How to create a new row for each comma separated value in a column in pandas

Question

I have a dataframe like this:

text                   category 
sfsd sgvv              abc,xyz
zydf sefs sdfsd        yyy
dfsd dsrgd dggr        xyz
eter vxg          


        

Answer 1

Try using set_index + stack + str.split + unstack + reset_index for much older versions:

print(df.set_index('text')
      .stack()
      .str.split(', ', expand=True)
      .stack()
      .unstack(-2)
      .reset_index(-1, drop=True)
      .reset_index())

Answer 2

Use DataFrame.explode (pandas 0.25+) with Series.str.split:

df1 = (df.assign(category = df['category'].str.split(','))
         .explode('category')
         .reset_index(drop=True))

For oldier pandas versions first DataFrame.set_index for not separator column(s), then Series.str.split and reshape by DataFrame.stack, last DataFrame.reset_index - first for remove second level of MultiIndex and then for convert index to column:

df1 = (df.set_index('text')['category']
         .str.split(',', expand=True)
         .stack()
         .reset_index(level=1, drop=True)
         .reset_index(name='category'))
print (df1)
              text category
0        sfsd sgvv      abc
1        sfsd sgvv      xyz
2  zydf sefs sdfsd      yyy
3  dfsd dsrgd dggr      xyz
4     eter vxg wfe      abc
5       dfvf ertet      abc
6       dfvf ertet      xyz

Answer 3

Below will give the output you need. Assuming df is your dataset name.

new_df_skel = dict()
new_df_skel['text'] = list()
new_df_skel['category'] = list()

for index,item in df.iterrows():
  item = dict(item)
  unref_cat = item['category']
  if "," in unref_cat:
    for strip in unref_cat.split(','):
      new_df_skel['category'].append(strip)
      new_df_skel['text'].append(item['text'])
  else:
    new_df_skel['category'].append(strip)
    new_df_skel['text'].append(unref_cat)

new_dataset = pd.DataFrame(new_df_skel)

Have a great day!

Answer 4

Linking to this question, try the following code for your dataframe:

We can first split the column, expand it, stack it and then join it back to the original df like below:

df.drop('category', axis=1).join(
  df['category'].str.split(',', expand=True).stack().reset_index(level=1,drop=True).rename('category'))