1）Plotly可视化画Scatter图_plotly scatter

 文章最前： 我是Octopus，这个名字来源于我的中文名--章鱼；我热爱编程、热爱算法、热爱开源。所有源码在我的个人github ；这博客是记录我学习的点点滴滴，如果您对 Python、Java、AI、算法有兴趣，可以关注我的动态，一起学习，共同进步。

最近由于工作需要，需要对数据进行一些可视化操作，找到了Plotly库，现在就学习一下plotly进行绘图

1.首先导入包 

import numpy as np 
import pandas as pd 
import plotly.graph_objects as go
import plotly.offline as po
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import matplotlib.pyplot as plt
import plotly.express as px
import random
import plotly.figure_factory as ff

 2）基础散点图可视化

random_x = np.random.randint(1, 100, 50)
random_y = np.random.randint(1, 100, 50)
data = [
go.Scatter(x = random_x, y = random_y, mode = 'markers')
]
 
layout = go.Layout(
xaxis = dict(
showgrid=True, # Hide Gridlines
showline=False, # Hide X-Axis
),
yaxis = dict(
showgrid=True, # Hide Gridlines
showline=False, # Hide X-Axis
),
)
fig = go.Figure(data = data, layout = layout)
iplot(fig)

3） 散点图设置大小

# Changing Marker size , shape & color using Marker parameter
x_val = np.random.randint(1,100,50)
y_val = np.random.randint(1,100,50)
data = [go.Scatter(
x = x_val,
y = y_val,
mode = 'markers',
marker = dict(
size = 10,
color = '#91bd3a', #color of marker
symbol = 'circle', # Shape of scatter plot
line = dict(width = 1) #width of boundary
)
)
]
layout = go.Layout(
xaxis=dict(
showgrid=False, # Hide Gridlines
showline=False, # Hide X-Axis
),
yaxis=dict(
showgrid=False, # Hide Gridlines
showline=False, # Hide X-Axis
),
)
fig = go.Figure(data=data,layout=layout)
iplot(fig)

4）定义标题和x轴以及y轴

# Defining Labels (X-Axis & Y-Axis label , Graph tile)
x_val = np.random.randint(1,100,50)
y_val = np.random.randint(1,100,50)
data = [go.Scatter(
x = x_val,
y = y_val,
mode = 'markers',
marker = dict(
size = 10,
color = '#4ED700',
symbol = 'circle',
line = dict(width = 1,color = '#0E8700')
)
)
]
layout = go.Layout(
title = '$Scatter Plot$', # Title
xaxis = dict(title = '$X-Axis$',showgrid=False,showline=False), # x-axis label
yaxis = dict(title = '$Y-Axis$',showgrid=False,showline=False), # y-axis label
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

5）设置不同的颜色

x_values = np.linspace(0, 100, 100) # 100 evenly spaced values
y_values = np.random.randn(100) # 100 random values
trace0 = go.Scatter(
x = x_values,
y = y_values,
mode = 'markers',
marker = dict(
size = 7,
color = '#F4D03F',
symbol = 'circle',
line = dict(width = 1,color = '#0E8700')
)
)
trace1 = go.Scatter(
x = x_values,
y = y_values-5,
mode = 'markers',
marker = dict(size = 7,
color = '#A9DFBF',
symbol = 'circle',
line = dict(width = 1,color = '#0E8700')
)
)
data = [trace0, trace1]
layout = go.Layout(
xaxis=dict(
showgrid=False, # Hide Gridlines
showline=False, # Hide X-Axis
),
yaxis=dict(
showgrid=False, # Hide Gridlines
showline=False, # Hide X-Axis
),
)
fig = go.Figure(data=data,layout=layout)
iplot(fig)

 6）去掉坐标轴

data = [go.Scatter(
x = auto_df["engine-size"],
y = auto_df["wheel-base"],
mode = 'markers',
marker = dict(size = 7,
color = '#4ED700',
symbol = 'circle',
line = dict(width = 1,color = '#0E8700')
)
)
]
layout = go.Layout(
title = 'EngineSize-WheelBase', # Chart Title
xaxis = dict(title = 'Engine Size$',showgrid=False,showline=False), # x-axis label
yaxis = dict(title = '$Wheel Base$',showgrid=False,showline=False), # y-axis label
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

 7） 绘制不同颜色

# trace0 will capture all smokers
trace0 = go.Scatter(
x = auto_df[auto_df["fuel-type"]=='gas']["engine-size"],
y = auto_df[auto_df["fuel-type"]=='gas']["wheel-base"],
mode = 'markers',
name = 'Gas',
marker = dict(size = 7, color = '#F39C12',symbol = 'circle',line = dict(width = 1))
)
# trace1 will capture all non-smokers
trace1 = go.Scatter(
x = auto_df[auto_df["fuel-type"]=='diesel']["engine-size"],
y = auto_df[auto_df["fuel-type"]=='diesel']["wheel-base"],
mode = 'markers',
name = 'Diesel',
marker = dict(size = 7, color = '#8BC34A',symbol = 'circle',line = dict(width = 1))
)
layout = go.Layout(
title = '$Scatter Plot$', # Title
xaxis = dict(title = '$Engine Size$',showgrid=False,showline=False), # x-axis label
yaxis = dict(title = '$Wheel Base$',showgrid=False,showline=False), # y-axis label
)
data = [trace0, trace1]
fig = go.Figure(data=data,layout=layout)
iplot(fig)

8）不同的点设置子图 

# Display multiple Scatter plots in one figure using Subplots
from plotly.subplots import make_subplots
#Subplot initialization
fig = make_subplots(
rows=1,
cols=2,
subplot_titles=("Subplot-1", "Subplot-2")
)
# Subplot - 1 (Add graph object trace to a figure)
fig.add_trace(go.Scatter
(
x = x_val,
y = y_val,
mode = 'markers',
marker = dict(size = 10, color = 'crimson',symbol = 'circle',line = dict(width = 1,color = '#0E8700'))
),
row=1, col=1
)
 
# Add graph object trace to a figure (Subplot-2)
fig.add_trace(go.Scatter
(
x = x_val,
y = y_val,
mode = 'markers',
marker = dict(size = 10, color = 'gold',symbol = 'circle',line = dict(width = 1))
),
row=1, col=2
)
# Hide grid lines
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
 
fig.show()