PYTHON

pandas split column into multiple columns by delimiter

df[['A', 'B']] = df['AB'].str.split(' ', 1, expand=True)

r separate column into multiple columns

df %>%
  separate(mainColumn,c('c1','c2'),'_',extra ='merge')

pandas split column into multiple columns

df.Name.str.split(expand=True,)
          0  1
0   Steve   Smith
1   Joe Nadal
2   Roger   Federer

split coumn of df into multiple dynamic columns

d = [pd.DataFrame(df[col].tolist()).add_prefix(col) for col in df.columns]
df = pd.concat(d, axis=1)

   id0  id1   id2  value0  value1  value2
0   10   10   NaN   apple  orange    None
1   15   67   NaN  banana  orange    None
2   12   34  45.0   apple  banana  orange

split a column into multiple columns

df[['A', 'B']] = df['AB'].str.split(' ', 1, expand=True)
df['A'], df['B'] = df['AB'].str.split('-', 1).str

pandas split cell into multiple columns

df[['A', 'B']] = df['AB'].str.split(' ', 1, expand=True)

split rows into multiple columns in pandas

df = pd.DataFrame(df.Raw_info.values.reshape(-1, 3), 
                   columns=['Function_name', 'prop1', 'prop2'])

print(df) 
  Function_name            prop1            prop2
0    Function_1  internal_prop_1  external_prop_1
1    Function_2  internal_prop_2  external_prop_2
2    Function_3  internal_prop_3  external_prop_3

Split Column with delimiter into multiple columns

DECLARE @tt TABLE(i INT IDENTITY,x VARCHAR(8000));
INSERT INTO @tt(x)VALUES('-9;-9;-1;-9;-9;-9;-9;-9;-1;-9;-9;-9;-9;-9;-9;-9;-9;-9;-1;-9;-9;-9;-9;-9;-9;-9;-9;-9;-1;-9;-1;-9;-9;-9;-1;-9;-9;-9;-9;-9;-9;-1;-1;-1;-1;-9;-1;-1;-9;-9;-9;-9;-1;-9;-1;-9;-9;-9;-1;-9;-1;-9;-1;-9;-9;-9;-9;-1;-9;-9;-1;-1;-9;-1;-1;0000;FFF8;-9;-9;-9;-1;-9;-1;-9;FFF6;-9;-1;-9;-1;-9;-1;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9;-9');

SELECT
    i,
    val1=n.v.value('/e[1]','VARCHAR(16)'),
    val2=n.v.value('/e[2]','VARCHAR(16)'),
    val3=n.v.value('/e[3]','VARCHAR(16)'),
    -- ... repeat for val4 .. val114
    val115=n.v.value('/e[115]','VARCHAR(16)')
FROM
    @tt
    CROSS APPLY (
        SELECT 
            CAST('<e>'+REPLACE(x,';','</e><e>')+'</e>' AS XML) AS itm
    ) AS i
    CROSS APPLY i.itm.nodes('/') AS n(v);

df columns has multiple comma separested values split into columns generate column name auto

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {'CustNum': [32363, 31316],
     'CustomerName': ['McCartney, Paul', 'Lennon, John'],
     'ItemQty': [3, 25],
     'Item': ['F04', 'F01'],
     'Seatblocks': ['test', 'test2,test3,test4'],
     'ItemExt': [60, 360]
    }
)
print(df)

df = df.join(df['Seatblocks'].str.split(',', expand=True).add_prefix('Seatblocks'))
print(df)