Iris数据集实战

pip install seaborn

读取文件

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df_Iris = pd.read_csv(r'iris.data.txt')

数据可视化
花萼长度与宽度/花瓣长度与宽度的可视化

from collections import Counter, defaultdict
import matplotlib
import matplotlib.pyplot as plt
import numpy as np 
 matplotlib.rcParams['font.sans-serif'] = ['SimHei']
 
style_list = ['o', '^', 's']       # 设置点的不同形状，不同形状默认颜色不同，也可自定义
data = iris_datas.data
labels = iris_datas.target_names
cc = defaultdict(list)
 
for i, d in enumerate(data):
    cc[labels[int(i/50)]].append(d) 
p_list = []
c_list = []
 
for each in [0, 2]:
    for i, (c, ds) in enumerate(cc.items()):
        draw_data = np.array(ds)
        p = plt.plot(draw_data[:, each], draw_data[:, each+1], style_list[i])
        p_list.append(p)
        c_list.append(c)
 
    plt.legend(map(lambda x: x[0], p_list), c_list)
    plt.title('鸢尾花花瓣的长度和宽度') if each else plt.title('鸢尾花花萼的长度和宽度')
    plt.xlabel('花瓣的长度(cm)') if each else plt.xlabel('花萼的长度(cm)')
    plt.ylabel('花瓣的宽度(cm)') if each else plt.ylabel('花萼的宽度(cm)')
    plt.show()

数据直方图

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"  
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']  
dataset = pd.read_csv(url, names=names)
dataset.hist()

散点图

dataset.plot(x='sepal-length', y='sepal-width', kind='scatter')

KDE图
KDE图也被称作密度图(Kernel Density Estimate,核密度估计)。

dataset.plot(kind='kde') 

箱线图

dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)

……

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博客刚开，多多包涵，以后会尽量完善的
I’m Ray.I’m ok.