'''
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('/chartBie')
def chartBie():
    # 饼图
    return render_template('chartBie.html')

@web.route('/chart_data')
def chart_data():
    # 获取图标数据

    # 图标类型
    type = request.args.get("type")

    # 二手房数据
    file_name = 'chart00' + type+'.json'
    file_manager = FileManager()
    file = file_manager.get_data_file_path(file_name)
    # file = os.path.join(config.DATA_DIR,'chart00'+ type+'.json' )
    with open(file, encoding='utf8') as fp:
        data_list = json.load(fp)

    return jsonify(data_list)


# 启动 flask web 框架
web.run(debug=True)

Histogram analysis of housing average price in each administrative area

def chart001(self):
    # 柱状图 - 展示各行政区房屋均价

    # 读取清洗后的数据文件
    result_df = self.read_clean_result_file()

    # 从字典对象导入数据
    df = pd.DataFrame(result_df)
    
    # 根据地址分组并计算每平米的平均价
    mean_data = df.groupby(['地址'], as_index=False)['每平方价格'].agg({'每平方价格': 'mean'}).round()

    # 取出地址列数据
    address = mean_data['地址'].unique()

    # 取出每平方价格列数据
    price = mean_data['每平方价格'].unique()

    # 定义表格顶部title
    title = "各行政区房屋均价展示"

    # 定义表格顶部说明
    text = self.top_text

    # 定义图表title
    chart_title = '各行政区房屋均价展示'

    # 定义图表横坐标
    chart_x = '所在地区'

    # 定义图表纵坐标
    chart_y = '房屋均价'

    # 定义图表单位
    unit = '元'

    # 定义图表类型
    types = 'bar'

    # 组装图表json数据
    data_json = {
        'data1': address.tolist(),
        'data2': price.tolist(),
        'title': title,
        'text': text,
        'chartTitle': chart_title,
        'chartX': chart_x,
        'chartY': chart_y,
        'unit': unit,
        'type': types
    }

    #  将json数据写入文件
    with open('../data/chart001.json', 'w', encoding='utf-8') as write_f:  # 打开本地文件

        json.dump(data_json, write_f, indent=4, ensure_ascii=False)   # python对象转换成json对象

echarts rendering histogram

function drawCharts(data) {
    var optionMap = {
        title: {
            text: data.chartTitle,
            left: 'center',
        },
        tooltip: {
            trigger: 'axis',
            axisPointer: {
                type: 'shadow'
            },
        },
        toolbox: {
            feature: {
                saveAsImage: {
                    title: ''
                }
            }
        },
        xAxis: {
            type: 'category',
            name: data.chartX,
            data: data.data1
        },
        yAxis: {
            type: 'value',
            name: data.chartY,
            axisLabel: {
                formatter: '{value} ' + data.unit
            }
        },
        series: [{
            data: data.data2,
            type: data.type
        }]
    };
    //初始化echarts实例
    var myChart = echarts.init(document.getElementById('dom1'));

    //使用制定的配置项和数据显示图表
    myChart.setOption(optionMap);

}

Pie chart of the proportion of houses in each area range

def chart006(self):
    # 饼状图 - 南京各面积区间房屋数量占比图函数
    
    # 读取清洗后的数据文件
    result_df = self.read_clean_result_file()

    # 从字典对象导入数据
    df = pd.DataFrame(result_df)

    # 定义各面积区间
    area_level = [0, 50, 100, 150, 200, 250, 300, 350, 400, 1500]

    # 标识分后bins
    label_level = ['小于50', '50-100', '100-150', '150-200', '200-250', '250-300', '300-350', '350-400', '大于400']

    # 数据从最大值到最小值进行等距划分
    area_cut = pd.cut(df['建筑面积'], area_level, labels=label_level)

    # 获取等距划分后的数据
    df_area = area_cut.value_counts()

    # 获取各区间数据
    area = df_area.index.tolist()

    # 获取各区间房屋数量
    count = df_area.values.tolist()

    # 拼接图表所需数据
    arr = []
    for k, i in enumerate(area):
        arr.append({'name': i, 'value': count[k]})

    # 定义表格顶部title
    title = "面积区间房屋数量占比图"

    # 定义表格顶部说明
    text = self.top_text

    # 定义图表标题
    chart_title = '各面积区间房屋数量占比图'

    # 定义图表名称
    chart_name = '各面积区间房屋数量占比图'

    # 组装图表json数据
    data_json = {
        'data1': arr,
        'title': title,
        'text': text,
        'chartTitle': chart_title,
        'chartName': chart_name,
    }

    #  将json数据写入文件
    with open('../data/chart006.json', 'w', encoding='utf-8') as write_f:  # 打开本地文件

        json.dump(data_json, write_f, indent=4, ensure_ascii=False)  # python对象转换成json对象

echarts rendering pie chart

function drawCharts(data) {
    var optionMap = {
        title: {
            text: data.chartTitle,
            left: 'center',
        },
        tooltip: {
            trigger: 'item'
        },
        toolbox: {
            feature: {
                saveAsImage: {
                    title: ''
                }
            }
        },
        legend: {
            orient: 'vertical',
            left: 'left'
        },
        series: [{
            name: data.chartName,
            type: 'pie',
            radius: '50%',
            data: data.data1,
            itemStyle: {
                normal: {
                    label: {
                        show: true,
                        formatter: '{b} : {d}%'
                    },
                    labelLine: {
                        show: true
                    }
                }
            },
            emphasis: {
                itemStyle: {
                    shadowBlur: 10,
                    shadowOffsetX: 0,
                    shadowColor: 'rgba(0, 0, 0, 0.5)'
                }
            }
        }]
    };
    //初始化echarts实例
    var myChart = echarts.init(document.getElementById('dom1'));

    //使用制定的配置项和数据显示图表
    myChart.setOption(optionMap);

}