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.sub() is used to subtract series or list like objects with same length from the caller series.
Syntax: Series.sub(other, level=None, fill_value=None, axis=0)
Parameters:
other: other series or list type to be subtracted from caller series
fill_value: Value to be replaced by NaN in series/list before subtracting
level: integer value of level in case of multi indexReturn type: Caller series with subtracted values
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: Subtracting List
In this example, the top 5 rows are stored in new variable using .head() method. After that a list of same length is created and subtracted from the salary column using .sub() method
# 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 =[5, 4, 3, 2, 1] # subtracting list data # creating new column short_data["Subtracted values"]= short_data["Salary"].sub(list) # display short_data |
Output:
As shown in the output image, it can be compared that the Subtracted values column is having the subtracted values of Salary column – list.
Example #2: Adding series to series with null values
In this example, the Age column is subtracted from the Salary column. Since the salary column contains null values too, by default it returns NaN no matter what is subtracted. In this example, 20 is passed to replace null values with 20.
# importing pandas module import pandas as pd # reading csv file from url # age series age = data["Age"] # na replacement na = 20 # adding values # storing to new column data["Subtracted values"]= data["Salary"].sub(other = age, fill_value = na) # display data |
Output:
As shown in the output image, the Subtracted value column has subtracted age column from 20 in case of Null values.
