pandas 函数基于dict创建组合列 [英] pandas function to create combined column based on dict

问题描述

我正在尝试在 pandas.DataFrame

中创建一个加权列

我有一个python dictionary ，键为 pandas.DataFrame 列名，值为相应的权重。

I have a python dictionary with the keys being the pandas.DataFrame column names and the values the corresponding weights.

我想创建一个新列，该列根据字典和引用 pandas.DataFrame 列值。

I would like to create a new column which is weighted based on the dictionary and reference pandas.DataFrame column values.

考虑到我的字典配置将更改并包含错误配置？

What is an efficient way to do this considering my dictionary configuration will change and contain "misconfiguration" ?

例如：

import pandas as pd
import numpy as np
weights = {'IX1' : 0.3, 'IX2' : 0.2, 'IX3' : 0.4, 'IX4' : 0.1}
np.random.seed(0)
df = pd.DataFrame(np.random.randn(10, 3), columns=['IX1', 'IX2', 'IX3'])

##Desired output --- manually combine
df['Composite'] = df['IX1']*0.3 + df['IX2']*0.2 + df['IX3']*0.4

我希望代码仍然可以运行，即使 pandas.DataFrame 缺少列

I would like the code to still run even if the pandas.DataFrame is missing columns

推荐答案

首先创建 Index.intersection ，然后选择此列，并与 dot 与 Series from dict仅针对同一列进行过滤：

First create variable for same values for columns and keys in dictionary by Index.intersection, then select this columns and use matrix multiplication with dot with Series from dict filtered for same columns only:

df['Composite'] = df['IX1']*0.3 + df['IX2']*0.2 + df['IX3']*0.4

cols = df.columns.intersection(weights.keys())
df['Composite1'] = df[cols].dot(pd.Series(weights)[cols])
print (df)
        IX1       IX2       IX3  Composite  Composite1
0  1.764052  0.400157  0.978738   1.000742    1.000742
1  2.240893  1.867558 -0.977278   0.654868    0.654868
2  0.950088 -0.151357 -0.103219   0.213468    0.213468
3  0.410599  0.144044  1.454274   0.733698    0.733698
4  0.761038  0.121675  0.443863   0.430192    0.430192
5  0.333674  1.494079 -0.205158   0.316855    0.316855
6  0.313068 -0.854096 -2.552990  -1.098095   -1.098095
7  0.653619  0.864436 -0.742165   0.072107    0.072107
8  2.269755 -1.454366  0.045759   0.408357    0.408357
9 -0.187184  1.532779  1.469359   0.838144    0.838144

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