Python | Pandas dataframe.rdiv()

Last Updated : 22 Nov, 2018

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.rdiv() function compute Floating division of dataframe and other, element-wise (binary operator rtruediv). Other object could be a scalar, pandas series or pandas dataframe. This function is essentially same as doing other / dataframe but with support to substitute a fill_value for missing data in one of the inputs.

Syntax: DataFrame.rdiv(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
numeric_only : Include only float, int, boolean data. Valid only for DataFrame or Panel objects
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 rdiv() function to divide a series with a dataframe element-wise

# importing pandas as pd 
import pandas as pd 
  
# Creating the dataframe  
df = pd.DataFrame({"A":[1, 5, 3, 4, 2], 
                   "B":[3, 2, 4, 3, 4],  
                   "C":[2, 2, 7, 3, 4], 
                   "D":[4, 3, 6, 12, 7]}) 
  
# Print the dataframe 
df 

Lets create a series

# importing pandas as pd 
import pandas as pd 
  
# Create a series 
sr = pd.Series([5, 10, 15, 20], index =["A", "B", "C", "D"]) 
  
# Print the series 
sr 

Let’s use the dataframe.rdiv() function to divide the series with a dataframe

# perform division of series with  
# dataframe element-wise over the column axis 
df.rdiv(sr, axis = 1) 

Output :

Example #2: Use rdiv() function to divide one dataframe with other which contains NaN value.

# importing pandas as pd 
import pandas as pd 
  
# Creating the first dataframe  
df1 = pd.DataFrame({"A":[1, 5, 3, 4, 2], 
                    "B":[3, 2, 4, 3, 4],  
                    "C":[2, 2, 7, 3, 4], 
                    "D":[4, 3, 6, 12, 7]}) 
  
# Creating the second dataframe 
df2 = pd.DataFrame({"A":[14, 5, None, 4, 12], 
                    "B":[7, 6, 4, 5, None], 
                    "C":[2, 11, 4, 3, 6],  
                    "D":[4, None, 6, 2, 4]}) 
  
# divide df2 by df1 element-wise 
# Fill all the missing values by 100 
df1.rdiv(df2, fill_value = 100) 