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plotly画图总结，助你一臂之力！--乌森数据可视化系列(2)

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作者介绍

莲石东路@乌森

心之所向，无界成长。从底层架构到应用实战，聊聊炼数成金背后的故事。

持续更新数据治理、数据科学、数据可视化、空间计算系列文章。


1

plotly


plotly是一个基于plotly.js构建的可视化工具库，支持2D、3D图形，交互流畅。相较于matplotlib这种基础绘图组件，plotly的可视化展现效果更好，可以媲美Tableau的高质量图。

11

plotly的名词解释




踪迹（trace）：类似于matplotlib中的图形（例如折线），只管画图。
布局（layout）：规定了一些需要辅助绘制的内容，如标题、图例等。
数据（data）：一个踪迹的列表，因为可能会同时绘制多个图形，如多条折线。
画布（figure）：与matplotlib中的画布类似，需要传入data和layout参数

12

plotly的绘图步骤




step 1:准备数据
step 2:创建一个或多个踪迹（traces）
step 3:同层踪迹合并
step 4:创建布局（layout）
step 5:在画布（figure）上组合graph部分和layout部分
step 6:图像显示/保存

import plotly as pyimport plotly.graph_objs as goimport numpy as npif __name__ == '__main__':    # 准备数据    x = [i for i in range(60)]    y_shanghai = [np.random.uniform(15, 18) for i in x]    y_beijing = [np.random.uniform(12, 17) for i in x]
    # 创建踪迹    trace1 = go.Scatter(         x=x,         y=y_shanghai,         mode='markers',         name='shanghai'    )    trace2 = go.Scatter(         x=x,         y=y_beijing,         mode='markers',         name='beijing'    )    # 同层踪迹拼合    data = [trace1, trace2]
    # 创建布局    layout = go.Layout(title='示例')
    # 在画布（figure）上按照布局绘制踪迹    fig = go.Figure(data=data, layout=layout)        # 显示    py.offline.plot(fig, filename='first_offline_start.html', auto_open=True)

plotly官方文档：https://plot.ly/python/

2

多子图绘制功能


同matplotlib一样，plotly的一个画布对象也可以包含多个子图。

import plotly as pyimport plotly.graph_objs as go
trace1 = go.Scatter(    x=[1, 2, 3],    y=[4, 5, 6])trace2 = go.Scatter(    x=[20, 30, 40],    y=[50, 60, 70],    xaxis='x2',    yaxis='y2')trace3 = go.Scatter(    x=[300, 400, 500],    y=[600, 700, 800],    xaxis='x3',    yaxis='y3')trace4 = go.Scatter(    x=[4000, 5000, 6000],    y=[7000, 8000, 9000],    xaxis='x4',    yaxis='y4')data = [trace1, trace2, trace3, trace4]
layout = go.Layout(    xaxis=dict(        domain=[0, 0.45]    ),    yaxis=dict(        domain=[0, 0.45]    ),    xaxis2=dict(        domain=[0.55, 1]    ),    xaxis3=dict(        domain=[0, 0.45],        anchor='y3'    ),    xaxis4=dict(        domain=[0.55, 1],        anchor='y4'    ),    yaxis2=dict(        domain=[0, 0.45],        anchor='x2'    ),    yaxis3=dict(        domain=[0.55, 1]    ),    yaxis4=dict(        domain=[0.55, 1],        anchor='x4'    ))
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename='multiple-subplots.html', auto_open=True)

3

绘制二维图表


plotly提供了常见二维图表的绘制。

31

数据分布




1）直方图
import plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as ffimport numpy as np
x = [np.random.randint(1, 100) for i in range(100)]  # 每个数字必须是整数
trace = go.Histogram(    x=x)data = [trace]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

2）箱型图
import plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as ffimport numpy as np
y1 = np.random.randn(50)-1y2 = np.random.randn(50)+1 trace1 = go.Box(    y=y1)trace2 = go.Box(    y=y2)data = [trace1, trace2]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

3）小提琴图
import plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as ffimport numpy as np
y1 = np.random.randn(50)-1y2 = np.random.randn(50)+1 trace1 = go.Violin(    y=y1)trace2 = go.Violin(    y=y2)data = [trace1, trace2]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)


32

数据比较




1）柱状图
import plotly as pyimport plotly.graph_objs as goimport numpy as np
trace1 = go.Bar(    x = ['Jan','Feb','Mar','Apr', 'May','Jun',         'Jul','Aug','Sep','Oct','Nov','Dec'],    y = [20,14,25,16,18,22,19,15,12,16,14,17],    name = 'Primary Product',    marker=dict(        color = 'rgb(49,130,189)'    ))trace2 = go.Bar(    x = ['Jan','Feb','Mar','Apr', 'May','Jun',         'Jul','Aug','Sep','Oct','Nov','Dec'],    y = [19,14,22,14,16,19,15,14,10,12,12,16],    name = 'Secondary Product',    marker=dict(        color = 'rgb(204,204,204)'    ))data = [trace1, trace2]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

