the value is min for that looped column, bind them together (_dfr) and create the 'd' column with pmin of those columns

library(dplyr)
library(purrr)
nm1 <- names(df1)[5:6]
map_dfr(nm1, ~ df1 %>%
         group_by(ID) %>%
         slice_min(!! rlang::sym(.x))) %>% 
     ungroup %>%
     mutate(d = select(., all_of(nm1)) %>% reduce(pmin))

-output

# A tibble: 4 x 7
#      X     Y ID       TI    X0    V2     d
#  <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#1  8.47  48.0 B_1    191. 0.134 0.348 0.134
#2  7.59  48.7 B_1_2 1350. 0.361 0.771 0.361
#3  8.15  48.4 B_1    191. 0.846 0.141 0.141
#4  8.06  48.2 B_1_2 1350. 0.983 0.224 0.224
 

Ignore NA values when using MIN function of apply

You can add na.rm=T to your function and set to NAwhen the entire vector of values is NA (otherwise it returns it returns -Inf).

my_min <- function(x) ifelse( !all(is.na(x)), min(x, na.rm=T), NA)
foo$MIN = apply(foo[c("A", "B")], 1, my_min)

output:

   A  B  C MIN
1  1  3 NA   3
2  2 NA  1   2
3  3  2  2   3
4 NA NA  3  NA

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