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  2. Python | Pandas Split strings into two lists / columns using str.split ()

Python | Pandas Split strings into two lists / columns using str.split ()

It works similarly to the default

Example # 1: Splitting a line into list

This data uses the split function to split the “Command” column at each “t”. The parameter is set to 1, and therefore the maximum number of splits per line will be 1. The expansion parameter is False, and therefore a series with a list of lines is returned instead of a data frame.

# pandas module import

import pandas as pd

 
# read CSV file from URL

data = pd.read_csv ( " https://media.python.engineering/wp-content/uploads /nba.csv " )

  
# deleting columns null to avoid errors

data.dropna (inplace = True )

 
# new data frame with delimited columns

data [ "Team" ] = data [ " Team " ]. str . Split ( "t" , n = 1 , expand = True )

 
# df display
data

Output:
As shown in the output image, the Team column now has a list. The line was split on the first occurrence of “t” and not on subsequent occurrences because the parameter n was set to 1 (max. 1 split per line). 

Example # 2: Creating separate columns from a string

In this example, the Name column is separated by a space (“”) and the extension parameter is set to True, which means it will return a data frame with all the separated rows in different columns. The dataframe is then used to create new columns and the old name column is dropped using the .drop () method.

 

# pandas module import

import pandas as pd

 
# read CSV file from URL

data = pd.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv " )

 
# deleting null columns, to avoid errors

data.dropna (inplace  = True )

 
# new data frame with delimited columns

new = data [ " Name " ]. str . split ( "" , n = 1 , expand = True )

 
# create a separate name column from a new dataframe

data [ "First Name" ] = new [ 0 ]

 
# create a separate last name column from a new data frame

data [ " Last Name " ] = new [ 1 ]

 
# Removing old columns Name

data.drop ( columns = [ "Name" ], inplace = True )

 
# df display
data

Output:
As shown in the output image, the split () function returned a new dataframe, and it was used to create two new columns (First Name and Last Name) in the dataframe.

New dataframe

Data frame with added columns