如何使用 Numpy 通过来自另一个 DataFrame 的因子来缩放 DataFrame 中的列

Question

To Scale each columns (A, B, C) in a DataFrame df:

l1 = [1,2,3]
l2 = [4,5,6]
l3 = [7,8,9]

df = pd.DataFrame([z for z in zip(l1,l2,l3)], columns= ['A', 'B', 'C'])

with scaling factors in a DataFrame scaling:

scaling = pd.DataFrame(dict(id=['B', 'A','C'], scaling = [0.2, 0.3, 0.4]))

using Numpy:

df = pd.DataFrame(np.array(df)*np.array(scaling['scaling']), columns=df.columns)

How to obtain right factors from scaling with the corresponding id ['B', 'A','C'] using Numpy?

I expected to have the following result with print(df)

   A    B    C
0  0.3  0.8  2.8
1  0.6  1.0  3.2
2  0.9  1.2  3.6

Answer 1

Try something like:

import pandas as pd

l1 = [1, 2, 3]
l2 = [4, 5, 6]
l3 = [7, 8, 9]

df = pd.DataFrame([z for z in zip(l1, l2, l3)], columns=['A', 'B', 'C'])

scaling = pd.DataFrame(dict(id=['B', 'A', 'C'], scaling=[0.2, 0.3, 0.4]))

# Get Scaling Into a more Usable Format
scaling = scaling.set_index('id').reindex(df.columns).to_numpy().reshape(1, -1)

# Perform scaling
scaled_df = df * scaling
print(scaled_df)

---

The goal is to just get scaling into a shape that can be easily applied to the DataFrame scaling . Once scaling is in the right shape and order:

   scaling
A      0.3
B      0.2
C      0.4

[[0.3 0.2 0.4]]

It can just be multiplied by the df :

     A    B    C
0  0.3  0.8  2.8
1  0.6  1.0  3.2
2  0.9  1.2  3.6