Sometimes I end up with a series of tuples/lists when using Pandas. This is common when, for example, doing a group-by and passing a function that has multiple return values: What is the correct way to "unpack" this structure so that I get a DataFrame with two columns? A related question is how I can unpack either this structure or

How to unpack a Series of tuples in Pandas?

Sometimes I end up with a series of tuples/lists when using Pandas. This is common when, for example, doing a group-by and passing a function that has multiple return values:

import numpy as np
from scipy import stats
df = pd.DataFrame(dict(x=np.random.randn(100),
                       y=np.repeat(list("abcd"), 25)))
out = df.groupby("y").x.apply(stats.ttest_1samp, 0)
print out

y
a       (1.3066417476, 0.203717485506)
b    (0.0801133382517, 0.936811414675)
c      (1.55784329113, 0.132360504653)
d     (0.267999459642, 0.790989680709)
dtype: object

What is the correct way to “unpack” this structure so that I get a DataFrame with two columns?

A related question is how I can unpack either this structure or the resulting dataframe into two Series/array objects. This almost works:

t, p = zip(*out)

but it t is

 (array(1.3066417475999257),
 array(0.08011333825171714),
 array(1.557843291126335),
 array(0.267999459641651))

and one needs to take the extra step of squeezing it.

Answer

maybe this is most strightforward (most pythonic i guess):

out.apply(pd.Series)

if you would want to rename the columns to something more meaningful, than:

out.columns=['Kstats','Pvalue']

if you do not want the default name for the index:

out.index.name=None

Advertisement

Answer