Expand pandas DataFrame column into multiple rows

Question

If I have a DataFrame such that:

pd.DataFrame( {\"name\" : \"John\", 
               \"days\" : [[1, 3, 5, 7]]
              })

<

Answer 1

Here's something with NumPy -

lens = [len(item) for item in df['days']]
df_out = pd.DataFrame( {"name" : np.repeat(df['name'].values,lens), 
               "days" : np.hstack(df['days'])
              })

As pointed in @unutbu's solution np.concatenate(df['days'].values) would be faster than np.hstack(df['days']).

It uses a loop-comprehension to extract the lengths of each 'days' element, which must be minimal runtime-wise.

Sample run -

>>> df
           days  name
0  [1, 3, 5, 7]  John
1        [2, 4]  Eric
>>> lens = [len(item) for item in df['days']]
>>> pd.DataFrame( {"name" : np.repeat(df['name'].values,lens), 
...                "days" : np.hstack(df['days'])
...               })
   days  name
0     1  John
1     3  John
2     5  John
3     7  John
4     2  Eric
5     4  Eric