How to concatenate multiple column values into a single column in Panda dataframe

Question

This question is same to this posted earlier. I want to concatenate three columns instead of concatenating two columns:

Here is the combining two columns:

Answer 1

Possibly the fastest solution is to operate in plain Python:

Series(
    map(
        '_'.join,
        df.values.tolist()
        # when non-string columns are present:
        # df.values.astype(str).tolist()
    ),
    index=df.index
)

Comparison against @MaxU answer (using the big data frame which has both numeric and string columns):

%timeit big['bar'].astype(str) + '_' + big['foo'] + '_' + big['new']
# 29.4 ms ± 1.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)


%timeit Series(map('_'.join, big.values.astype(str).tolist()), index=big.index)
# 27.4 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Comparison against @derchambers answer (using their df data frame where all columns are strings):

from functools import reduce

def reduce_join(df, columns):
    slist = [df[x] for x in columns]
    return reduce(lambda x, y: x + '_' + y, slist[1:], slist[0])

def list_map(df, columns):
    return Series(
        map(
            '_'.join,
            df[columns].values.tolist()
        ),
        index=df.index
    )

%timeit df1 = reduce_join(df, list('1234'))
# 602 ms ± 39 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit df2 = list_map(df, list('1234'))
# 351 ms ± 12.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)