23-Python-第三方库Json

myzbx 2025-05-28 19:13 17 浏览

1-json库的使用

`json`库是Python标准库的一部分，用于处理JSON数据。它提供了`loads`、`dumps`等方法。

安装三方库

pip install json

1-1-将JSON字符串解析为Python对象

1-1-1-语法

将JSON字符串解析为Python对象，然后可以像访问普通Python字典一样访问解析后的数据。

json.loads(json_str)

1-1-2-例子

import json

# 定义一个JSON字符串
json_str = '{"name": "张三", "age": 30, "city": "北京"}'

# 将JSON字符串解析为Python对象
data = json.loads(json_str)

# 访问解析后的数据
print(data['name'])
print(data['age'])
print(data['city'])

1-1-3-输出结果

1-2-将Python对象转换为JSON字符串

1-2-1-语法

`json.dumps`方法将Python字典转换为JSON字符串

json.dumps(pythonObj)

1-2-2-例子

import json

# 定义一个Python字典
data = {
    'name': 'John',
    'age': 30,
    'city': 'New York'
}

# 将Python对象转换为JSON字符串
json_str = json.dumps(data)

# 打印JSON字符串
print(json_str)

1-2-3-输出结果

1-3-例子

实现从json文件中读取不同省份的人口总数并显示成柱状图

1-3-1-例子01

population.json

[
    {"province": "广东省", "population": 126012510},
    {"province": "山东省", "population": 101527453},
    {"province": "河南省", "population": 98830000},
    {"province": "四川省", "population": 83674866},
    {"province": "江苏省", "population": 85054000},
    {"province": "河北省", "population": 74610235},
    {"province": "湖南省", "population": 66444864},
    {"province": "浙江省", "population": 65400000},
    {"province": "安徽省", "population": 61130000},
    {"province": "湖北省", "population": 58300000}
]

province_population_chart.py

用于封装json文件

import json
from jinja2 import Environment, FileSystemLoader
from collections import defaultdict


def read_province_population_from_json(json_file_path):
    province_population = defaultdict(int)
    try:
        with open(json_file_path, 'r', encoding='utf-8') as file:
            data = json.load(file)
            for item in data:
                province = item.get('province')
                population = item.get('population', 0)
                if province:
                    province_population[province] += population
        return province_population
    except FileNotFoundError:
        print(f"错误：未找到文件 {json_file_path}")
    except json.JSONDecodeError:
        print(f"错误：无法解析 {json_file_path} 为有效的 JSON")
    return province_population


def generate_bar_chart_html(province_population, html_output_path):
    env = Environment(loader=FileSystemLoader('.'))
    template = env.get_template('bar_chart_template.html')

    provinces = list(province_population.keys())
    populations = list(province_population.values())

    html_content = template.render(provinces=provinces, populations=populations)
    try:
        with open(html_output_path, 'w', encoding='utf-8') as file:
            file.write(html_content)
        print(f"HTML 文件已生成：{html_output_path}")
    except Exception as e:
        print(f"错误：写入 HTML 文件时出错 - {e}")


if __name__ == "__main__":
    json_file_path = 'province_population.json'
    html_output_path = 'province_population_chart.html'

    province_population = read_province_population_from_json(json_file_path)
    generate_bar_chart_html(province_population, html_output_path)

bar_chart_template.html

<!DOCTYPE html>
<html lang="en">

<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>省份人口柱状图</title>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>

<body>
    <canvas id="provincePopulationChart" width="800" height="400"></canvas>
    <script>
        const ctx = document.getElementById('provincePopulationChart').getContext('2d');
        const provinces = {{ provinces|tojson }};
        const populations = {{ populations|tojson }};

        new Chart(ctx, {
            type: 'bar',
            data: {
                labels: provinces,
                datasets: [{
                    label: '人口总数',
                    data: populations,
                    backgroundColor: 'rgba(75, 192, 192, 0.2)',
                    borderColor: 'rgba(75, 192, 192, 1)',
                    borderWidth: 1
                }]
            },
            options: {
                scales: {
                    y: {
                        beginAtZero: true
                    }
                }
            }
        });
    </script>
</body>

