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.rsub() function is used for finding the subtraction 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.rsub(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 rsub() function to subtract each element of a series to a corresponding value in a dataframe over the column axis.
# 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
# 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.rsub() function to subtract each element in a series with the corresponding element in the dataframe.
# equivalent to sr - df df.rsub(sr, axis = 1) |
Output :
Example #2: Use rsub() function to subtract each element in a dataframe with the corresponding element in other dataframe
# 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
# subtract df1 from df2 df1.rsub(df2) |
Output :
