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# Getting all unique values of columns in Pandas

schedule Aug 12, 2023
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To get all unique values of certain columns, use Pandas' `unique(~)` method.

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[2,3,2],"B":[4,5,5]})df A B0 2 41 3 52 2 5 ```

# Solution

## Single column

To get the unique values of a single column:

``` pd.unique(df["A"])   # returns a Numpy Array array([2, 3]) ```

## Multiple columns

To get the unique values of multiple columns combined:

``` pd.unique(df[["A","B"]].to_numpy().ravel())   # returns a Numpy Array array([2, 4, 3, 5]) ```

## Explanation

The `unique(~)` method expects a 1D array (e.g. Numpy array and Series), which is a problem since `df[["A","B"]]` returns a DataFrame:

``` df[["A","B"]] A B0 2 41 3 52 2 5 ```

To get around this problem, we must first convert this `DataFrame` into a Numpy array using `to_numpy()`, and then use the `ravel()` method to flatten the array to 1D:

``` df[["A","B"]].to_numpy().ravel() array([2, 4, 3, 5, 2, 5]) ```
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