So I want to multiply each row of a dataframe with a multiplier vector, and I am managing, but it looks ugly. Can this be improved?
import pandas as pd
import numpy as np
# original data
df_a = pd.DataFrame([[1,2,3],[4,5,6]])
print(df_a, '\n')
# multiplier vector
df_b = pd.DataFrame([2,2,1])
print(df_b, '\n')
# multiply by a list - it works
df_c = df_a*[2,2,1]
print(df_c, '\n')
# multiply by the dataframe - it works
df_c = df_a*df_b.T.to_numpy()
print(df_c, '\n')
"It looks ugly" is subjective, that said, if you want to multiply all rows of a dataframe with something else you either need:
a dataframe of a compatible shape (and compatible indices, as those are aligned before operations in pandas, which is why df_a*df_b.T would only work for the common index: 0)
a 1D vector, which in pandas is a Series
Using a Series:
df_a*df_b[0]
output:
0 1 2
0 2 4 3
1 8 10 6
Of course, better define a Series directly if you don't really need a 2D container:
s = pd.Series([2,2,1])
df_a*s
Just for the beauty, you can use Einstein summation:
>>> np.einsum('ij,ji->ij', df_a, df_b)
array([[ 2, 4, 3],
[ 8, 10, 6]])
