Posted in Python onFebruary 26, 2020
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 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
2: 环境pycharm新建Flask项目
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='\n') 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='\n') 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)+"年")) ) 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
{% 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>
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Flask和pyecharts实现动态数据可视化
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