ECharts是一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。ECharts最初由百度团队开源，并于2018年初捐赠给Apache基金会，成为ASF孵化级项目。
ECharts官网：Apache ECharts
PyEcharts 是一个用于生成 Echarts 图表的类库。 Python 是一门富有表达力的语言，很适合用于数据处理。当数据分析遇上数据可视化时，PyEcharts 诞生了。
目录
一、安装
二、操作
1、折线图（柱状图、散点图适用）
2、饼状图、环状图
3、关系图
4、词云
5、地图
6、雷达图
一、安装
pip install pyecharts二、操作
from pyecharts import options # 图形样式编辑
from pyecharts.charts import Graph/Bar/Line/... # 引入不同图（关系图、柱状图、折线图...）1、折线图（柱状图、散点图适用）
from pyecharts import options
from pyecharts.charts import Line/Bar/Scatter
def draw(xlist, ylist):
line = Line()
line.add_xaxis(xlist) # x坐标名称
line.add_yaxis("线名称", ylist, is_smooth=True) # 线名称、各点数值、是否平滑
line.set_global_opts(title_opts=options.TitleOpts(title='name')) # 表名称
line.render('xxx.html') # 生成html文件
def main():
xlist = ['一', '二', '三', '四', '五']
ylist = [3, 9, 15, 21, 27]
draw(xlist, ylist)
if __name__ == '__main__':
main()
2、饼状图、环状图
from pyecharts import options as opts
from pyecharts.charts import Pie
def draw(blist, nlist):
pie = Pie()
# 各部分名称，值；添加radius（内环大小、外环大小）即为环状
pie.add("", [list(i) for i in zip(nlist, blist)], radius=["40%", "75%"])
# 表名称
pie.set_global_opts(title_opts=opts.TitleOpts(title="name"))
# 显示名称及值
pie.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
# 生成html文件
pie.render('xxx.html')
def main():
nlist = ['一', '二', '三', '四', '五']
blist = [3, 9, 15, 21, 27]
draw(blist, nlist)
if __name__ == '__main__':
main()
3、关系图
from pyecharts import options as opts
from pyecharts.charts import Graph
def draw(alist, value):
nodes = []
for i in range(5):
nodes.append({'name': alist[i], 'value': value[i]}) # 设置节点名称及对应值（字典格式）
links = []
for i in nodes[2:]:
links.append(opts.GraphLink(source='A', target=i.get('name')))
for i in nodes[2:]:
links.append(opts.GraphLink(source='B', target=i.get('name'))) # 连线，指定连线的起始和目标节点
graph = Graph()
graph.add("", nodes, links, edge_symbol=['circle', 'arrow'], repulsion=200, is_draggable=True, layout='force') # repulsion节点间排斥力，is_draggable是否可拖拽节点，layout布局方式（有circular环形布局，force力引导布局，none无）
graph.set_global_opts(title_opts=opts.TitleOpts(title="name")) # 表名称
graph.render('xxx.html')
def main():
value = [0, 0, 5, 32, 45]
alist = ['A', 'B', 'c', 'd', 'e']
draw(alist, value)
if __name__ == '__main__':
main()
4、词云
from pyecharts.charts import WordCloud
data = [('天气', 23), ('美丽', 10), ('天空', 5) ,('飞鸟', 15) , ('如诗如画', 25) , ('心情', 5), ('哈哈', 17)]
#面向对象方式
cloudObj = WordCloud()
# shap默认为cicrle，其他形状有circle, cardioid, diamond, triangle-forward, triangle, pentagon, star
cloudObj.add('', data, shape='circle')
cloudObj.render('xxx.html')
# shap可更换为图片，但尝试后效果不好。图片存在bug，需刷新网页后才会显示。
# cloudObj.add('', data, mask_image='C:\XXX.png')
5、地图
（1）基础地图
from pyecharts.charts import Map
from pyecharts import options as opts
def map(provinces, value):
map = Map()
# china可改为world或各省市名称
map.add("", [list(i) for i in zip(provinces, value)], "china")
# 表名称；max_为最大值，超过此值都为最大值颜色;split_number,is_piecewise可以将颜色分块显示
map.set_global_opts(title_opts=opts.TitleOpts(title="title"), visualmap_opts=opts.VisualMapOpts(max_=5000, split_number=8, is_piecewise=True))
map.render('xxx.html')
def main():
provinces = ["广东", "北京", "上海", "辽宁", "湖南", "四川", "西藏"]
value = [300, 100, 2000, 800, 10000, 400, 5000]
map(provinces, value)
if __name__ == '__main__':
main()
（2）热力地图
from pyecharts.charts import Geo
from pyecharts import options as opts
from pyecharts.globals import ChartType
def map(provinces, value):
map = Geo()
# 仅以下两行与基础地图不同
map.add_schema(maptype="china")
map.add("", [list(i) for i in zip(provinces, value)], type_=ChartType.HEATMAP)
map.set_global_opts(title_opts=opts.TitleOpts(title="title"),
