# Checking for unique rows in classification
**URL:** https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870
**Category:** New to Julia
**Tags:** dataframes
**Created:** [August 23, 2021, 7:22pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870 "2021-08-23T19:22:04Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@cwallj](https://discourse.julialang.org/u/cwallj)
#### Post date: [August 23, 2021, 7:22pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870/1 "2021-08-23T19:22:04Z")
Hi all!
I’m very new to Julia as well as coding. I’m wanting to prep my data for a classification task. I’m trying to write a function that will look through an object of type DataFrame and find any duplicate rows that have different values for the classification column (since it would be silly to try and classify those rows if the same inputs result in a different classification). I started to write a for loop that would check, but then I thought that wouldn’t work because there might be a row at index 3 and another at 30 that are (with the exception of the classification column) identical that the for loop wouldn’t catch (at least I think it wouldn’t…). I’ll attach what I wrote so far, but I’m not really sure where to go with this one… Any advice would be much appreciated!
```julia
function class_check(df)
print("Enter name of classification column \n\n")
class_name = readline()
for i = 1:size(df, 2)
for j = 1:size(df, 1)
if df[j, :class_name] == df[j+1, :class_name]
print("Error")
end
end
end
end
```
---
### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [August 23, 2021, 8:10pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870/2 "2021-08-23T20:10:38Z")
One thing to note, `:class_name` and `class_name` are different things. The first is a `Symbol`, which is kind of like a `String`, and the second is the actual value you get from `readline()`.
---
### Author:  [@nilshg](https://discourse.julialang.org/u/nilshg)
#### Post date: [August 23, 2021, 10:21pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870/3 "2021-08-23T22:21:28Z")
I’m not sure I agree with your premise - it’s not uncommon in regression and classification tasks for the same covariates to produce different outcomes, as in general you wouldn’t expect to perfectly observe all covariates (and hence have no error in the outcome).
In any case, it sounds like you might be interested in a `groupby` operation on all your covariates, something like:
```julia
julia> df = DataFrame(y = [1, 2, 3, 4, 5], x1 = ["a", "b", "c", "c", "e"], x2 = ["f", "g", "d", "d", "e"])
5×3 DataFrame
Row │ y x1 x2
│ Int64 String String
─────┼───────────────────────
1 │ 1 a f
2 │ 2 b g
3 │ 3 c d
4 │ 4 c d
5 │ 5 e e
julia> combine(groupby(df, [:x1, :x2]), :y => Ref => :y)
4×3 DataFrame
Row │ x1 x2 y
│ String String SubArray…
─────┼───────────────────────────
1 │ a f [1]
2 │ b g [2]
3 │ c d [3, 4]
4 │ e e [5]
```
When doing this you have to decide what to do with the multiple different outcomes (you could replace `Ref` by e.g. `first` to keep the first observed `y`, or `mean` to get the average, or whatever other function is appropriate in your case).
---
### Author:  [@ayodyas](https://discourse.julialang.org/u/ayodyas)
#### Post date: [August 11, 2022, 1:40pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870/4 "2022-08-11T13:40:54Z")
is there any possibility to get the no of repeatations if also from and to are same number
df = DataFrame(y = [1, 2, 3, 4, 5], x1 = [“a”, “b”, “c”, “d”, “e”], x2 = [“f”, “g”, “d”, “c”, “e”])
for example c to d and d to c can be counted as repeatations?
---
### Author:  [@bernhard](https://discourse.julialang.org/u/bernhard)
#### Post date: [August 11, 2022, 2:35pm UTC](https://discourse.julialang.org/t/checking-for-unique-rows-in-classification/66870/5 "2022-08-11T14:35:23Z")
you can use `Set` if you ‘want to ignore the order’.
I am sure there are more elegant solutions, but maybe this is what you are looking for:
```julia
df = DataFrame(y = [1, 2, 3, 4, 5], x1 = ["a", "b", "c", "d", "e"], x2 = ["f", "g", "d", "c", "e"])
df.id = Set.(string.(df.x1,df.x2))
rs = combine(groupby(df, :id), :y => Ref => :y)
```