"unstack" a pandas column containing lists into multiple rows [duplicate]

Question

Say I have the following Pandas Dataframe:

df = pd.DataFrame({"a" : [1,2,3], "b" : [[1,2],[2,3,4],[5]]})    a          b 0  1     [1, 2] 1  2  [2, 3, 4] 2  3        [5] 

How would I "unstack" the lists in the "b" column in order to transform it into the dataframe:

   a  b 0  1  1 1  1  2 2  2  2 3  2  3 4  2  4 5  3  5 

Answer 1

UPDATE: generic vectorized approach - will work also for multiple columns DFs:

assuming we have the following DF:

In [159]: df Out[159]:    a          b  c 0  1     [1, 2]  5 1  2  [2, 3, 4]  6 2  3        [5]  7 

Solution:

In [160]: lst_col = 'b'  In [161]: pd.DataFrame({      ...:     col:np.repeat(df[col].values, df[lst_col].str.len())      ...:     for col in df.columns.difference([lst_col])      ...: }).assign(**{lst_col:np.concatenate(df[lst_col].values)})[df.columns.tolist()]      ...: Out[161]:    a  b  c 0  1  1  5 1  1  2  5 2  2  2  6 3  2  3  6 4  2  4  6 5  3  5  7 

Setup:

df = pd.DataFrame({     "a" : [1,2,3],     "b" : [[1,2],[2,3,4],[5]],     "c" : [5,6,7] }) 

Vectorized NumPy approach:

In [124]: pd.DataFrame({'a':np.repeat(df.a.values, df.b.str.len()),                         'b':np.concatenate(df.b.values)}) Out[124]:    a  b 0  1  1 1  1  2 2  2  2 3  2  3 4  2  4 5  3  5 

OLD answer:

Try this:

In [89]: df.set_index('a', append=True).b.apply(pd.Series).stack().reset_index(level=[0, 2], drop=True).reset_index() Out[89]:    a    0 0  1  1.0 1  1  2.0 2  2  2.0 3  2  3.0 4  2  4.0 5  3  5.0 

Or bit nicer solution provided by @Boud:

In [110]: df.set_index('a').b.apply(pd.Series).stack().reset_index(level=-1, drop=True).astype(int).reset_index() Out[110]:    a  0 0  1  1 1  1  2 2  2  2 3  2  3 4  2  4 5  3  5