这篇文章主要介绍了使用pyecharts1.7进行简单的可视化大全,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
近期，又有接触到pyecharts这个包的使用，后面发现这个曾经好用的包发生了一些变化，为了方便大家的使用，这里整理如下：
绘图风格theme:默认WHITE
LIGHT, DARK, WHITE, CHALK, ESSOS, INFOGRAPHIC, MACARONS, PURPLE_PASSION, ROMA, ROMANTIC, SHINE, VINTAGE, WALDEN, WESTEROS, WONDERLAND
1.柱状图绘制
1.1 最基础的柱状图
from pyecharts.charts import Bar,Grid from pyecharts import options as opts from pyecharts.globals import ThemeType import random import numpy as np # 准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] #绘图 bar=Bar(init_opts = opts.InitOpts(width='600px',height='400px')) bar.add_xaxis(name) bar.add_yaxis("salery",salery) bar.set_global_opts(title_opts=opts.TitleOpts(title="收入情况")) #仅在notebook中显示 bar.render_notebook() #在HTML中显示 bar.render("收入情况")
效果图：
1.2 稍微复杂的柱状图
为了减少代码量，此处不再导入包。绘制收入和消费情况，并使用新风格,并添加副标题，使用新版本的链式写法。
#准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] cost=[random.randint(1000,2000) for i in range(4)] #绘图 bar=( Bar(init_opts = opts.InitOpts(width='600px',height='400px',theme=ThemeType.LIGHT)) .add_xaxis(name) .add_yaxis("salery",salery) .add_yaxis("cost",cost) .set_global_opts(title_opts=opts.TitleOpts(title="收入及消费情况",subtitle="随机样本")) ) bar.render_notebook()
#效果图：
1.3 堆叠式柱状图
使用堆叠式柱状图（部分堆叠），并自定义颜色，修改图例的显示位置，不显示数字，改变背景颜色
#准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] cost=[random.randint(1000,2000) for i in range(4)] #所在城市平均薪水 salery_ave=[random.randint(3000,4000) for i in range(4)] colors=["#007892","#ff427f","#fc8210","#ffd8a6"] #进行绘图 bar=( Bar(init_opts = opts.InitOpts(width='600px',height='400px',bg_color=colors[-1])) .add_xaxis(name) .add_yaxis("salery",salery,stack="stack_one") .add_yaxis("cost",cost,stack="stack_one") .add_yaxis("salery_ave",salery_ave) .set_colors(colors) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts(title_opts=opts.TitleOpts(title="收入、消费及其城市平均收入情况"), legend_opts=opts.LegendOpts(type_="scroll", pos_right="right", orient="vertical") ) ) bar.render_notebook()
效果展示：
1.3.1 调整标题与图的位置
grid=Grid() # 分别调整上下左右的位置，参数为像素值或百分比 grid.add(bar,grid_opts=opts.GridOpts(pos_top="30%",pos_bottom="10%",pos_left="10%",pos_right="10%")) grid.render_notebook()
效果演示
1.4 绘制簇状图
#准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] cost=[random.randint(1000,2000) for i in range(4)] #所在城市平均薪水 salery_ave=[random.randint(3000,4000) for i in range(4)] colors=["#007892","#ff427f","#fc8210","#ffd8a6"] #进行绘图 bar=( Bar(init_opts = opts.InitOpts(width='600px',height='400px',bg_color=colors[-1])) .add_xaxis(name) .add_yaxis("salery",salery) .add_yaxis("salery_ave",salery_ave) .reversal_axis() .set_colors(colors) .set_series_opts(label_opts=opts.LabelOpts(position="right")) .set_global_opts(title_opts=opts.TitleOpts(title="收入、消费及其城市平均收入情况"), legend_opts=opts.LegendOpts(type_="scroll", pos_right="right", orient="vertical") ) ) bar.render_notebook()
效果图演示
1.5 数据量大时的显示方法
#准备数据 name=[chr(i) for i in range(65,85,1)] salery=[random.randint(3000,5000) for i in range(20)] #所在城市平均薪水 salery_ave=[random.randint(3000,4000) for i in range(20)] colors=["#007892","#ff427f","#fc8210","#ffd8a6"] #绘图 修改 orient为vertical，可将滑动按钮移动垂直方向 bar=( Bar(init_opts = opts.InitOpts(width='600px',height='400px',bg_color=colors[-1])) .add_xaxis(name) .add_yaxis("salery",salery) .add_yaxis("salery_ave",salery_ave) .set_colors(colors) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts(title_opts=opts.TitleOpts(title="收入、消费及其城市平均收入情况"), legend_opts=opts.LegendOpts(type_="scroll", pos_right="right", orient="vertical"), datazoom_opts=[opts.DataZoomOpts(type_="slider")] ) ) bar.render_notebook()
演示
2.绘制散点图
2.1 普通散点图
import random from pyecharts import options as opts from pyecharts.charts import Scatter from pyecharts.globals import ThemeType #准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] cost=[random.randint(1000,2000) for i in range(4)] #所在城市平均薪水 salery_ave=[random.randint(3000,4000) for i in range(4)] colors=["#007892","#ff427f","#fc8210","#ffd8a6"] #进行绘图 scatter=(Scatter(init_opts = opts.InitOpts(width='600px',height='400px',theme=ThemeType.DARK)) .add_xaxis(name) .add_yaxis("salery",salery) .add_yaxis("cost",cost) .set_global_opts(title_opts=opts.TitleOpts(title="收入与消费情况"))) scatter.render_notebook()
查看
2.2 3D散点图绘制
import random from pyecharts import options as opts from pyecharts.charts import Scatter3D from pyecharts.faker import Faker #准备数据 data = [(random.randint(0,100),random.randint(0,100),random.randint(0,1<i style="color:transparent">来源gaodai$ma#com搞$代*码网</i>00)) for i in range(50)] name=["长","宽","高"] #绘图 scatter3D=Scatter3D(init_opts = opts.InitOpts(width='600px',height='400px')) #初始化 scatter3D.add(name,data, grid3d_opts=opts.Grid3DOpts( width=100, depth=100 )) scatter3D.set_global_opts(title_opts=opts.TitleOpts(title="散点图"), visualmap_opts=opts.VisualMapOpts( range_color=Faker.visual_color #颜色映射 )) scatter3D.render_notebook()
效果图：
2.3 带涟漪的散点图
symbol的类型：
“pin”,“rect”,“roundRect”,“diamond”,“arrow”,“triangle”
import random from pyecharts import options as opts from pyecharts.charts import EffectScatter from pyecharts.globals import ThemeType #准备数据 name=["A","B","C","D"] salery=[random.randint(3000,5000) for i in range(4)] cost=[random.randint(1000,2000) for i in range(4)] #所在城市平均薪水 salery_ave=[random.randint(3000,4000) for i in range(4)] colors=["#007892","#ff427f","#fc8210","#ffd8a6"] #进行绘图 scatter=(EffectScatter(init_opts = opts.InitOpts(width='600px',height='400px',theme=ThemeType.DARK)) .add_xaxis(name) .add_yaxis("salery",salery,symbol="pin",symbol_size=20,symbol_rotate=180) .add_yaxis("cost",cost,symbol="rect",symbol_size=20) .set_global_opts(title_opts=opts.TitleOpts(title="收入与消费情况"), xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)), #添加网格 yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)) ) .set_series_opts(effect_opts=opts.EffectOpts(scale=3,period=2)) #调整涟漪的范围和周期 ) scatter.render_notebook()
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