# 0 相关源码
1 数据可视化的作用及常用方法
1.1 为什么要数据可视化
1.1.1 何为数据可视化?
◆ 将数据以图形图像的形式展现出来
◆ 人类可以对三维及以下的数据产生直观的感受
1.1.2 数据可视化的好处
◆ 便于人们发现与理解数据蕴含的信息
◆ 便于人们进行讨论
1.2 数据可视化的常用方法
◆ 对于web应用,一般使用echarts,hightcharts,d3.js等
◆ 对于数据分析利器python , 使用matplotlib等可视化库
◆ 对于非码农的数据分析员, 一般使用excel等
2 初识Echarts
◆ echarts是由百度开源的JS数据可视化库,底层依赖ZRender渲染
◆ 虽然该项目并不能称为最优秀的可视化库,但是在国内市场占有率很高,故本教程选择echarts.
◆ echarts 提供的图表很丰富 ,我们只需使用其中几个即可
2.1 学习使用echarts绘图
◆ 我们将通过官网的文档,共同学习echarts使用的基本方法
◆ 使用流程:
- 定义网页结构
- 声明DOM
- 填充并解析数据
- 渲染数据
◆ 我们主要学习的图表有折线图、条形图、散点图等
3 通过Echarts实现图表化数据展示
3.1 实现一个echarts图表的例子
简单线形图
- 替换为年份数据
- 替换为降雨量数据
柱状图动画延迟
var xAxisData = [2009,2007,2006,2005,2004,2003,2002,2001,2000,1999,1998,1997,1996,1995,1994,1993,1992,1991,1990,1989,1988,1987,1986,1985,1984,1983,1982,1981,1980,1979,1978,1977,1976,1975,1974,1973,1972,1971,1970,1969,1968,1967,1966,1965,1964,1963,1962,1961,1960,1959,1958,1957,1956,1955,1954,1953,1952,1951,1950,1949];
var data = [0.4806,0.4839,0.318,0.4107,0.4835,0.4445,0.3704,0.3389,0.3711,0.2669,0.7317,0.4309,0.7009,0.5725,0.8132,0.5067,0.5415,0.7479,0.6973,0.4422,0.6733,0.6839,0.6653,0.721,0.4888,0.4899,0.5444,0.3932,0.3807,0.7184,0.6648,0.779,0.684,0.3928,0.4747,0.6982,0.3742,0.5112,0.597,0.9132,0.3867,0.5934,0.5279,0.2618,0.8177,0.7756,0.3669,0.5998,0.5271,1.406,0.6919,0.4868,1.1157,0.9332,0.9614,0.6577,0.5573,0.4816,0.9109,0.921];
option = {
title: {
text: '柱状图动画延迟'
},
legend: {
data: ['beijing'],
align: 'left'
},
toolbox: {
// y: 'bottom',
feature: {
magicType: {
type: ['stack', 'tiled']
},
dataView: {},
saveAsImage: {
pixelRatio: 2
}
}
},
tooltip: {},
xAxis: {
data: xAxisData,
silent: false,
splitLine: {
show: false
}
},
yAxis: {
},
series: [{
name: 'beijing',
type: 'bar',
data: data,
animationDelay: function (idx) {
return idx * 10;
}
}
],
animationEasing: 'elasticOut',
animationDelayUpdate: function (idx) {
return idx * 5;
}
};
var xAxisData = [2009,2007,2006,2005,2004,2003,2002,2001,2000,1999,1998,1997,1996,1995,1994,1993,1992,1991,1990,1989,1988,1987,1986,1985,1984,1983,1982,1981,1980,1979,1978,1977,1976,1975,1974,1973,1972,1971,1970,1969,1968,1967,1966,1965,1964,1963,1962,1961,1960,1959,1958,1957,1956,1955,1954,1953,1952,1951,1950,1949];
var data = [0.4806,0.4839,0.318,0.4107,0.4835,0.4445,0.3704,0.3389,0.3711,0.2669,0.7317,0.4309,0.7009,0.5725,0.8132,0.5067,0.5415,0.7479,0.6973,0.4422,0.6733,0.6839,0.6653,0.721,0.4888,0.4899,0.5444,0.3932,0.3807,0.7184,0.6648,0.779,0.684,0.3928,0.4747,0.6982,0.3742,0.5112,0.597,0.9132,0.3867,0.5934,0.5279,0.2618,0.8177,0.7756,0.3669,0.5998,0.5271,1.406,0.6919,0.4868,1.1157,0.9332,0.9614,0.6577,0.5573,0.4816,0.9109,0.921];
option = {
title: {
text: '柱状图动画延迟'
},
legend: {
data: ['beijing','shanghai'],
align: 'left'
},
toolbox: {
// y: 'bottom',
feature: {
magicType: {
type: ['stack', 'tiled']
},
dataView: {},
saveAsImage: {
pixelRatio: 2
}
}
},
tooltip: {},
xAxis: {
data: xAxisData,
silent: false,
splitLine: {
show: false
}
},
yAxis: {
},
series: [
{
name: 'beijing',
type: 'bar',
data: data,
animationDelay: function (idx) {
return idx * 10;
}
},
{
name: 'shanghai',
type: 'bar',
data: data,
animationDelay: function (idx) {
return idx * 10;
}
}
],
animationEasing: 'elasticOut',
animationDelayUpdate: function (idx) {
return idx * 5;
}
};
Spark机器学习实践系列
- 基于Spark的机器学习实践 (一) - 初识机器学习
- 基于Spark的机器学习实践 (二) - 初识MLlib
- 基于Spark的机器学习实践 (三) - 实战环境搭建
- [基于Spark的机器学习实践 (四) - 数据可视化
](https://zhuanlan.zhihu.com/p/61868232)