DAY 9 热力图和子图的绘制

最新推荐文章于 2025-12-09 22:34:00 发布

原创

最新推荐文章于 2025-12-09 22:34:00 发布 · 724 阅读

15 ·

CC 4.0 BY-SA版权

文章标签：

#机器学习 #python #jupyter

知识点：热力图和子图的绘制

介绍了热力图的绘制方法
介绍了子图的绘制方法
介绍了enumerate（）函数

import pandas as pd
dt = pd.read_csv('data.csv')

将 Years in current job 和 Home Ownership 列映射为数字，绘制箱线图，数据至少要是数值，否则报错

查看 Years in current job 下的值：

dt['Years in current job'].value_counts()

输出：

Years in current job
10+ years    2332
2 years       705
3 years       620
< 1 year      563
5 years       516
1 year        504
4 years       469
6 years       426
7 years       396
8 years       339
9 years       259
Name: count, dtype: int64

# 创建映射字典
mapping = {
  "Home Ownership": {
    'Home Mortgage': 3,
    'Rent': 1,
    'Own Home': 0,
    'Have Mortgage': 2
  },
  "Years in current job":{
    '< 1 year':0,
    '1 year':1,
    '2 years':2,
    '3 years':3,
    '4 years':4,
    '5 years':5,
    '6 years':6,
    '7 years':7,
    '8 years':8,
    '9 years':9,
    '10+ years':10
  }
}
# map()映射
dt['Home Ownership'] = dt['Home Ownership'].map(mapping['Home Ownership'])
dt['Years in current job'] = dt['Years in current job'].map(mapping['Years in current job'])

查看映射后的效果：

# 查看映射后的效果
dt.head(10)

输出：

一、绘制热力图

热力图本质上只能对连续值进行处理，不适合处理数值型的离散值

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# 连续特征 continuous_features
continuous_features=[
 'Annual Income',
 'Years in current job',
 'Number of Open Accounts',
 'Years of Credit History',
 'Maximum Open Credit',
 '

最低0.47元/天解锁文章