## Question or problem about Python programming:

I have a Pandas dataframe and I want to find all the unique values in that dataframe…irrespective of row/columns. If I have a 10 x 10 dataframe, and suppose they have 84 unique values, I need to find them – Not the count.

I can create a set and add the values of each rows by iterating over the rows of the dataframe. But, I feel that it may be inefficient (cannot justify that). Is there an efficient way to find it? Is there a predefined function?

## How to solve the problem:

### Solution 1:

In [1]: df = DataFrame(np.random.randint(0,10,size=100).reshape(10,10)) In [2]: df Out[2]: 0 1 2 3 4 5 6 7 8 9 0 2 2 3 2 6 1 9 9 3 3 1 1 2 5 8 5 2 5 0 6 3 2 0 7 0 7 5 5 9 1 0 3 3 5 3 2 3 7 6 8 3 8 4 4 8 0 2 2 3 9 7 1 2 7 5 3 2 8 5 6 4 3 7 0 8 6 4 2 6 5 3 3 4 5 3 2 7 7 6 0 6 6 7 1 7 5 1 8 7 4 3 1 0 6 9 7 7 3 9 5 3 4 5 2 0 8 6 4 7 In [13]: Series(df.values.ravel()).unique() Out[13]: array([9, 1, 4, 6, 0, 7, 5, 8, 3, 2])

Numpy unique sorts, so its faster to do it this way (and then sort if you need to)

In [14]: df = DataFrame(np.random.randint(0,10,size=10000).reshape(100,100)) In [15]: %timeit Series(df.values.ravel()).unique() 10000 loops, best of 3: 137 ﾵs per loop In [16]: %timeit np.unique(df.values.ravel()) 1000 loops, best of 3: 270 ﾵs per loop

### Solution 2:

Or you can use:

`df.stack().unique()`

Then you don’t need to worry if you have `NaN`

values, as they are excluded when doing the stacking.