Python | Pandas Series.mod()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Python Series.mod() is used to return the remainder after division of two numbers

Syntax: Series.mod(other, axis=’columns’, level=None, fill_value=None)

Parameters:
other: other series or list type to be divided and checked for remainder with caller series
fill_value: Value to be replaced by NaN in series/list before operation
level: integer value of level in case of multi index

Return type: Caller series with mod values ( caller series [i] % other series [i] )



To download the data set used in following example, 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: Checking remainder

In this example, 5 rows of data frame are extracted using head() method. A series is created from a Python list using Pandas Series() method. The mod() method is called on new short data frame and the created list is passed as other parameter.

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# importing pandas module  
import pandas as pd 
    
# reading csv file from url  
    
# creating short data of 5 rows 
short_data = data.head() 
    
# creating list with 5 values 
list =[1, 2, 3, 4, 3
    
# finding remainder
# creating new column 
short_data["Remainder"]= short_data["Salary"].mod(list
    
# display 
short_data 

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Output:

As shown in output image, remainder after division of values at same index in caller series and other series was returned. Since nothing was passed to fill_value parameter, the Null values are returned as it is.

 
Example #2: Handling Null Values

Just like in the above example, same steps are done but this time a variable is created and some random value is passed to it. The value is then passed as fill_value parameter to mod() method.

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# importing pandas module  
import pandas as pd 
    
# reading csv file from url  
    
# creating short data of 5 rows 
short_data = data.head() 
    
# creating list with 5 values 
list =[1, 2, 3, 4, 3
    
# replacing null value with any number
null_replacement = 21218
  
# finding remainder
# creating new column 
short_data["Remainder"]= short_data["Salary"].mod(list, fill_value = null_replacement) 
    
# display 
short_data 

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Output:
As shown in output image, null values were replaced by 21218 and all operations were done with that value. Hence, instead of NaN, 21218 % 3 = 2 was returned at 3rd position.



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