visualmap_opts=opts.VisualMapOpts(max_=5000))
map.render('xxx.html')
def main():
provinces = ["广东", "北京", "上海", "辽宁", "湖南", "四川", "西藏"]
value = [300, 100, 2000, 800, 10000, 400, 5000]
map(provinces, value)
if __name__ == '__main__':
main()
6、雷达图
from pyecharts.charts import Radar
from pyecharts import options as opts
def radar(v1):
radar = Radar(init_opts=opts.InitOpts())
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name="传球", max_=100),
opts.RadarIndicatorItem(name="射门", max_=100),
opts.RadarIndicatorItem(name="身体", max_=100),
opts.RadarIndicatorItem(name="防守", max_=100),
opts.RadarIndicatorItem(name="速度", max_=100),
opts.RadarIndicatorItem(name="盘带", max_=100),
],
splitarea_opt=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)), # 是否显示分隔区域，透明度为1
textstyle_opts=opts.TextStyleOpts(color="#000000")
)
radar.add(series_name="C·罗纳尔多", data=v1, areastyle_opts=opts.AreaStyleOpts(color="#FF0000", opacity=0.2)) # 区域面积，透明度
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title="标准球员属性雷达图"), legend_opts=opts.LegendOpts(selected_mode="single")) # 可以设置单一选择查看
radar.render('xxx.html')
def main():
v1 = [[83, 92, 87, 49, 89, 86]]
radar(v1)
if __name__ == '__main__':
main()
7、树形图
from pyecharts.charts import Tree
from pyecharts import options as opts
def draw(data):
tree = Tree()
tree.add('', data)
tree.set_global_opts(title_opts=opts.TitleOpts(title="title"))
tree.render('xxx.html')
def main():
# children数据类型为列表；可以为每个节点写入数值（value）
data = [
{"children": [{"name": "B", "value": "1"},
{"children": [{"children": [{"name": "I"}], "name": "E"}, {"name": "F"}],
"name": "C"},
{"children": [{"children": [{"name": "J"}, {"name": "K"}], "name": "G"}, {"name": "H"}],
"name": "D"}],
"name": "A"}
]
draw(data)
if __name__ == '__main__':
main()
8、 主题河流图
from pyecharts import options as opts
from pyecharts.charts import ThemeRiver
def draw(data, data_list):
tr = ThemeRiver()
# series_name各数据名称；data数据数值；SingleAxisOpts河流图位置
tr.add(series_name=data_list, data=data,
singleaxis_opts=opts.SingleAxisOpts(pos_top="50", pos_bottom="50", type_="time"))
# TooltipOpts设置辅助线；LegendOpts设置图例位置
tr.set_global_opts(title_opts=opts.TitleOpts(title="title"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
legend_opts=opts.LegendOpts(pos_top=0))
tr.render('xxx.html')
def main():
data = [['2015/11/08', 35, 'TY'],
['2015/11/09', 36, 'TY'],
['2015/11/10', 37, 'TY'],
['2015/11/11', 22, 'TY'],
['2015/11/12', 24, 'TY'],
['2015/11/13', 26, 'TY'],
['2015/11/14', 34, 'TY'],
['2015/11/15', 21, 'TY'],
['2015/11/16', 18, 'TY'],
['2015/11/17', 45, 'TY'],
['2015/11/18', 32, 'TY'],
['2015/11/19', 35, 'TY'],
['2015/11/20', 30, 'TY'],
['2015/11/21', 28, 'TY'],
['2015/11/22', 27, 'TY'],
['2015/11/23', 26, 'TY'],
['2015/11/24', 15, 'TY'],
['2015/11/25', 30, 'TY'],
['2015/11/26', 35, 'TY'],
['2015/11/27', 42, 'TY'],
['2015/11/28', 42, 'TY'],
['2015/11/08', 21, 'SS'],
['2015/11/09', 25, 'SS'],
['2015/11/10', 27, 'SS'],
['2015/11/11', 23, 'SS'],
['2015/11/12', 24, 'SS'],
['2015/11/13', 21, 'SS'],
['2015/11/14', 35, 'SS'],
['2015/11/15', 39, 'SS'],
['2015/11/16', 40, 'SS'],
['2015/11/17', 36, 'SS'],
['2015/11/18', 33, 'SS'],
['2015/11/19', 43, 'SS'],
['2015/11/20', 40, 'SS'],
['2015/11/21', 34, 'SS'],
['2015/11/22', 28, 'SS'],
['2015/11/23', 26, 'SS'],
['2015/11/24', 37, 'SS'],
['2015/11/25', 41, 'SS'],
['2015/11/26', 46, 'SS'],
['2015/11/27', 47, 'SS'],
['2015/11/28', 41, 'SS'],
['2015/11/08', 10, 'QG'],
['2015/11/09', 15, 'QG'],
['2015/11/10', 35, 'QG'],
['2015/11/11', 38, 'QG'],
['2015/11/12', 22, 'QG'],
['2015/11/13', 16, 'QG'],
['2015/11/14', 7, 'QG'],
['2015/11/15', 2, 'QG'],
['2015/11/16', 17, 'QG'],
['2015/11/17', 33, 'QG'],