</html>

1-3-2-输出结果

1-3-3-例子02

创建 MySQL 数据库表，并将 2020 年到 2025 年不同省份的人口总数以饼状图形式显示在 HTML 中

database_operations.py

import random

def create_table(mycursor):
    mycursor.execute("""
    CREATE TABLE IF NOT EXISTS population (
        id INT AUTO_INCREMENT PRIMARY KEY,
        province VARCHAR(255),
        year INT,
        population INT
    )
    """)

def insert_data(mycursor, mydb):
    provinces = ["广东", "山东", "河南", "四川", "江苏"]
    years = [2020, 2021, 2022, 2023, 2024, 2025]
    for province in provinces:
        for year in years:
            population = random.randint(10000000, 20000000)
            sql = "INSERT INTO population (province, year, population) VALUES (%s, %s, %s)"
            val = (province, year, population)
            mycursor.execute(sql, val)
    mydb.commit()

def query_data(mycursor):
    provinces = ["广东", "山东", "河南", "四川", "江苏"]
    total_population = {}
    for province in provinces:
        sql = "SELECT SUM(population) FROM population WHERE province = %s AND year BETWEEN 2020 AND 2025"
        val = (province,)
        mycursor.execute(sql, val)
        result = mycursor.fetchone()
        total_population[province] = result[0]
    return total_population

database_connection.py

import mysql.connector

def create_connection():
    mydb = mysql.connector.connect(
        host="localhost",
        user="your_username",
        password="your_password",
        database="your_database"
    )
    return mydb

chart_generator.py

import matplotlib.pyplot as plt
import os

def generate_chart(total_population):
    labels = total_population.keys()
    sizes = total_population.values()

    plt.pie(sizes, labels=labels, autopct='%1.1f%%')
    plt.axis('equal')

    if not os.path.exists('charts'):
        os.makedirs('charts')
    chart_path = 'charts/population_pie_chart.png'
    plt.savefig(chart_path)
    plt.close()
    return chart_path

html_generator.py

def generate_html(chart_path):
    html_content = f"""
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <meta charset="UTF-8">
        <title>2020 - 2025 年不同省份人口总数饼状图</title>
    </head>
    <body>
        <h1>2020 - 2025 年不同省份人口总数饼状图</h1>
        <img src="{chart_path}" alt="Population Pie Chart">
    </body>
    </html>
    """
    with open('population_chart.html', 'w', encoding='utf-8') as f:
        f.write(html_content)

main.py

from database_connection import create_connection
from database_operations import create_table, insert_data, query_data
from chart_generator import generate_chart
from html_generator import generate_html

# 建立数据库连接
mydb = create_connection()
mycursor = mydb.cursor()

# 创建表
create_table(mycursor)

# 插入数据
insert_data(mycursor, mydb)

# 查询数据
total_population = query_data(mycursor)

# 生成饼状图
chart_path = generate_chart(total_population)

# 生成 HTML 文件
generate_html(chart_path)

print("数据库表创建成功，数据插入成功，饼状图已保存为图片，HTML 文件已生成。")

Chart.js

上一篇：ejschart 最简便的前端图表框架-入门篇
下一篇：20、数据可视化:魔镜报表——React 19 图表集成

23-Python-第三方库Json

1-json库的使用

1-1-将JSON字符串解析为Python对象

1-1-1-语法

1-1-2-例子

1-1-3-输出结果

1-2-将Python对象转换为JSON字符串

1-2-1-语法

1-2-2-例子

1-2-3-输出结果

1-3-例子

1-3-1-例子01

population.json

province_population_chart.py

bar_chart_template.html

1-3-2-输出结果

1-3-3-例子02

database_operations.py

database_connection.py

chart_generator.py

html_generator.py

main.py

相关推荐

使用VSCode模板提高Vue开发效率（vscode开发vue3）

吃透 Vue 项目开发实践|16个方面深入前端工程化开发技巧【下】

8.7k star，一款超炫酷的动态可视化大屏项目

移动前端重构实战系列:5-7章（前端移动端主要技术有哪些）

说下你的vue项目的目录结构，该怎么划分?

这么牛逼的cursor功能，赶紧用起来

用上Vue3，你真的变了吗?（vue3 key）

20个免费音乐音效下载网站，无版权素材商业影片也可用

什么是“恒定光圈”，什么是“浮动光圈”?-摄影早自习第331

comfyui安装MimicMotion扩展，AI跳舞动作模仿