create a tuple from columns in a pandas DataFrame


I would like to automatically create a tuple (to be passed to a scipy.stats function) from columns in a pandas dataframe, so that each row of the tuple are the values from each column of the dataframe. here is the header from my dataframe:

4_3-a-0 5_3-a-4 7_3-a-3 datetime_pac 2015-09-03 22:00:00 -100.4 -96.857143 -55.000000 2015-09-03 22:01:00 -100.5 -91.700000 -55.600000 2015-09-03 22:02:00 -100.4 -90.875000 -55.900000 2015-09-03 22:03:00 -100.4 -94.000000 -55.555556 2015-09-03 22:04:00 -100.5 -99.500000 -55.545455

I can achieve this manually like so:

from scipy import stats stats.f_oneway(df.ix[:,0], df.ix[:,1], df.ix[:,2])

But I would like to 'automate' it in cases where the number of columns in the dataframe is unknown. The following attempts (and many variations of) would not work:

stats.f_oneway(tuple(x) for x in xtmp.values) stats.f_oneway((xtmp[x]) for x in xtmp.columns)

Thanks for your help!


What about

tuple([tuple(df[col]) for col in df])

Just call apply and call tuple:

In [3]: df = pd.DataFrame(np.random.randn(5,3)) df Out[3]: 0 1 2 0 0.785562 -0.263813 2.239865 1 1.083918 0.035746 0.429111 2 1.422599 -0.818151 0.765725 3 1.022289 0.098561 -2.393095 4 -0.548451 -0.345796 0.298237 In [4]: df.apply(tuple, axis=1) Out[4]: 0 (0.785562108573, -0.263813112223, 2.23986497964) 1 (1.08391788685, 0.0357457180803, 0.429110675053) 2 (1.4225989372, -0.818150896781, 0.765724984713) 3 (1.02228880387, 0.0985610274998, -2.39309469576) 4 (-0.548450748411, -0.345796089243, 0.298237353... dtype: object



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