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# Counting unique values in rows of a Pandas DataFrame

schedule Aug 12, 2023
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To count the number of unique values in a row of a DataFrame, use the `nunique(~)` method.

Consider the following DataFrame:

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

# Unique values in rows

To get the number of unique values in each row of `df`:

``` df.nunique(axis=1) a 1b 2c 2dtype: int64 ```

Here, note the following:

• `axis=1` indicates that we want to count row-wise as opposed to column-wise

• `1` is returned for row `a`. Think of `nunique(~)` as first applying a set operation that eliminates subsequent duplicate values (e.g. `set([3,3])=[3]`), and then fetching the size of the resulting set.

# Unique values in specific row

To get the number of unique values in a specific row:

``` df.loc["b"].nunique() 2 ```

Here, we are using the DataFrame's `loc` property to fetch row `b` as a `Series`. We then use the Series' `nunique(~)` to get the number of unique values in this row.

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