Can datasets remove duplicated rows?
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.
**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
Can datasets remove duplicated rows? 
 **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 
 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.