因子分析：从 FactorAnalyzer.loadings_

如何解决因子分析：从 FactorAnalyzer.loadings_

我开始熟悉 Python 的 FactorAnalyzer 进行因子分析。

需要帮助打印带有因子系数和显着性参数的因子标签。

我使用 loadings_ 但它的输出非常混乱；

数据源（我下载到我的笔记本上）：https://vincentarelbundock.github.io/Rdatasets/datasets.html（基于性格评估的 BFI 数据集）。

import pandas as pd
from sklearn.datasets import load_iris
from factor_analyzer import FactorAnalyzer
import matplotlib.pyplot as plt


df = pd.read_csv("../Downloads/bfi.csv")
df.head()

# Dropping unnecessary columns
df.drop(['gender','education','age'],axis=1,inplace=True)

# Dropping missing values rows
df.dropna(inplace=True)

基于特征值，只有 6 个因子是显着的（值 >1.0）。因为我不能得到好的输出，我不知道这些因素。

fa = FactorAnalyzer( method='minres',n_factors=6,rotation="varimax")

fa.fit(df)

print(fa.loadings_)

输出：

数组([[-0.02290301,-0.03247244,0.03316871,-0.03809335,0.00379506,0.10374847]， [ 0.09939617,0.06047379,0.02669442,-0.53078469,-0.12030937,0.16363839]， [ 0.03176731,0.259875,0.1402256,0.64656946,0.05577021,-0.09704963]， [-0.00525556,0.40884857,0.10953353,0.5870038,0.01618433,0.03914857]， [-0.07926603,0.25534237,0.22930809,0.39176034,-0.13629257,0.03340065]， [-0.14364476,0.4910488,0.0856494,0.45108989,0.00911123,0.10588827]， [ 0.00562295,0.12364715,0.54015018,0.00422137,0.18345833,0.13879815]， [ 0.08435816,0.10650466,0.65249593,0.05653766,0.0792028,0.20858043]， [-0.03394649,0.0497959,0.54587749,0.10028627,-0.0123717,0.05447959]， [ 0.23161662,0.0089893,-0.67278538,-0.08998026,-0.15345088,0.226977]， [ 0.29340234,-0.1436436,-0.55970426,-0.04706994,0.0256143,0.09577898]， [ 0.05310218,-0.52147723,0.02649196,-0.09054497,-0.05928098,0.33201867]， [ 0.26318891,-0.62292324,-0.11075758,-0.07455019,-0.03044005,0.29120361]， [ 0.00119,0.63056485,0.07741736,0.15388275,0.21421252,0.09215221]， [-0.14723885,0.68281775,0.10390412,0.2065131,-0.13327166,-0.03773659]， [ 0.02197833,0.50438366,0.31238313,0.04844782,0.18521834,-0.11350852]， [ 0.79096653,0.033469,-0.04001445,-0.19151604,-0.07737848,-0.16815916]， [ 0.77708495,-0.01765921,-0.02173671,-0.15558624,0.00764293,-0.19939099]， [ 0.72818732,-0.03614561,-0.0674602,-0.02313414,-0.01532483,0.02192578]， [ 0.59778566,-0.2770728,-0.1837043,0.01861508,0.06451108,0.18288879]， [ 0.53479082,-0.11293748,-0.04097176,0.09644977,-0.1645811,0.11185692]， [-0.00891931,0.3023176,0.10733112,-0.00134206,0.46434464,0.16741622]， [ 0.16146455,0.02029611,-0.10051682,0.04691938,-0.50064301,0.08416413]， [ 0.0196248,0.40211954,0.07042896,0.06363394,0.54784203,0.12081641]， [ 0.22872114,-0.0926477,-0.03000306,0.14801512,0.34628284,0.20228616],[ 0.06801995,0.00091956,-0.06223948,-0.05313796,-0.57993276,0.10662123]])

这是我的问题。我想不出让输出可用的方法（类似这样）：

 Factor 1.  Factor 2. Factor 3.  factor 4.  Factor 5.  Factor 6

A1.    value 1.   value 2.  value 2.   value 4.   value 5.   Value 6

A1 是第一个变量。总共有 26 个以上的变量 * 在下降之前）。有 2700 条记录。

如何以可用的方式打印 loadings_ 的输出？
如何仅打印带有我选择的标签的因子（在我的例子中是 6 个）？
fa.get_factor_variance() 的相同输出问题

fa.get_factor_variance()

(数组([2.76721162,2.72814014,2.07554605,1.6108362,1.46335442,0.62155903]），数组([0.10643122,0.10492847,0.07982869,0.06195524,0.05628286,0.02390612]），数组（[0.10643122,0.21135968,0.29118838,0.35314362,0.40942648,0.43333259]))

感谢您的帮助！！