Pandas provide a method to split string around a passed separator/delimiter. After that, the string can be stored as a list in a series or it can also be used to create multiple column data frames from a single separated string. It works similarly to Python’s default split() method but it can only be applied to an individual string. Pandas <code>str.split() method can be applied to a whole series. .str has to be prefixed every time before calling this method to differentiate it from Python’s default function otherwise, it will throw an error. To work in google colab import the files before using the dataset.
Syntax:
Series.str.split(pat=None, n=-1, expand=False)Parameters:
- pat: String value, separator or delimiter to separate string at.
- n: Numbers of max separations to make in a single string, default is -1 which means all.
- expand: Boolean value, returns a data frame with different value in different columns if True. Else it returns a series with list of strings.
Return Type: Series of list or Data frame depending on expand Parameter
To download the CSV used in code, click here.
In the following examples, the data frame used contains data of some NBA players. The image of data frame before any operations is attached below.
Example #1: Splitting string into list In this data, the split function is used to split the Team column at every “t”. The parameter is set to 1 and hence, the maximum number of separations in a single string will be 1. The expand parameter is False and that is why a series with List of strings is returned instead of a data frame.
Python3
# importing pandas moduleimport pandas as pd # reading csv file from urldata = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") # dropping null value columns to avoid errorsdata.dropna(inplace = True) # new data frame with split value columnsdata["Team"]= data["Team"].str.split("t", n = 1, expand = False)# df displaydata |
Output: 
As shown in the output image, the Team column is now having a list. The string was separated at the first occurrence of “t” and not at the later occurrence since the n parameter was set to 1 (Max 1 separation in a string).
Example #2: Making separate columns from string In this example, the Name column is separated at space (” “), and the expand parameter is set to True, which means it will return a data frame with all separated strings in different columns. The Data frame is then used to create new columns and the old Name column is dropped using .drop() method.
Python3
# importing pandas moduleimport pandas as pd # reading csv file from urldata = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")# dropping null value columns to avoid errorsdata.dropna(inplace = True)# new data frame with split value columnsnew = data["Name"].str.split(" ", n = 1, expand = True)# making separate first name column from new data framedata["First Name"]= new[0]# making separate last name column from new data framedata["Last Name"]= new[1]# Dropping old Name columnsdata.drop(columns =["Name"], inplace = True)# df displaydata |
Output: As shown in the output image, a new data frame was returned by the split() function and it was used to create two new columns ( First Name and Last Name) in the data frame.
New Data frame
Data frame with Added columns 
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