In R and pandas, we can apply arithmetic operations in data columns in a similar manner. Hence, we can perform arithmetic operations such as addition or subtraction on elements in corresponding positions in two or more DataFrames.

Here, we construct a DataFrame in R with columns labeled x and y, and subtract column y from column x:

    >norm_df<- data.frame(x=rnorm(7,0,1), y=rnorm(7,0,1))
    >norm_df$x - norm_df$y
    [1] -1.3870730  2.4681458 -4.6991395  0.2978311 -0.8492245  1.5851009 -1.4620324

The with operator in R also has the same effect as arithmetic operations:

    >with(norm_df,x-y)
    [1] -1.3870730  2.4681458 -4.6991395  0.2978311 -0.8492245  1.5851009 -1.4620324

In pandas, the same arithmetic operations can be performed on columns and the equivalent operator is eval:

    In [10]: import pandas as pd
             import numpy as np
    df = pd.DataFrame({'x': np.random.normal(0,1,size=7), 'y': np.random.normal(0,1,size=7)})
    
    In [11]: df.x-df.y
    Out[11]: 0   -0.107313
             1    0.617513
             2   -1.517827
             3    0.565804
             4   -1.630534
             5    0.101900
             6    0.775186
    dtype: float64
    
    In [12]: df.eval('x-y')
    Out[12]: 0   -0.107313
             1    0.617513
             2   -1.517827
             3    0.565804
             4   -1.630534
             5    0.101900
             6    0.775186
    dtype: float64