Pandas split the data column for each character

Question

I have several data columns that look like this:

                         Day1
0    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
1    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
2    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
3    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
4    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD

I want each character to be in a separate column:

     012345678910111213....
0    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
1    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
2    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
3    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD
4    DDDDDDDDDDBBBBBBAAAAAAAAAABBBBBBDDDDDDDDDDDDDDDD

So, “Day 1-Column” is divided into 48 columns, and each column has one of the values ​​A / B / C / D

I tried with split, but that didn't work.

Answer 1

You can call applyand for each call of a line pd.Seriesin listvalues:

In [16]:

df['Day1'].apply(lambda x: pd.Series(list(x)))
Out[16]:
  0  1  2  3  4  5  6  7  8  9  ... 38 39 40 41 42 43 44 45 46 47
0  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
1  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
2  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
3  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
4  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D

[5 rows x 48 columns]

It looks like you have trailing spaces, remove them with str.rstrip:

df['Day1'] = df['Day1'].str.rstip()

then do it higher

Answer 2

use the Series.str.extractall () Method :

In [19]: df.Day1.str.extractall('(.)', flags=re.U)[0].unstack().rename_axis(None, 1)
Out[19]:
  0  1  2  3  4  5  6  7  8  9  ... 38 39 40 41 42 43 44 45 46 47
0  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
1  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
2  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
3  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D
4  D  D  D  D  D  D  D  D  D  D ...  D  D  D  D  D  D  D  D  D  D

[5 rows x 48 columns]