Find unique values in a Pandas dataframe, irrespective of row or column location

Question

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?

Answer 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