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# Getting all duplicate rows in Pandas

schedule Aug 11, 2023
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To get all duplicate rows as a DataFrame, use the DataFrame's `duplicated(~)` method.

# Getting all duplicate rows

As an example, consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,3],"B":[5,6,5]}, index=["a","b","c"])df A Ba 3 5b 4 6c 3 5 ```

Here, rows `a` and `c` are duplicates.

To get all duplicate rows of `df`:

``` df[df.duplicated(keep=False)] A Ba 3 5c 3 5 ```

## Explanation

Here, the `duplicated(~)` method returns a Series of booleans where the duplicate rows are marked as `True`:

``` df.duplicated(keep=False) a Trueb Falsec Truedtype: bool ```

The `keep=False` indicates that we want all the duplicate rows to be marked as `True`, as opposed to only the `"first"` or `"last"`.

We then apply this boolean mask using the `[]` notation to retrieve the rows marked as `True`, that is, all the duplicate rows:

``` df[df.duplicated(keep=False)] A Ba 3 5c 3 5 ```

# Getting all rows where value for a column is duplicate

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,3,4],"B":[5,6,7]}, index=["a","b","c"])df A Ba 3 5b 3 6c 4 7 ```

To get all rows where value for column `A` is duplicate:

``` df[df["A"].duplicated(keep=False)] A Ba 3 5b 3 6 ```

The logic is exactly the same for the case above - the only difference is that we call `duplicated(~)` on column `A`:

``` df["A"].duplicated(keep=False) a Trueb Truec FalseName: A, dtype: bool ```
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