2）雷达图
import plotly as pyimport plotly.graph_objs as go
categories = ['processing cost','mechanical properties','chemical stability', 'thermal stability', 'device integration']
trace1 = go.Scatterpolar(    r=[1, 5, 2, 2, 3],    theta=categories,    fill='toself',    name='Product A')
trace2 = go.Scatterpolar(    r=[4, 3, 2.5, 1, 2],    theta=categories,    fill='toself',    name='Product B')data = [trace1, trace2]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename='test.html', auto_open=True)

3）折线图
import plotly as pyimport plotly.graph_objs as goimport numpy as np
x = [i for i in range(100)]y1 = np.random.randn(100)y2 = np.random.randn(100)
trace1 = go.Scatter(    x=x,    y=y1,    mode='lines',    name='line1')trace2 = go.Scatter(    x=x,    y=y2,    mode='lines',    name='line2')data = [trace1, trace2]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

33

数据关系




1）散点图
import plotly as pyimport plotly.graph_objs as goimport numpy as np
random_x = np.random.randn(1000)random_y = np.random.randn(1000)
trace = go.Scatter(    x=random_x,    y=random_y,    mode='markers')data = [trace]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

2）气泡图
import plotly as pyimport plotly.graph_objs as goimport numpy as np
random_x = np.random.randn(1000)random_y = np.random.randn(1000)
trace = go.Scatter(    x=random_x,    y=random_y,    mode='markers',    marker=dict(        size=16,        color=np.random.randn(500),        colorscale='Viridis',        showscale=True    ))data = [trace]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

34

数据成分




1）饼图
import plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as ffimport numpy as np
labels = ['产品1','产品2','产品3','产品4','产品5']values = [38.7,15.33,19.9,8.6,17.47]
trace = go.Pie(    labels=labels,     values=values,    hole=0.3, # 空白区域比例    hoverinfo="label + percent")data = [trace]
layout = go.Layout(title='示例')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)

2）堆积柱形图
import plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as ffimport numpy as np
trace1 = go.Bar(    y = ['CU.SHF', 'AG.SHF', 'AU.SHF'],    x = [21258, 30279, 8056],    name = '期货1',    orientation = 'h',    marker = dict(        color = '#104E8B',        line = dict(            color = '#104E8B',            width = 3)    ))trace2 = go.Bar(    y = ['CU.SHF', 'AG.SHF', 'AU.SHF'],    x = [19853, 9375, 4063],    name = '期货2',    orientation = 'h',    marker = dict(        color = '#1874CD',        line = dict(            color = '#104E8B',            width = 3)    ))trace3 = go.Bar(    y = ['CU.SHF', 'AG.SHF', 'AU.SHF'],    x = [4959, 13018, 8731],    name = '期货3',    orientation = 'h',    marker = dict(        color = '#1C86EE',        line = dict(            color = '#104E8B',            width = 3)    ))
data = [trace1, trace2, trace3]
layout = go.Layout(    title='稀有金属期货持仓量对比图',    barmode='stack')
fig = go.Figure(data=data, layout=layout)    py.offline.plot(fig, filename='test.html', auto_open=True)
拓展阅读：https://www.cnblogs.com/ws17345067708/tag/Plotly/

4

数据成分


使用plotly绘制三维图表的方法和绘制二维图表类似，也不需要引入额外模块。


1）三维空间中的点

import numpy as npimport plotly as pyimport plotly.graph_objs as go
x1, y1, z1 = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 200).transpose()trace1 = go.Scatter3d(    x=x1,    y=y1,    z=z1,    mode='markers',    marker=dict(        color='rgb(217, 217, 217)',        size=12,        symbol='circle',        opacity=0.8    ))
x2, y2, z2 = np.random.multivariate_normal(np.array([0,0,0]), np.eye(3), 200).transpose()trace2 = go.Scatter3d(    x=x2,    y=y2,    z=z2,    mode='markers',    marker=dict(        color='rgb(127, 127, 127)',        size=12,        symbol='circle',        opacity=0.8    ))
data = [trace1, trace2]
layout = go.Layout(    title='三维散点',    autosize=True,    margin=dict(        l=65,        r=50,        b=65,        t=90    ))
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename='test.html', auto_open=True)


2）三维空间中的线

import numpy as npimport plotly as pyimport plotly.graph_objs as go
def f(x, y):    z = np.power(x, 2) + np.power(y, 2)    return z x = y = np.linspace(start=0, stop=10, num=1000)z = f(x, y)
trace = go.Scatter3d(    x=x,    y=y,    z=z,    mode='lines')
data = [trace]
layout = go.Layout(    title='三维线',    autosize=True,    margin=dict(        l=65,        r=50,        b=65,        t=90    ))
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename='test.html', auto_open=True)
fig.show()


