Often we see duplicated rows of data in the DataFrames we are working with. This could happen due to errors in data collection or in saving and loading the data. To check for duplicates, we can use the pandas function `.duplicated()`, which will return a Series telling us which rows are duplicate rows. Let's say we have a DataFrame `fruits` that represents this table:

|*item*|*price*|*calories*| |-|-|-| |"banana"|"$1"|105| |"apple"|"$0.75"|95| |"apple"|"$0.75"|95| |"peach"|"$3"|55| |"peach"|"$4"|55| |"clementine"|"$2.5"|35|

If we call `fruits.duplicated()`, we would get the following table:

|id|value| |-|-| |0|False| |1|False| |2|True| |3|False| |4|False| |5|False|

We can see that row 2, which represents an `"apple"` with price `"$0.75"` and `95` calories, is a duplicate row. Every value in this row is the same as in another row. We can use the pandas `.drop_duplicates()` function to remove all rows that are duplicates of another row. If we call `fruits.drop_duplicates()`, we would get the table:

|*item*|*price*|*calories*| |-|-|-| |"banana"|"$1"|105| |"apple"|"$0.75"|95| |"peach"|"$3"|55| |"peach"|"$4"|55| |"clementine"|"$2.5"|35|

The `"apple"` row was deleted because it was exactly the same as another row. But the two `"peach"` rows remain because there is a difference in the *price* column. If we wanted to remove every row with a duplicate value in the *item* column, we could specify a `subset`: ```py fruits = fruits.drop_duplicates(subset=['item']) ``` By default, this keeps the first occurrence of the duplicate:

|*item*|*price*|*calories*| |-|-|-| |"banana"|"$1"|105| |"apple"|"$0.75"|95| |"peach"|"$3"|55| |"clementine"|"$2.5"|35|

Make sure that the columns you drop duplicates from are specifically the ones where duplicates don't belong. You wouldn't want to drop duplicates with the *`price`* column as a subset, for example, because it's okay if multiple items cost the same amount!