flask + Pandas + echarts 使用饼状图等将二手房数据进行分析+可视化

'''
Description: 分页读取并显示 csv 文件数据
'''
from math import ceil
import csv
import json

from flask import Flask, render_template, request, redirect, jsonify
from spiders.file_manager import FileManager

# 初始化框架
web = Flask(__name__)


@web.route('/')
def index():
    # 首页
    return redirect('/list')


@web.route('/table/<int:page_num>')
def table(page_num):
    # 分页读取并显示 csv 文件数据
    file_manager = FileManager()
    file = file_manager.get_data_file_path("tao365_detail.csv")

    # 若不指定limits默认为 5
    limits = request.args.get('limits', 5, type=int)

    def show_csv(reader, page=page_num, limits=limits):
        # 内部函数，根据limits和所在页数生成数据
        df = []
        for row in reader:
            if page_num * limits >= (reader.line_num - 1) > (page_num - 1) * limits:
                df.append(row)

        return df

    with open(file, 'r+', encoding='utf-8') as f:
        # 计算页面数
        row_length = len(f.readlines()) - 1
        pages = int(ceil(row_length / limits))

    with open(file, 'r+', encoding='utf-8') as f:
        # 计算数据
        reader = csv.reader(f)
        next(reader)
        df = show_csv(reader, page_num, limits)

    # 加载模版和模版数据
    return render_template('table.html', df=df, pages=pages, page_num=page_num, limits=limits)


@web.route('/table_detail')
def table_detail():
    # 二手房详情
    row = request.args.get('row').split(',')
    return render_template('table_detail.html', row=row)

@web.route('/list')
def list_house():
    # 二手房列表
    return render_template('list.html')

@web.route('/chart1')
def chart1():
    # 柱状图
    return render_template('chart1.html')

@web.route('/chart2')
def chart2():
    # 横向柱状图
    return render_template('chart2.html')

@web.route('/chartBi