I know that I can get the unique values of a
DataFrame by resetting the index but is there a way to avoid this step and get the unique values directly?
Given I have:
C A B 0 one 3 1 one 2 2 two 1
I can do:
df = df.reset_index() uniq_b = df.B.unique() df = df.set_index(['A','B'])
Is there a way built in pandas to do this?
One way is to use
In : df Out: C A B 0 one 3 1 one 2 2 two 1 In : df.index.levels Out: Index([one, two], dtype=object)
Andy Hayden's answer (
index.levels[blah]) is great for some scenarios, but can lead to odd behavior in others. My understanding is that Pandas goes to great lengths to "reuse" indices when possible to avoid having the indices of lots of similarly-indexed DataFrames taking up space in memory. As a result, I've found the following annoying behavior:
import pandas as pd import numpy as np np.random.seed(0) idx = pd.MultiIndex.from_product([['John', 'Josh', 'Alex'], list('abcde')], names=['Person', 'Letter']) large = pd.DataFrame(data=np.random.randn(15, 2), index=idx, columns=['one', 'two']) small = large.loc[['Jo'==d[0:2] for d in large.index.get_level_values('Person')]] print small.index.levels print large.index.levels
Index([u'Alex', u'John', u'Josh'], dtype='object') Index([u'Alex', u'John', u'Josh'], dtype='object')
rather than the expected
Index([u'John', u'Josh'], dtype='object') Index([u'Alex', u'John', u'Josh'], dtype='object')
As one person pointed out on the other thread, one idiom that seems very natural and works properly would be:
I hope this helps someone else dodge the super-unexpected behavior that I ran into.