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Split a text column into two columns in Pandas DataFrame
  • Difficulty Level : Medium
  • Last Updated : 26 Dec, 2018

Let’s see how to split a text column into two columns in Pandas DataFrame.

Method #1 : Using Series.str.split() functions.

Split Name column into two different columns. By default splitting is done on the basis of single space by str.split() function.




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John Larter', 'Robert Junior', 'Jonny Depp'],
                   'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
   
# bydefault splitting is done on the basis of single space.
print("\nSplitting 'Name' column into two different columns :\n",
                                  df.Name.str.split(expand=True))

Output :

 
Split Name column into “First” and “Last” column respectively and add it to the existing Dataframe .




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John Larter', 'Robert Junior', 'Jonny Depp'],
                    'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
   
# Adding two new columns to the existing dataframe.
# bydefault splitting is done on the basis of single space.
df[['First','Last']] = df.Name.str.split(expand=True)
   
print("\n After adding two new columns : \n", df)

Output:



 
Use underscore as delimiter to split the column into two columns.




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John_Larter', 'Robert_Junior', 'Jonny_Depp'],
                    'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
   
# Adding two new columns to the existing dataframe.
# splitting is done on the basis of underscore.
df[['First','Last']] = df.Name.str.split("_",expand=True)
   
print("\n After adding two new columns : \n",df)

Output :

 
Use str.split(), tolist() function together.




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John_Larter', 'Robert_Junior', 'Jonny_Depp'],
                    'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
  
print("\nSplitting Name column into two different columns :"
print(pd.DataFrame(df.Name.str.split('_',1).tolist(),
                         columns = ['first','Last']))

Output :

 
Method #2 : Using apply() function.

Split Name column into two different columns.




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John_Larter', 'Robert_Junior', 'Jonny_Depp'],
                    'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
  
print("\nSplitting Name column into two different columns :"
print(df.Name.apply(lambda x: pd.Series(str(x).split("_"))))

Output :

Split Name column into two different columns named as “First” and “Last” respectively and then add it to the existing Dataframe.




# import Pandas as pd
import pandas as pd
   
# create a new data frame
df = pd.DataFrame({'Name': ['John_Larter', 'Robert_Junior', 'Jonny_Depp'],
                    'Age':[32, 34, 36]})
   
print("Given Dataframe is :\n",df)
  
print("\nSplitting Name column into two different columns :"
  
# splitting 'Name' column into Two columns 
# i.e. 'First' and 'Last'respectively and 
# Adding these columns to the existing dataframe.
df[['First','Last']] = df.Name.apply(
   lambda x: pd.Series(str(x).split("_")))
   
print(df)

Output :

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