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pythonpandasseriesflatten
Alex
Alex

Reputation: 1537

how to flatten array in pandas dataframe

Assuming I have a pandas dataframe such as

df_p = pd.DataFrame(
   {'name_array':
    [[20130101, 320903902, 239032902],
     [20130101, 3253453, 239032902],
     [65756, 4342452, 32425432523]],
    'name': ['a', 'a', 'c']} )

Image of dataframe

I want to extract the series which contains the flatten arrays in each row whilst preserving the order

The expected result is a pandas.core.series.Series

Image of expected output

This question is not a duplicate because my expected output is a pandas Series, and not a dataframe.

Upvotes: 6

Views: 19407

Answers (4)

Milad Ce
Milad Ce

Reputation: 91

you can flatten list of column's lists, and then create series of that, in this way:

pd.Series([element for row in df_p.name_array for element in row])

Upvotes: 1

alkasm
alkasm

Reputation: 23042

The solutions using melt are slower than OP's original method, which they shared in the answer here, especially after the speedup from my comment on that answer.

I created a larger dataframe to test on:

df = pd.DataFrame({'name_array': np.random.rand(1000, 3).tolist()})

And timing the two solutions using melt on this dataframe yield:

In [16]: %timeit pd.melt(df.name_array.apply(pd.Series).reset_index(), id_vars=['index'],value_name='name_array').drop('variable', axis=1).sort_values('index')
173 ms ± 5.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [17]: %timeit df['name_array'].apply(lambda x: pd.Series([i for i in x])).melt().drop('variable', axis=1)['value']
175 ms ± 4.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

The OP's method with the speedup I suggested in the comments:

In [18]: %timeit pd.Series(np.concatenate(df['name_array']))
18 ms ± 887 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

And finally, the fastest solution as provided here but modified to provide a series instead of dataframe output:

In [14]: from itertools import chain
In [15]: %timeit pd.Series(list(chain.from_iterable(df['name_array'])))
402 µs ± 4.68 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

This last method is faster than melt() by 3 orders of magnitude and faster than np.concatenate() by 2 orders of magnitude.

Upvotes: 6

panktijk
panktijk

Reputation: 1614

You can use pd.melt:

pd.melt(df_p.name_array.apply(pd.Series).reset_index(), 
        id_vars=['index'],
        value_name='name_array') \
        .drop('variable', axis=1) \
        .sort_values('index')

OUTPUT:

index   name_array
0       20130101
0       320903902
0       239032902
1       20130101
1       3253453
1       239032902
2       65756
2       4342452
2       32425432523

Upvotes: 1

Alex
Alex

Reputation: 1537

This is the solution I've figured out. Don't know if there are more efficient ways.

df_p = pd.DataFrame(
   {'name_array':
    [[20130101, 320903902, 239032902],
     [20130101, 3253453, 239032902],
     [65756, 4342452, 32425432523]],
    'name': ['a', 'a', 'c']} )

data = pd.DataFrame( {'column':np.concatenate(df_p['name_array'].values)} )['column']

output:

[0       20130101
 1      320903902
 2      239032902
 3       20130101
 4        3253453
 5      239032902
 6          65756
 7        4342452
 8    32425432523
 Name: column, dtype: int64]

Upvotes: 2

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