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