Pandas split column into multiple columns by comma

Question

I am trying to split a column into multiple columns based on comma/space separation.

My dataframe currently looks like

     KEYS                                                  1
0   FIT-4270                                          4000.0439
1   FIT-4269                                          4000.0420, 4000.0471
2   FIT-4268                                          4000.0419
3   FIT-4266                                          4000.0499
4   FIT-4265                                          4000.0490, 4000.0499, 4000.0500, 4000.0504,

I would like

   KEYS                                                  1           2            3        4 
0   FIT-4270                                          4000.0439
1   FIT-4269                                          4000.0420  4000.0471
2   FIT-4268                                          4000.0419
3   FIT-4266                                          4000.0499
4   FIT-4265                                          4000.0490  4000.0499  4000.0500  4000.0504 

My code currently removes The KEYS column and I'm not sure why. Could anyone improve or help fix the issue?

v = dfcleancsv[1]

#splits the columns by spaces into new columns but removes KEYS?

dfcleancsv = dfcleancsv[1].str.split(' ').apply(Series, 1)

Answer 1

In case someone else wants to split a single column (deliminated by a value) into multiple columns - try this:

series.str.split(',', expand=True)

This answered the question I came here looking for.

Credit to EdChum's code that includes adding the split columns back to the dataframe.

pd.concat([df[[0]], df[1].str.split(', ', expand=True)], axis=1)

Note: The first argument df[[0]] is DataFrame.

The second argument df[1].str.split is the series that you want to split.

split Documentation

concat Documentation

Answer 2

Using Edchums answer of

pd.concat([df[[0]], df[1].str.split(', ', expand=True)], axis=1)

I was able to solve it by substituting my variables.

dfcleancsv = pd.concat([dfcleancsv['KEYS'], dfcleancsv[1].str.split(', ', expand=True)], axis=1)

Answer 3

The OP had a variable number of output columns. In the particular case of a fixed number of output columns another elegant solution to name the resulting columns is to use a multiple assignation.

Load a sample dataset and reshape it to long format to obtain a variable called organ_dimension.

import seaborn
iris = seaborn.load_dataset('iris')
df = iris.melt(id_vars='species', var_name='organ_dimension', value_name='value')

Split the organ_dimension variable in 2 variables organ and dimension based on the _ separator.

df[['organ', 'dimension']] = df['organ_dimension'].str.split('_', expand=True)
df.head()

Out[10]: 
  species organ_dimension  value  organ dimension
0  setosa    sepal_length    5.1  sepal    length
1  setosa    sepal_length    4.9  sepal    length
2  setosa    sepal_length    4.7  sepal    length
3  setosa    sepal_length    4.6  sepal    length
4  setosa    sepal_length    5.0  sepal    length

Based on this answer "How to split a column into two columns?"

Answer 4

The simplest way to use is, vectorization

df = df.apply(lambda x:pd.Series(x))

Answer 5

maybe this should work:

df = pd.concat([df['KEYS'],df[1].apply(pd.Series)],axis=1)

Answer 6

Check this out

Responder_id    LanguagesWorkedWith
0   1   HTML/CSS;Java;JavaScript;Python
1   2   C++;HTML/CSS;Python
2   3   HTML/CSS
3   4   C;C++;C#;Python;SQL
4   5   C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA
... ... ...
87564   88182   HTML/CSS;Java;JavaScript
87565   88212   HTML/CSS;JavaScript;Python
87566   88282   Bash/Shell/PowerShell;Go;HTML/CSS;JavaScript;W...
87567   88377   HTML/CSS;JavaScript;Other(s):
87568   88863   Bash/Shell/PowerShell;HTML/CSS;Java;JavaScript...`
###Split the LanguagesWorkedWith column into  multiple columns  by using` data= data1['LanguagesWorkedWith'].str.split(';').apply(pd.Series)`.###
` data1 = pd.read_csv('data.csv', sep=',')
data1.set_index('Responder_id',inplace=True)
data1
data1.loc[1,:]
data= data1['LanguagesWorkedWith'].str.split(';').apply(pd.Series)
data.head()`

Answer 7

You may also want to try datar, a package ports dplyr, tidyr and related R packages to python:

>>> df
         i       j              A
  <object> <int64>       <object>
0       AR       5    Paris,Green
1      For       3  Moscow,Yellow
2      For       4  NewYork,Black
>>> from datar import f
>>> from datar.tidyr import separate
>>> separate(df, f.A, ['City', 'Color'])
         i       j     City    Color
  <object> <int64> <object> <object>
0       AR       5    Paris    Green
1      For       3   Moscow   Yellow
2      For       4  NewYork    Black