How to combine multiple rows of strings into one using pandas?

Question

I have a DataFrame with multiple rows. Is there any way in which they can be combined to form one string?

For example:

     words
0    I, will, hereby
1         


        

Answer 1

How about traditional python's join? And, it's faster.

In [209]: ', '.join(df.words)
Out[209]: 'I, will, hereby, am, gonna, going, far, to, do, this'

Timings in Dec, 2016 on pandas 0.18.1

In [214]: df.shape
Out[214]: (6, 1)

In [215]: %timeit df.words.str.cat(sep=', ')
10000 loops, best of 3: 72.2 µs per loop

In [216]: %timeit ', '.join(df.words)
100000 loops, best of 3: 14 µs per loop

In [217]: df = pd.concat([df]*10000, ignore_index=True)

In [218]: df.shape
Out[218]: (60000, 1)

In [219]: %timeit df.words.str.cat(sep=', ')
100 loops, best of 3: 5.2 ms per loop

In [220]: %timeit ', '.join(df.words)
100 loops, best of 3: 1.91 ms per loop

Answer 2

If you have a DataFrame rather than a Series and you want to concatenate values (I think text values only) from different rows based on another column as a 'group by' key, then you can use the .agg method from the class DataFrameGroupBy. Here is a link to the API manual.

Sample code tested with Pandas v0.18.1:

import pandas as pd

df = pd.DataFrame({
    'category': ['A'] * 3 + ['B'] * 2,
    'name': ['A1', 'A2', 'A3', 'B1', 'B2'],
    'num': range(1, 6)
})

df.groupby('category').agg({
    'name': lambda x: ', '.join(x),
    'num': lambda x: x.max()
})

Answer 3

You can use str.cat to join the strings in each row. For a Series or column s, write:

>>> s.str.cat(sep=', ')
'I, will, hereby, am, gonna, going, far, to, do, this'

Answer 4

For anyone want to know how to combine multiple rows of strings in dataframe,
I provide a method that can concatenate strings within a 'window-like' range of near rows as follows:

# add columns based on 'windows-like' rows
df['windows_key_list'] = pd.Series(df['key'].str.cat([df.groupby(['bycol']).shift(-i)['key'] for i in range(1, windows_size)], sep = ' ')

Note: This can't be reached by groupby, because we don't mean the same id of rows, just near rows.