动态可视化图表：“城市居民与农民生存大解析！消费指数狂飙，究竟是福是祸？

地址

https://www.bilibili.com/video/BV1W64y1N7oV/?share_source=copy_web&vd_source=494dad6ec7cce090ffcc05c1b6a83c00

图片

源代码

main.py

import json
import pandas as pd

# 此处修改！！！！！！！！！！！！！！！！这里找到配置文件！！！
with open('config/config.json', 'r') as config_file:
    config = json.load(config_file)

# 使用配置信息
file_path = config["file_path"]
num_columns = config["num_columns"]
labels = config["labels"]
years = config["years"]

# 读取文件内容
with open(file_path, 'r') as file:
    lines = file.readlines()

# 将每行数据存储到一个列表中
data_list = [float(line.strip()) for line in lines]

# 编写1：每行19个数据的形式
formatted_data = [data_list[i:i + num_columns] for i in range(0, len(data_list), num_columns)]

# 构建数据结构，按照内容和年份分组
content_data = {label: {year: value for year, value in zip(years, row_data)} for label, row_data in
                zip(labels, zip(*formatted_data))}

# 打印结果
output_data = [["金额", "", "", "名称", "年份"]]

for label, year_data in content_data.items():
    output_data.extend([year_data[year], "", "", label, year] for year in years)

# 打印结果，每行最后一个元素后面加逗号
for row in output_data:
    # 对名称一列加上双引号
    row[1] = f'"{row[1]}"'
    row[2] = f'"{row[2]}"'
    row[3] = f'"{row[3]}"'
    row_str = ",".join(map(str, row))
    print(f"[{row_str}],")



# 转换为 DataFrame
df = pd.DataFrame(content_data)

# 将 DataFrame 写入 Excel 文件
excel_path = 'output/output_excel.xlsx'
df.to_excel(excel_path, index_label="年份")

config.json

{
  "file_path": "resource/result2.txt",
  "num_columns": 7,
  "labels": [
    "居民消费价格指数",
    "城市居民消费价格指数",
    "农村居民消费价格指数",
    "商品售价价格指数",
    "农产品生产者价格指数",
    "工业生产者出山价格指数",
    "工业生产者购进价格指数"
  ],
  "years": [
    1990,
    1995,
    1996,
    1997,
    1998,
    1999,
    2000,
    2001,
    2002,
    2003,
    2004,
    2005,
    2006,
    2007,
    2008,
    2009,
    2010,
    2011,
    2012,
    2013,
    2014,
    2015,
    2016,
    2017,
    2018,
    2019,
    2020,
    2021,
    2022
  ]
}

数据就写在Result2.txt