plotly-express-8-plotly实现散点图

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

1 基于px的散点图

1.1 模拟数据

直接将数据传进来

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])

1.2 内置数据gapminder

1.3 内置数据iris

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()

1.4 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

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)"})

2 基于go.Scatter的散点图

2.1 demo

• go.Figure确定画布
• go.Scatter画图，传入需要的数据

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

2.2 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

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()

2.3 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

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()

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()

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()

2.4 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

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()

2.5 Scatter with a Color Dimension

指的是在图形右边实现颜色的不断变化

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

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),
        showscale=True
    )
))

fig.show()