Splitting a list in a Pandas cell into multiple columns [duplicate]

Answer 1

You can loop through the Series with apply() function and convert each list to a Series, this automatically expand the list as a series in the column direction:

df[0].apply(pd.Series)

#   0    1   2
#0  8   10  12
#1  7    9  11

Update: To keep other columns of the data frame, you can concatenate the result with the columns you want to keep:

pd.concat([df[0].apply(pd.Series), df[1]], axis = 1)

#   0    1   2  1
#0  8   10  12  A
#1  7    9  11  B

Answer 2

You could do pd.DataFrame(df[col].values.tolist()) - is much faster ~500x

In [820]: pd.DataFrame(df[0].values.tolist())
Out[820]:
   0   1   2
0  8  10  12
1  7   9  11

In [821]: pd.concat([pd.DataFrame(df[0].values.tolist()), df[1]], axis=1)
Out[821]:
   0   1   2  1
0  8  10  12  A
1  7   9  11  B

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Timings

Medium

In [828]: df.shape
Out[828]: (20000, 2)

In [829]: %timeit pd.DataFrame(df[0].values.tolist())
100 loops, best of 3: 15 ms per loop

In [830]: %timeit df[0].apply(pd.Series)
1 loop, best of 3: 4.06 s per loop

Large

In [832]: df.shape
Out[832]: (200000, 2)

In [833]: %timeit pd.DataFrame(df[0].values.tolist())
10 loops, best of 3: 161 ms per loop

In [834]: %timeit df[0].apply(pd.Series)
1 loop, best of 3: 40.9 s per loop