numpy.unique with order preserved

['b','b','b','a','a','c','c']

numpy.unique gives

['a','b','c']

How can I get the original order preserved

['b','a','c']

Great answers. Bonus question. Why do none of these methods work with this dataset? http://www.uploadmb.com/dw.php?id=1364341573 Here's the question numpy sort wierd behavior

Numpy np.unique() is slow, O(Nlog(N)), but you can do this by following code:

import numpy as np
a = np.array(['b','b','b','a','a','c','c'])
_, idx = np.unique(a, return_index=True)
print(a[np.sort(idx)])

Output:

['b' 'a' 'c']

Pandas pd.unique() is much faster for big array O(N):

import pandas as pd

a = np.random.randint(0, 1000, 10000)
%timeit np.unique(a)
%timeit pd.unique(a)

1000 loops, best of 3: 644 us per loop
10000 loops, best of 3: 144 us per loop

Note: Pandas pd.unique() has the further benefit of preserving order by default:

pandas.unique(values)

Return unique values based on a hash table.

Uniques are returned in order of appearance. This does NOT sort.

Significantly faster than numpy.unique for long enough sequences. Includes NA values.