python iris 自编PCA和LDA dict转dataframe 平行坐标图

文章内容：使用鸢尾花数据，将sklearn自带的iris从字典dict格式转化为dataframe格式，用平行坐标图进行可视化，由图认为有必要做PCA和LDA，利用PCA和LDA的原理自编函数实现降维分析，分别绘制图像

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import warnings
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split  
from sklearn import datasets
from pandas.plotting import parallel_coordinates
plt.rcParams['font.sans-serif'] = ['SimHei']  # 绘图时可以显示中文
plt.rcParams['axes.unicode_minus']=False   # 绘图时显示负号
warnings.filterwarnings("ignore")  # 不要显示警告

1）read data

In [220]:

iris_data = datasets.load_iris() 

2) dict 转 dataframe

In [221]:

iris0 = pd.DataFrame(data= np.c_[iris_data['data'], iris_data['target']], columns = iris_data['feature_names'] + ['target'])

3）target 中 0,1,2 转字符

In [222]:

iris = iris0
species = pd.Series(iris.target)
species = species.replace(0, 'setosa')
species = species.replace(1, 'versicolor')
species = species.replace(2, 'virginica')
iris['target'] = species 

4) basic infomation

In [223]:

iris.sample(6)

Out[223]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
64	5.6	2.9	3.6	1.3	versicolor
32	5.2	4.1	1.5	0.1	setosa
144	6.7	3.3	5.7	2.5	virginica
86	6.7	3.1	4.7	1.5	versicolor
69	5.6	2.5	3.9	1.1	versicolor
46	5.1	3.8	1.6	0.2	setosa

In [224]:

iris.shape

O