plotly-express-8-plotly绘制散点图

plotly_express-8-plotly绘制散点图

本文中介绍的是利用plotly_express绘制散点图，使用的是scatter()方法。

With px.scatter, each data point is represented as a marker point, whose location is given by the x and y columns.

通过plotly_express库来实现
通过plotly.graph_objects实现

基于plotly_express的散点图

模拟数据

直接将数据传进来

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import plotly_express as px
import pandas as pd
import numpy as np

px.scatter(x=[1,2,6,7,9,8,3,4,5],y=[2,14,12,24,36,8,25,7,18])

内置数据gapminder

内置数据iris

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df = px.data.iris()
fig = px.scatter(
df,
x="sepal_width",
y="sepal_length", # 绘图的数据及xy轴
color="species", # 点的颜色
size='petal_length', # 点的大小
hover_data=['petal_width'] # 悬停显示的数据
)

fig.show()

连续型的点图line-scatter

连续型的点图，比如：三角函数的图形、线性图形等

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x = np.linspace(0,10,100) # 0-10的100个数
y = np.sin(x)
px.line(x=x,y=y,labels={"x":"t","y":"sin(t)"})

基于go.Scatter的散点图

demo

go.Figure确定画布
go.Scatter画图，传入需要的数据

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t = np.linspace(0, 10, 50)
y = np.sin(t)
fig = go.Figure(data=go.Scatter(x=t, y=y, mode="markers"))
fig.show()

子图制作

在一个画布figure中画多个图

go.figure确定画布
go.add_trace()：将不同的图形画在一个画布上
fig.show()：显示图形

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np.random.seed(2)

N = 100
random_x = np.linspace(0, 1, N)

random_y0 = np.random.randn(N) + 8
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 8
random_y3 = np.random.randn(N) - 4

fig = go.Figure()

# add traces
fig.add_trace(go.Scatter(x=random_x,y=random_y0,
mode="markers",name="markers"))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode='lines+markers',name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode='lines',name='lines'))
fig.add_trace(go.Scatter(x=random_x, y=random_y3,
mode='markers',name='markers'))
fig.show()

冒泡散点-bubble scatter

冒泡散点图：随着坐标轴数值的变化，点的大小随着变化

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fig = go.Figure(go.Scatter(
x=np.linspace(0,50,10),
y=np.random.randint(0,50,10),
mode="markers",
marker=dict(size=np.random.randint(0,50,10), # 通过字典的形式来实现
color=np.random.randint(50,100,10))
))
fig.show()

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t = np.linspace(0, 10, 100)

fig = go.Figure()

fig.add_trace(go.Scatter(
x=t, y=np.sin(t),
name='sin',
mode='markers',
marker_color='rgba(20, 180, 60, .8)'
))

fig.add_trace(go.Scatter(
x=t, y=np.cos(t),
name='cos',
mode='markers',
marker_color='rgba(25, 182, 193, .9)'
))

# fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter', # 标题
yaxis_zeroline=False, xaxis_zeroline=False)
fig.show()

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t = np.linspace(0, 10, 100)

fig = go.Figure()

fig.add_trace(go.Scatter(
x=t, y=np.sin(t),
name='sin',
mode='markers',
marker_color='rgba(20, 180, 60, .8)'
))

fig.add_trace(go.Scatter(
x=t, y=np.cos(t),
name='cos',
mode='lines',
marker_color='rgba(25, 182, 193, .9)'
))

# Set options common to all traces with fig.update_traces
# 设置整个散点图的大小和间隔
fig.update_traces(mode='markers', marker_line_width=2, marker_size=8)
fig.update_layout(title='Styled Scatter',
yaxis_zeroline=True, xaxis_zeroline=False)

fig.show()

数据悬停Data Labels on Hover

在使用go.Scatter的时候，如何实现悬停时候数据的显示

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df = px.data.iris()
fig = go.Figure(data = go.Scatter( # Figure类中的第一个属性是data
x=df["sepal_length"], # xy坐标轴的数据
y=df["sepal_width"],
mode="markers", # 点的表示
marker_color=df["species_id"], # 点的颜色，px中是color
text=df["species"])) # 悬停的显示，px中是hove_data
fig.show()

Scatter with a Color Dimension

指的是在图形右边实现颜色的不断变化

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x = np.linspace(0,10,500)
y = np.random.randint(0,100,500)

fig = go.Figure(data=go.Scatter(
x=x,
y=y,
mode="markers",
marker=dict( # marker是字典的形式
size=20,
color=np.random.randint(0,100,500), # 指定颜色区间
colorscale="Viridis", # 选择哪种颜色
showscale=True # 右边的颜色尺度尺是否显示
)
))

fig.show()

默认的颜色

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