利用Pyecharts绘制15个超实用精美图表~
俊红的数据分析之路
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·
2021-10-21 00:33
什么是pyecharts?pyecharts是Python与ECharts的结合,Python是我们所熟知的语言,而ECharts是百度开源的数据可视化图表设计,这两者的有效结合,使得图表可视化更加绚丽多彩。
本文使用Python语言,借助pyecharts库,绘制常用的柱形图、折线图、饼图、散点图等,使用pyecharts库的交互功能实现动态可视化功能,对于里面的代码都可以修改进行复用,下面一起学习。
条形图
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkLine(自定义)"))
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=50, name="yAxis=50")]
),
)
)
c.render_notebook()
水平条形图动图
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values,itemstyle_opts=opts.ItemStyleOpts(color="#d14a61"))
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom(slider-水平)"),
datazoom_opts=[opts.DataZoomOpts()],
)
)
c.render_notebook()
折线图
from pyecharts.charts import Line
from pyecharts import options as opts
# 示例数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data1 = [123, 153, 89, 107, 98, 23]
data2 = [56, 77, 93, 68, 45, 67]
line = (Line()
.add_xaxis(cate)
.add_yaxis('电商渠道', data1,
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]))
.add_yaxis('门店', data2,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(name="自定义标记点",
coord=[cate[2], data2[2]], value=data2[2])]))
.set_global_opts(title_opts=opts.TitleOpts(title="Line-基本示例", subtitle="我是副标题"))
)
line.render_notebook()
面积图
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
c = (
Line()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
.add_yaxis("商家B", Faker.values(), areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
.set_global_opts(title_opts=opts.TitleOpts(title="Line-面积图"))
)
c.render_notebook()
饼图
from pyecharts.charts import Pie
from pyecharts import options as opts
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [153, 124, 107, 99, 89, 46]
pie = (Pie()
.add('', [list(z) for z in zip(cate, data)],
radius=["30%", "75%"],
rosetype="radius"
)
.set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例", subtitle="我是副标题"))
.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))
)
pie.render_notebook()
玫瑰图
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
v = Faker.choose()
c = (
Pie()
.add(
"",
[list(z) for z in zip(v, Faker.values())],
radius=["30%", "75%"],
center=["25%", "50%"],
rosetype="radius",
label_opts=opts.LabelOpts(is_show=False),
)
.add(
"",
[list(z) for z in zip(v, Faker.values())],
radius=["30%", "75%"],
center=["75%", "50%"],
rosetype="area",
)
.set_global_opts(title_opts=opts.TitleOpts(title="Pie-玫瑰图示例"))
)
c.render_notebook()
漏斗图
import pyecharts.options as opts
from pyecharts.charts import Funnel
x_data = ["展现", "点击", "访问", "咨询", "订单"]
y_data = [100, 80, 60, 40, 20]
data = [[x_data[i], y_data[i]] for i in range(len(x_data))]
(
Funnel()
.add(
series_name="",
sort_='ascending',
data_pair=data,
gap=2,
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a}
{b} : {c}%"),label_opts=opts.LabelOpts(is_show=True, position="inside"),
itemstyle_opts=opts.ItemStyleOpts(border_color="#fff", border_width=1),
)
.set_global_opts(title_opts=opts.TitleOpts(title="漏斗图", subtitle="纯属虚构"))
).render_notebook()
热力图
from pyecharts.charts import HeatMap
from pyecharts import options as opts
from pyecharts.faker import Faker
import random
# 示例数据
data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
heat = (HeatMap()
.add_xaxis(Faker.clock)
.add_yaxis("访客数",
Faker.week,
data,
label_opts=opts.LabelOpts(is_show=True, position="inside"))
.set_global_opts(
title_opts=opts.TitleOpts(title="HeatMap-基本示例", subtitle="我是副标题"),
visualmap_opts=opts.VisualMapOpts(),
legend_opts=opts.LegendOpts(is_show=False))
)
heat.render_notebook()
散点图
from pyecharts import options as opts
from pyecharts.charts import Scatter
from pyecharts.faker import Faker
c = (
Scatter()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Scatter-VisualMap(Size)"),
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
c.render_notebook()
三维散点图
from pyecharts import options as opts
from pyecharts.charts import Scatter3D
import random
data = [[random.randint(0, 150), random.randint(0, 100), random.randint(0, 100)]
for _ in range(1000)]
scatter3D = (Scatter3D()
.add("", data)
.set_global_opts(
title_opts=opts.TitleOpts("Scatter3D-基本示例"),
