Can datasets remove duplicated rows?
Hi ! For now this is probably the best option.
We might add a feature like this in the feature as well.

Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?
Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases Yes, I'd like to work on this feature once I'm done with #2500, but first I have to do some research, and see if the implementation wouldn't be too complex.

In the meantime, maybe [this lib](https://github.com/TomScheffers/pyarrow_ops) can help. However, note that this lib operates directly on pyarrow tables and relies only on `hash` to find duplicates (e.g. `-1` and `-2` have the same hash in Python 3, so this lib will treat them as duplicates), which doesn't make much sense. > Hi ! For now this is probably the best option.
> We might add a feature like this in the feature as well.
> 
> Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?
> Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases

Great if this is can be done. Thanks!!

Not sure if you are asking me. In any case I don't know of any unfortunately :( in practice if data is really large we normally do it with spark (only for info. I understand this is not useful in developing this library..) Hello,

I'm also interested in this feature.
Has there been progress on this issue?

Could we use a similar trick as above, but with a better hashing algorithm like SHA?

We could also use a [bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), should we care a lot about collision in this case? For reference, we can get a solution fairly easily if we assume that we can hold in memory all unique values. 

```python
from datasets import Dataset
from itertools import cycle
from functools import partial

memory = set()
def is_unique(elem:Any , column: str, memory: set) -> bool:
    if elem[column] in memory:
        return False
    else:
        memory.add(elem[column])
        return True

# Example dataset
ds = Dataset.from_dict({"col1" : [sent for i, sent in zip(range(10), cycle(["apple", "orange", "pear"]))],
                                      "col2": [i % 5 for i in range(10)]})

# Drop duplicates in `ds` on "col1"
ds2 = ds.filter(partial(is_unique, column="col1", memory=memory))
```

Of course, we can improve the API so that we can introduce `Dataset.drop_duplicates`.
For the parallel version, we can use a shared memory set. An approach that works assuming you can hold the all the unique document hashes in memory:
```python
from datasets import load_dataset

def get_hash(example):
    """Get hash of content field."""
    return {"hash": hash(example["content"])} # can use any hashing function here
    
def check_uniques(example, uniques):
    """Check if current hash is still in set of unique hashes and remove if true."""
    if example["hash"] in uniques:
        uniques.remove(example["hash"])
        return True
    else:
        return False

ds = load_dataset("some_dataset")
ds = ds.map(get_hash)
uniques = set(ds.unique("hash"))
ds_filter = ds.filter(check_uniques, fn_kwargs={"uniques": uniques})
```
If the `uniques` could be stored in arrow then no additional memory would used at all but I don't know if this is possible.
 @lvwerra hey, could you tell me how reliable is this deduplication method. i am currently using the same deduplication strategy to deduplicate a large text corpus to pretrain LLMs ~ 11B to 20B. just needed to ensure if this strategy would be fine on large datasets for LLMs pretraining. 
**Is your feature request related to a problem? Please describe.**
i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..


**Describe the solution you'd like**
have a functionality of " remove duplicated rows"

**Describe alternatives you've considered**
convert dataset to pandas, remove duplicate, and convert back...


**Additional context**
no