['2015/11/18', 40, 'QG'],
['2015/11/19', 32, 'QG'],
['2015/11/20', 26, 'QG'],
['2015/11/21', 35, 'QG'],
['2015/11/22', 40, 'QG'],
['2015/11/23', 32, 'QG'],
['2015/11/24', 26, 'QG'],
['2015/11/25', 22, 'QG'],
['2015/11/26', 16, 'QG'],
['2015/11/27', 22, 'QG'],
['2015/11/28', 10, 'QG'],
['2015/11/08', 10, 'DD'],
['2015/11/09', 15, 'DD'],
['2015/11/10', 35, 'DD'],
['2015/11/11', 38, 'DD'],
['2015/11/12', 22, 'DD'],
['2015/11/13', 16, 'DD'],
['2015/11/14', 7, 'DD'],
['2015/11/15', 2, 'DD'],
['2015/11/16', 17, 'DD'],
['2015/11/17', 33, 'DD'],
['2015/11/18', 4, 'DD'],
['2015/11/19', 32, 'DD'],
['2015/11/20', 26, 'DD'],
['2015/11/21', 35, 'DD'],
['2015/11/22', 40, 'DD'],
['2015/11/23', 32, 'DD'],
['2015/11/24', 26, 'DD'],
['2015/11/25', 22, 'DD'],
['2015/11/26', 16, 'DD'],
['2015/11/27', 22, 'DD'],
['2015/11/28', 10, 'DD']]
data_list = ['TY', 'SS', 'QG', 'DD']
draw(data, data_list)
if __name__ == '__main__':
main()
9、 折柱混合图
from pyecharts.charts import Bar, Line
from pyecharts import options as opts
x = ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']
a = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
b = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
bar = Bar()
bar.add_xaxis(xaxis_data=x)
bar.add_yaxis(series_name="蒸发量", y_axis=a, label_opts=opts.LabelOpts(is_show=False))
# 增加一个y轴用于折线图的数据展示
# splitline_opts背景辅助线
bar.extend_axis(yaxis=opts.AxisOpts(type_="value", min_=0, max_=25, interval=5,
axislabel_opts=opts.LabelOpts(formatter="{value}°C"),
splitline_opts=opts.SplitLineOpts(is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color='#d14a61'))
)
)
# 左侧y轴及全局设置
bar.set_global_opts(title_opts=opts.TitleOpts(title="2020年每月天气概况"),
tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), # 显示提示框、鼠标辅助线
xaxis_opts=opts.AxisOpts(name_location="middle", name_gap=30),
yaxis_opts=opts.AxisOpts(type_="value", min_=0, max_=210, interval=42,
axislabel_opts=opts.LabelOpts(formatter="{value}ml"),
axistick_opts=opts.AxisTickOpts(is_show=True),
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color='#5793f3'))
)
)
# 折线图
line = Line()
line.add_xaxis(xaxis_data=x)
# z大于0可以使折现位于柱状上方
line.add_yaxis(series_name="平均温度", yaxis_index=1, y_axis=b, label_opts=opts.LabelOpts(is_show=True), z=10)
# 将两图写入一个坐标系中
bar.overlap(line)
bar.render('xxx.html')
10、 工具（补充）
（1）添加区域缩放
'''
以折线图为例
'''
line.set_global_opts(
title_opts=opts.TitleOpts(title='name'),
datazoom_opts=opts.DataZoomOpts(
is_show=True
, range_start=0 # 默认开始位置（最小0）
, range_end=100 # 默认结束位置（最大100）
, orient='horizontal' # 横向horizontal和纵向vertical
)
)
（2）添加工具栏
'''
以折线图为例
'''
line.set_global_opts(toolbox_opts=opts.ToolboxOpts(is_show=True))
（3）标记最大最小值
'''
以折线图为例
'''
line.set_series_opts(markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值")]))
（4）平均值标线
'''
以折线图为例
'''
line.add_yaxis(
"线名称", ylist, is_smooth=True,
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_='average', name='平均值')]
, linestyle_opts=opts.LineStyleOpts(
type_='dashed' # 点状
, opacity=0.5 # 透明度0-1值越小越透明
, color='black'
)
)
)
（5）保存为其他格式到本地
'''
已折线图为例，除备注两行外其他代码与之前相同（注意需要引入两个库）
'''
from pyecharts import options
from pyecharts.charts import Line
from snapshot_selenium import snapshot
from pyecharts.render import make_snapshot
def draw(xlist, ylist):
line = Line(init_opts=options.InitOpts(bg_color='#ffffff')) # 背景色更改为白色，默认为透明
line.add_xaxis(xlist)
line.add_yaxis("线名称", ylist, is_smooth=True)
line.set_global_opts(title_opts=options.TitleOpts(title='name'))
make_snapshot(snapshot, line.render(), "xxx.png") # 生成png格式文件
def main():
xlist = ['一', '二', '三', '四', '五']
ylist = [3, 9, 15, 21, 27]
draw(xlist, ylist)
if __name__ == '__main__':
main()
