课程学习来源：b站up：【蚂蚁学python】
【课程链接：【【数据可视化】Python数据图表可视化入门到实战】】
【课程资料链接：【链接】】

Python绘制散点图查看BMI与保险费的关系

散点图:

• 用两组数据构成多个坐标点，考察坐标点的分布，判断两变量之间是否存在某种关联或总结坐标点的分布模式
• 散点图核心的价值在于发现变量之间的关系，然后进行预测分析，做出科学的决策

实例:医疗费用个人数据集中，"身体质量指数BMI"与"个人医疗费用"两者之间的关系

数据集原地址:https://www.kaggle.com/mirichoi0218/insurance/home

1.读取保险费数据集

import pandas as pd

df = pd.read_csv("../DATA_POOL/PY_DATA/ant-learn-visualization-master/datas/insurance/insurance.csv")

df.head(10)

	age	sex	bmi	children	smoker	region	charges
0	19	female	27.900	0	yes	southwest	16884.92400
1	18	male	33.770	1	no	southeast	1725.55230
2	28	male	33.000	3	no	southeast	4449.46200
3	33	male	22.705	0	no	northwest	21984.47061
4	32	male	28.880	0	no	northwest	3866.85520
5	31	female	25.740	0	no	southeast	3756.62160
6	46	female	33.440	1	no	southeast	8240.58960
7	37	female	27.740	3	no	northwest	7281.50560
8	37	male	29.830	2	no	northeast	6406.41070
9	60	female	25.840	0	no	northwest	28923.13692

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1338 entries, 0 to 1337
Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1338 non-null   int64  
 1   sex       1338 non-null   object 
 2   bmi       1338 non-null   float64
 3   children  1338 non-null   int64  
 4   smoker    1338 non-null   object 
 5   region    1338 non-null   object 
 6   charges   1338 non-null   float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB

2.pyecharts绘制散点图

# 将数据按照bmi升序排列
df.sort_values(by = "bmi", inplace = True)# inplace =true 表示直接更改df本身的数据
df.head()

	age	sex	bmi	children	smoker	region	charges
172	18	male	15.960	0	no	northeast	1694.79640
428	21	female	16.815	1	no	northeast	3167.45585
1226	38	male	16.815	2	no	northeast	6640.54485
412	26	female	17.195	2	yes	northeast	14455.64405
1286	28	female	17.290	0	no	northeast	3732.62510

bmi = df["bmi"].to_list()
charges = df["charges"].to_list()

import pyecharts.options as opts
from pyecharts.charts import Scatter

scatter = (
    Scatter()
    .add_xaxis(
        xaxis_data = bmi
    )
    .add_yaxis(
        series_name = "",
        y_axis = charges,
        symbol_size = 4,
        label_opts = opts.LabelOpts(is_show = False)
    )
    .set_global_opts(
        xaxis_opts = opts.AxisOpts(type_ = "value"),
        yaxis_opts = opts.AxisOpts(type_ = "value"),
        title_opts = opts.TitleOpts(title = "(BMI-保险费)关系图", pos_left = "center")
    )
)

from IPython.display import HTML

# 同上，读取 HTML 文件内容
# bar.render()的值是一个路径，以字符串形式表示
with open(scatter.render(), 'r', encoding='utf-8') as file:
    html_content = file.read()

# 直接在 JupyterLab 中渲染 HTML
HTML(html_content)