Python | Pandas dataframe.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.
Pandas dataframe.mod() function returns modulo of dataframe and other, element-wise (binary operator mod). This function is essentially same as the Dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. This function can be used with either a series or a dataframe.
 

Syntax: DataFrame.mod(other, axis='columns', level=None, fill_value=None) 
Parameters : 
Other : Series, DataFrame, or constant 
axis : For Series input, axis to match Series index on 
level : Broadcast across a level, matching Index values on the passed MultiIndex level 
fill_value : Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing
Returns : result : DataFrame
 

Example #1: Use mod() function to find the modulo of each value in the dataframe with a constant.
 

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.DataFrame({"A":[12, 4, 5, 44, 1], 
                   "B":[5, 2, 54, 3, 2], 
                   "C":[20, 16, 7, 3, 8], 
                   "D":[14, 3, 17, 2, 6]})

# Print the dataframe
df

Lets use the dataframe.mod() function to find the modulo of dataframe with 3 
 

# find mod of dataframe values with 3
df.mod(3)

Output : 
 

  
Example #2: Use mod() function to find the modulo with a series over the column axis. 
 

# importing pandas as pd
import pandas as pd

# Creating the dataframe 
df = pd.DataFrame({"A":[12, 4, 5, 44, 1], 
                   "B":[5, 2, 54, 3, 2], 
                   "C":[20, 16, 7, 3, 8], 
                   "D":[14, 3, 17, 2, 6]})

# Print the dataframe
df

Let's create the series object
 

# create a series
sr = pd.Series([3, 2, 4, 5])

# setting its column index similar to the dataframe
sr.index =["A", "B", "C", "D"]

# print the series
sr

Lets use the dataframe.mod() function to find the modulo of dataframe with series 
 

# find mod of dataframe values with series
# axis = 1 indicates column axis
df.mod(sr, axis = 1)

Output :