# 气泡图import plotly.express as pxdf = px.data.gapminder()fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp", size="pop", # 设置气泡大小依据字段pop color="continent", hover_name="country", log_x=True, size_max=60, #设置最大气泡 )fig.s...
graph_objects 类的 scatter() 方法产生一个散点轨迹。属性的模式决定了数据点的外观。 语法:plotly.graph_objects.Scatter(arg=None, cliponaxis=None, connectgaps=None, customdata=None, customdatasrc=None, dx=None, dy=None, error_x=None, error_y=None, fill=None , fillcolor=None, groupnorm=Non...
bordercolor="Black", # 图例边框颜色和宽度 borderwidth=2 ) ) fig.show() Graph Objects设置图例 下面的多个案例都是基于plotly.graph_objects来进行图例的设置: 图例名称 fig = go.Figure() fig.add_trace(go.Scatter( x=[1, 2, 3, 4, 5], y=[1, 2, 3, 4, 5], name="图1" # 图例名称 ...
import plotly.graph_objects as go # 导入plotly.graph_objects import numpy as np # 生成数据 t =...
import plotly.graph_objects as go import plotly.offline as of # 这个为离线模式的导入方法 import pandas as pd #使用pandas处理csv数据 example1 Scatter 通过Scatter方法画折线图: data = pd.read_csv(r'C:\Users\Administrator\Desktop\data\nz_weather.csv') ...
import plotly.express as px import plotly.graph_objects as go df = px.data.tips() fig = go.Figure(data =[go.Scatter3d(x = df['total_bill'], y = df['time'], z = df['day'], mode ='markers', marker = dict( color = df['tip'], colorscale ='Viridis', opacity = 0.5 ) ...
fig= px.line(df, x='year', y='lifeExp', color='country') fig.show() 二、 使用 go.Scatter (go:plotly.graph_objects) 1.最简单的散点图 importplotly.graph_objects as goimportnumpy as np N= 1000t= np.linspace(0, 10, 100)
fig= px.line(df, x='year', y='lifeExp', color='country') fig.show() 二、 使用 go.Scatter (go:plotly.graph_objects) 1.最简单的散点图 importplotly.graph_objects as goimportnumpy as np N= 1000t= np.linspace(0, 10, 100)
通过plotly.graph_objects实现 1 基于px的散点图 1.1 模拟数据 直接将数据传进来 代码语言:javascript 复制 importplotly_expressaspximportpandasaspdimportnumpyasnp px.scatter(x=[1,2,6,7,9,8,3,4,5],y=[2,14,12,24,36,8,25,7,18])
通过plotly.graph_objects实现 image 1 基于plotly_express的散点图 1.1 模拟数据 直接将数据传进来 importplotly_expressaspximportpandasaspdimportnumpyasnp px.scatter(x=[1,2,6,7,9,8,3,4,5],y=[2,14,12,24,36,8,25,7,18]) image 1.2 内置数据gapminder ...