visualmap_opts=opts.VisualMapOpts(range_color=Faker.visual_color))
)
scatter3D.render_notebook()
箱线图
from pyecharts import options as opts
from pyecharts.charts import Boxplot
v1 = [
[850, 740, 900, 1070, 930, 850, 950, 980, 980, 880, 1000, 980],
[960, 940, 960, 940, 880, 800, 850, 880, 900, 840, 830, 790],
]
v2 = [
[890, 810, 810, 820, 800, 770, 760, 740, 750, 760, 910, 920],
[890, 840, 780, 810, 760, 810, 790, 810, 820, 850, 870, 870],
]
c = Boxplot()
c.add_xaxis(["expr1", "expr2"])
c.add_yaxis("A", c.prepare_data(v1))
c.add_yaxis("B", c.prepare_data(v2))
c.set_global_opts(title_opts=opts.TitleOpts(title="BoxPlot-基本示例"))
c.render_notebook()
地图
from pyecharts import options as opts
from pyecharts.charts import Map
import random
province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西', '河北', '安徽', '河南', '山东', '西藏']
data = [(i, random.randint(50, 150)) for i in province]
_map = (
Map()
.add("销售额", data, "china")
.set_global_opts(
title_opts=opts.TitleOpts(title="Map-基本示例"),
legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True),
)
)
_map.render_notebook()
区域热力图
from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.globals import ChartType
import random
province = ['杭州', '宁波', '温州', '嘉兴', '丽水', '衢州', '绍兴', '台州', '湖州','金华','舟山']
data = [(i, random.randint(50, 150)) for i in province]
geo = (Geo()
.add_schema(maptype="浙江")
.add("门店数", data,
type_=ChartType.HEATMAP)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(),
legend_opts=opts.LegendOpts(is_show=False),
title_opts=opts.TitleOpts(title="Geo-浙江热力地图"))
)
geo.render_notebook()
世界地图
from pyecharts.charts import Map # 注意这里与老版本pyecharts调用的区别
from pyecharts import options as opts
import random
country = ['China', 'Canada', 'France', 'Japan', 'Russia', 'USA']
data_world = [(i, random.randint(100, 200)) for i in country]
world = (
Map()
.add('', # 此处没取名,所以空着
data_world, # 数据
'world') # 地图类型
.set_global_opts(
title_opts=opts.TitleOpts(title='World Map'),
visualmap_opts=opts.VisualMapOpts(
max_=200,
min_=100,
is_piecewise=True) # 定义图例为分段型,默认为连续的图例
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
world.render_notebook()
词云图
import pyecharts.options as opts
from pyecharts.charts import WordCloud
data = [
("生活资源", "999"),
("供热管理", "888"),
("供气质量", "777"),
("生活用水管理", "688"),
("一次供水问题", "588"),
("交通运输", "516"),
("城市交通", "515"),
("环境保护", "683"),
("房地产管理", "662"),
("城乡建设", "649"),
("社会保障与福利", "429"),
("社会保障", "707"),
("文体与教育管理", "506"),
("公共安全", "506"),
("公交运输管理", "586"),
("出租车运营管理", "585"),
]
(
WordCloud()
.add(series_name="热点分析", data_pair=data, word_size_range=[6, 66])
.set_global_opts(
title_opts=opts.TitleOpts(
title="热点分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)
),
tooltip_opts=opts.TooltipOpts(is_show=True),
)
).render_notebook()
组合图
from pyecharts import options as opts
from pyecharts.charts import Bar,Line,Grid
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
x_data = ["{}月".format(i) for i in range(1, 13)]
bar = (
Bar()
.add_xaxis(x_data)
.add_yaxis(
"蒸发量",
4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],
yaxis_index=0,
color="#d14a61",
)
.add_yaxis(
"降水量",
5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],
yaxis_index=1,
color="#5793f3",
)
.extend_axis(
yaxis=opts.AxisOpts(
name="蒸发量",
type_="value",
min_=0,
max_=250,
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
)
)
.extend_axis(
yaxis=opts.AxisOpts(
type_="value",
name="温度",
min_=0,
max_=25,
position="left",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#675bba")
),
axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
)
)
.set_global_opts(
yaxis_opts=opts.AxisOpts(
name="降水量",
min_=0,
max_=250,
position="right",
offset=50,
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="#5793f3")
),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
title_opts=opts.TitleOpts(title="Grid-多 Y 轴示例"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
)
)
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(
"平均温度",
2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
yaxis_index=2,
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
)
)
bar.overlap(line)
bar.render_notebook()
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