Python | Pandas dataframe.rmul()

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.rmul() function is used for finding the multiplication of dataframe and other, element-wise (binary operator rfloordiv). This function is essentially same as doing other * dataframe but with a support to substitute for missing data in one of the inputs. 

Syntax:DataFrame.rmul(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 rmul() function to find the multiplication of a series with a dataframe.

Python3

# 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]},
                    index =["A1", "A2", "A3", "A4", "A5"])

# Print the dataframe
df

Let’s create the series 

Python3

# importing pandas as pd
import pandas as pd

# Create the series
sr = pd.Series([12, 25, 64, 18], index =["A", "B", "C", "D"])

# Print the series
sr

Lets use the dataframe.rmul() function to find the multiplication of a series with dataframe 

Python3

df.rmul(sr, axis = 1)

Output : 

Example #2: Use rmul() function to perform multiplication of a dataframe with other.

Python3

# 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]},
                    index =["A1", "A2", "A3", "A4", "A5"])

# Creating the second dataframe
df2 = pd.DataFrame({"A":[10, 11, 7, 8, 5],
                    "B":[21, 5, 32, 4, 6],
                    "C":[11, 21, 23, 7, 9],
                    "D":[1, 5, 3, 8, 6]},
                     index =["A1", "A2", "A3", "A4", "A5"])

# Print the first dataframe
print(df1)

# Print the second dataframe
print(df2)

Lets perform df2 * df1 

Python3

# perform multiplication of df2 with df1
df1.rmul(df2)

Output :