How to explode a list inside a Dataframe cell into separate rows

Question

I\'m looking to turn a pandas cell containing a list into rows for each of those values.

So, take this:

If I\'d like to unpack and stack the value

Answer 1

Nicer alternative solution with apply(pd.Series):

df = pd.DataFrame({'listcol':[[1,2,3],[4,5,6]]})

# expand df.listcol into its own dataframe
tags = df['listcol'].apply(pd.Series)

# rename each variable is listcol
tags = tags.rename(columns = lambda x : 'listcol_' + str(x))

# join the tags dataframe back to the original dataframe
df = pd.concat([df[:], tags[:]], axis=1)

Answer 2

Instead of using apply(pd.Series) you can flatten the column. This improves performance.

df = (pd.DataFrame({'name': ['A.J. Price'] * 3, 
                'opponent': ['76ers', 'blazers', 'bobcats'], 
                'nearest_neighbors': [['Zach LaVine', 'Jeremy Lin', 'Nate Robinson', 'Isaia']] * 3})
  .set_index(['name', 'opponent']))



%timeit (pd.DataFrame(df['nearest_neighbors'].values.tolist(), index = df.index)
           .stack()
           .reset_index(level = 2, drop=True).to_frame('nearest_neighbors'))

1.87 ms ± 9.74 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


%timeit (df.nearest_neighbors.apply(pd.Series)
          .stack()
          .reset_index(level=2, drop=True)
          .to_frame('nearest_neighbors'))

2.73 ms ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Answer 3

Use apply(pd.Series) and stack, then reset_index and to_frame

In [1803]: (df.nearest_neighbors.apply(pd.Series)
              .stack()
              .reset_index(level=2, drop=True)
              .to_frame('nearest_neighbors'))
Out[1803]:
                    nearest_neighbors
name       opponent
A.J. Price 76ers          Zach LaVine
           76ers           Jeremy Lin
           76ers        Nate Robinson
           76ers                Isaia
           blazers        Zach LaVine
           blazers         Jeremy Lin
           blazers      Nate Robinson
           blazers              Isaia
           bobcats        Zach LaVine
           bobcats         Jeremy Lin
           bobcats      Nate Robinson
           bobcats              Isaia

Details

In [1804]: df
Out[1804]:
                                                   nearest_neighbors
name       opponent
A.J. Price 76ers     [Zach LaVine, Jeremy Lin, Nate Robinson, Isaia]
           blazers   [Zach LaVine, Jeremy Lin, Nate Robinson, Isaia]
           bobcats   [Zach LaVine, Jeremy Lin, Nate Robinson, Isaia]

Answer 4

So all of these answers are good but I wanted something ^really simple^ so here's my contribution:

def explode(series):
    return pd.Series([x for _list in series for x in _list])                               

That's it.. just use this when you want a new series where the lists are 'exploded'. Here's an example where we do value_counts() on taco choices :)

In [1]: my_df = pd.DataFrame(pd.Series([['a','b','c'],['b','c'],['c']]), columns=['tacos'])      
In [2]: my_df.head()                                                                               
Out[2]: 
   tacos
0  [a, b, c]
1     [b, c]
2        [c]

In [3]: explode(my_df['tacos']).value_counts()                                                     
Out[3]: 
c    3
b    2
a    1

Answer 5

I think this a really good question, in Hive you would use EXPLODE, I think there is a case to be made that Pandas should include this functionality by default. I would probably explode the list column with a nested generator comprehension like this:

pd.DataFrame({
    "name": i[0],
    "opponent": i[1],
    "nearest_neighbor": neighbour
    }
    for i, row in df.iterrows() for neighbour in row.nearest_neighbors
    ).set_index(["name", "opponent"])