Q:

Python Pandas aggregating whitespace-separated values in text field

I have a dataframe like this:
0      A\nA\nA
1      na\nB|D|E|F|G|H\nB|D|E|F|G|H
2      B\nB|C\nB
3      na\nna\nna

And I would like to aggregate the values by highest count :
0      A
1      B|D|E|F|G|H
2      B
3      na

I assume I should delimit the column first by '\n' so I am using
df = pd.DataFrame([ x.split('\n') for x in df.tolist()])

So I get:
       0            1               2
0      A            A               A
1      na           B|D|E|F|G|H     B|D|E|F|G|H
2      B            B|C             B
3      na           na              na

How can I merge the columns next to get the desired output?
Thanks.

A:

You can use Counter with most_common:
from collections import Counter

df = pd.DataFrame([Counter(x.split('\n')).most_common(1)[0][0] for x in df.tolist()])
print (df)
             0
0            A
1  B|D|E|F|G|H
2            B
3           na

Another solution with str.split and apply value_counts:
df = df.str.split('\n', expand=True).apply(lambda x: pd.value_counts(x).index[0],axis=1)
print (df)
0              A
1    B|D|E|F|G|H
2              B
3             na
dtype: object

Timings:
In [238]: %timeit (pd.DataFrame([Counter(x.split('\n')).most_common(1)[0][0] for x in df.tolist()]))
1000 loops, best of 3: 197 µs per loop

In [239]: %timeit (df.str.split('\n', expand=True).apply(lambda x: pd.value_counts(x).index[0],axis=1))
100 loops, best of 3: 2.33 ms per loop

In [241]: %timeit (pd.DataFrame([ x.split('\n') for x in df.tolist()]).mode(1))
100 loops, best of 3: 2.32 ms per loop

Larger DataFrame:
#len (df) = 40k

from collections import Counter
df = pd.Series(['A\nA\nA','na\nB|D|E|F|G|H\nB|D|E|F|G|H','B\nB|c\nB','na\nna\nna'])
#print (df)
df = pd.concat([df]*10000).reset_index(drop=True)

In [331]: %timeit (pd.DataFrame([Counter(x.split('\n')).most_common(1)[0][0] for x in df.tolist()]))
1 loop, best of 3: 257 ms per loop

In [332]: %timeit (df.apply(lambda x: Counter(x.split('\n')).most_common()[0][:][0]))
1 loop, best of 3: 282 ms per loop

In [333]: %timeit (pd.DataFrame([ x.split('\n') for x in df.tolist()]).mode(1))
1 loop, best of 3: 9.18 s per loop

In [334]: %timeit (df.str.split('\n', expand=True).apply(lambda x: pd.value_counts(x).index[0],axis=1))
1 loop, best of 3: 15.7 s per loop

