Flsk&pyecharts 动态数据可视化

Posted dgwblog

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了Flsk&pyecharts 动态数据可视化相关的知识,希望对你有一定的参考价值。

1:数据源

Hollywood Movie Dataset: 好莱坞2006-2011数据集 

实验目的: 实现 统计2006-2011的数据综合统计情况,进行数据可视化

gitee地址:  https://gitee.com/dgwcode/an_example_of_py_learning/tree/master/MovieViwer

1.数据例子:

Film ,Major Studio,Budget
300,Warner Bros,
300,Warner Bros.,65
3:10 to Yuma,Lionsgate,48
30 Days of Night,Independent,32
Across the Universe,Independent,45
Alien vs. Predator -- Requiem,Fox,40
Alvin and the Chipmunks,Fox,70
American Gangster,Universal,10
Bee Movie,Paramount,15
Beowulf,Paramount,15
Blades of Glory,Paramount,61

技术图片

 

 2: 环境

pycharm新建Flask项目

技术图片

 

 

 技术图片

 

 3 数据处理:

Film ,Major Studio,Budget 为数据的三个标题 截断这三个数据就行

import pandas as pd
from threading import Timer
import math


# coding=utf-8
def getTotalData():
    data1 = pd.read_csv(static/1.csv);
    data2 = pd.read_csv(static/2.csv);
    data3 = pd.read_csv(static/3.csv);
    data4 = pd.read_csv(static/4.csv);
    data5 = pd.read_csv(static/5.csv);
    datadic1 = [];
    datadic2 = [];
    datadic3 = [];
    datadic4 = [];
    datadic5 = [];
    # 处理数据.csv
    for x, y in zip(data1[Major Studio], data1[Budget]):
        datadic1.append((x, y))
    for x, y in zip(data2[Major Studio], data2[Budget]):
        datadic2.append((x, y))
    for x, y in zip(data3[Lead Studio], data3[Budget]):
        datadic3.append((x, y))
    for x, y in zip(data4[Lead Studio], data4[Budget]):
        datadic4.append((x, y))
    for x, y in zip(data5[Lead Studio], data5[Budget]):
        datadic5.append((x, y))
    totaldata = [];
    totaldata.append(datadic1);
    totaldata.append(datadic2);
    totaldata.append(datadic3);
    totaldata.append(datadic4);
    totaldata.append(datadic5);
    return totaldata;


indexx = 0;
curindex = 0;
end = 5;
returnData = dict();


# 定时处理数据
def dataPre():
    global indexx, end, curindex, flag, returnData;
    totalData = getTotalData();  # list[map]
    # x = len(totalData[0]) + totalData[1].len() + totalData[2].len() + totalData[3].len() + totalData[4].len();
    data = totalData[indexx];
    # init
    # print(curindex, end, indexx)
    # print(len(data))
    for k, v in data[curindex:end]:
        if v == "nan" or math.isnan(v):# 截断 k v中 nan
            continue;
        if returnData.get(k, -1) == -1:
            print(k, v);
            returnData[k] = v;
        else:
            returnData[k] = returnData[k] + v;
    print(len(returnData))
    if end < len(data) - 20:
        curindex = end;
        end = end + 20;
    if end >= len(data) - 20:
        indexx += 1;
        curindex = 0;
        end = 20;
    t = Timer(2, dataPre)
    t.start()
    print(returnData.keys(), end=
)
    return returnData;


if __name__ == "__main__":
    dataPre();

 




4:实际程序入口

from flask import Flask, render_template
from pyecharts.charts import Bar
from pyecharts import options as opts
import math
import dealdata
from threading import Timer
from pyecharts.globals import ThemeType


app = Flask(__name__, static_folder="templates")


@app.route(/)
def hello_world():
    dataPre();# 数据入口
    return render_template("index.html")

# 定义全局索引
indexx = 0;
curindex = 0;
end = 5;
returnData = dict();


# 定时处理数据
def dataPre():
    global indexx, end, curindex, flag, returnData;
    totalData = dealdata.getTotalData();  # list[map]
    # x = len(totalData[0]) + totalData[1].len() + totalData[2].len() + totalData[3].len() + totalData[4].len();
    data = totalData[indexx];
    #print(totalData)
    # init
    # print(curindex, end, indexx)
    # print(len(data))
    for k, v in data[curindex:end]:
        if v == "nan" or math.isnan(v):  # 截断 k v中 nan
            continue;
        if returnData.get(k, -1) == -1:
            returnData[k] = v;
        else:
            returnData[k] = returnData[k] + v;
    print(len(returnData)) # 反应长度关系
    if end < len(data) - 15: # 参数为截断的项数 与前端时间要对应
        curindex = end;
        end = end + 15;
    if end >= len(data) - 15:
        indexx += 1;
        curindex = 0;
        end = 15;
    t = Timer(1, dataPre)
    t.start()
    #print(returnData, end=‘
‘)



def bar_reversal_axis() -> Bar:
    global returnData;
    #print(sorted(returnData.items(), key=lambda x: x[1]))
    sorted(returnData.items(), key=lambda x: x[1],reverse=False)
    #print(returnData.keys())
    c = (
        Bar({"theme": ThemeType.MACARONS})
            .add_xaxis(list(returnData.keys()))
            .add_yaxis("电影公司名称:",list(returnData.values()),color="#BF3EFF")
            .reversal_axis()
            .set_series_opts(label_opts=opts.LabelOpts(position="right",color="#BF3EFF",
                                                       font_size=12))
            .set_global_opts(title_opts=opts.TitleOpts(title="2007-2011好莱坞电影最受欢迎公司",
                                                      pos_left=60%,subtitle="当前"+str(2006+indexx)+""))

    )
    return c;


@app.route("/barChart")
def index():
    c = bar_reversal_axis();
    return c.dump_options_with_quotes();

if __name__ == __main__:
    app.run();

5: 前端

<html>
<head>
  <meta charset="UTF-8">
  <title>Awesome-pyecharts</title>
  <script src="https://cdn.bootcss.com/jquery/3.0.0/jquery.min.js"></script>
  <script type="text/javascript" src="https://assets.pyecharts.org/assets/echarts.min.js"></script>
    <style>
        div{
            padding-left: 100px;
        }
    </style>

</head>
<body>
  <div id="bar" style="width:1024px; height:1024px;"></div>
  <script>
    var chart = echarts.init(document.getElementById(bar), white, {renderer: canvas});
    $(
      function () {
        fetchData(chart);
        setInterval(fetchData, 500);
      }
    );
    function fetchData() {
      $.ajax({
        type: "GET",
        url: "http://127.0.0.1:5000/barChart",
        dataType: json,
        success: function (result) {
          chart.setOption(result);
        }
      });
    }
  </script>
</body>
</html>

 

 

6: 扩展资料

 

https://github.com/pyecharts/pyecharts/tree/master/pyecharts/render/templates

 

技术图片

 

 

{% import macro as macro %}
<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>{{ chart.page_title }}</title>
    {{ macro.render_chart_dependencies(chart) }}
</head>
<body>
    <div id="{{ chart.chart_id }}" style="width:{{ chart.width }}; height:{{ chart.height }};"></div>
    <script>
        var canvas_{{ chart.chart_id }} = document.createElement(canvas);
        var mapChart_{{ chart.chart_id }} = echarts.init(
             canvas_{{ chart.chart_id }}, {{ chart.theme }}, {width: 4096, height: 2048, renderer: {{ chart.renderer }}});
        {% for js in chart.js_functions.items %}
            {{ js }}
        {% endfor %}
        var mapOption_{{ chart.chart_id }} = {{ chart.json_contents }};
        mapChart_{{ chart.chart_id }}.setOption(mapOption_{{ chart.chart_id }});
        var chart_{{ chart.chart_id }} = echarts.init(
        document.getElementById({{ chart.chart_id }}), {{ chart.theme }}, {renderer: {{ chart.renderer }}});
        var options_{{ chart.chart_id }} = {
           "globe": {
           "show": true,
           "baseTexture": mapChart_{{ chart.chart_id }},
           shading: lambert,
            light: {
                ambient: {
                    intensity: 0.6
                },
                main: {
                    intensity: 0.2
                }
             }
           }};
        chart_{{ chart.chart_id }}.setOption(options_{{ chart.chart_id }});
    </script>
</body>
</html>

 


















 

 

以上是关于Flsk&pyecharts 动态数据可视化的主要内容,如果未能解决你的问题,请参考以下文章

爬取疫情数据,以django+pyecharts实现数据可视化web网页

教你轻松实现炫酷的动态数据可视化

利用 Flask 动态展示 Pyecharts 图表数据的几种方法

利用 Flask 动态展示 Pyecharts 图表数据的几种方法

Pythonpyecharts 数据可视化模块

Pythonpyecharts 数据可视化模块