3）三维空间中的面

import numpy as npimport plotly as pyimport plotly.graph_objs as go
def f(x, y):    z = np.power(x, 2) + np.power(y, 2)    return z
a = np.linspace(start=0, stop=3, num=4, dtype=np.int32)b = np.linspace(start=0, stop=3, num=4, dtype=np.int32)
x, y = np.meshgrid(a, b)z = f(x, y)

trace = go.Surface(x=x, y=y, z=z)data = [trace]
layout = go.Layout(    title='三维曲面',    autosize=True,    margin=dict(        l=65,        r=50,        b=65,        t=90    ))
fig = go.Figure(data=data, layout=layout)
py.offline.plot(fig, filename='test.html', auto_open=True)   


5

高级封装

Plotly Express是Plotly的高级封装，它为复杂的图表绘制提供了一种简单的语法。


51

数据分布




1）直方图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.histogram(    tips, x="total_bill", y="tip", color="sex",     marginal="rug", hover_data=tips.columns)
py.offline.plot(fig, filename='test.html', auto_open=True)


2）箱形图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.box(tips, x="day", y="total_bill", color="smoker", notched=True)
py.offline.plot(fig, filename='test.html', auto_open=True)   


3）小提琴图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.violin(    tips, y="tip", x="smoker", color="sex",     box=True, points="all", hover_data=tips.columns)
py.offline.plot(fig, filename='test.html', auto_open=True)


52

数据比较




1）柱形图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.bar(tips, x="sex", y="total_bill", color="smoker", barmode="group")
py.offline.plot(fig, filename='test.html', auto_open=True)


2）分组柱形图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.bar(    tips, x="sex", y="total_bill", color="smoker",     barmode="group", facet_row="time", facet_col="day",     category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]})
py.offline.plot(fig, filename='test.html', auto_open=True)   


3）雷达图

import pandas as pdimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
df = pd.DataFrame(dict(    r=[1, 5, 2, 2, 3],    theta=['processing cost','mechanical properties','chemical stability', 'thermal stability', 'device integration']))
fig = px.line_polar(df, r='r', theta='theta', line_close=True)# fig.update_traces(fill='toself')
py.offline.plot(fig, filename='test.html', auto_open=True)


4）折线图

import plotly as pyimport plotly.express as pximport plotly.graph_objs as go
gapminder = px.data.gapminder()
# 选取2个国家：中国、阿富汗country_list = ["China", "Afghanistan"]    tem_gapminder = gapminder.query("country == @country_list")
fig = px.line(    tem_gapminder, x="year", y="lifeExp", color="country",     line_shape="spline", line_dash="country", title="国家人均寿命")# 修改时添加的代码fig.update_traces(dict(mode="markers+lines"))fig.update(layout=dict(xaxis=dict(title="年份", showline=True, nticks=10), yaxis=dict(title="人均寿命", showline=True, nticks=10)))
py.offline.plot(fig, filename='test.html', auto_open=True)


53

数据关系




1）带边缘的散点图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
iris = px.data.iris()
fig = px.scatter(    iris, x="sepal_width", y="sepal_length", color="species",     marginal_y="violin", marginal_x="box",     trendline="ols")
py.offline.plot(fig, filename='test.html', auto_open=True)


2）矩阵散点图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
iris = px.data.iris()
fig = px.scatter_matrix(    iris, dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"], color="species")
py.offline.plot(fig, filename='test.html', auto_open=True)


3）带时间轴的散点图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
gapminder = px.data.gapminder()
fig = px.scatter(    gapminder, x="gdpPercap", y="lifeExp", animation_frame="year",     animation_group="country", size="pop", color="continent",     hover_name="country", facet_col="continent",log_x=True,     size_max=45, range_x=[100,100000], range_y=[25,90])
py.offline.plot(fig, filename='test.html', auto_open=True)


4）三元散点图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
election = px.data.election()
fig = px.scatter_ternary(    election, a="Joly", b="Coderre", c="Bergeron", color="winner",     size="total", hover_name="district", size_max=15,     color_discrete_map = {"Joly": "blue", "Bergeron": "green", "Coderre":"red"})
py.offline.plot(fig, filename='test.html', auto_open=True)


5）平行坐标图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
iris = px.data.iris()
fig = px.parallel_coordinates(    iris, color="species_id",     labels={"species_id": "Species", "sepal_width": "Sepal Width", "sepal_length": "Sepal Length",  "petal_width": "Petal Width", "petal_length": "Petal Length"},    color_continuous_scale=px.colors.diverging.Tealrose, color_continuous_midpoint=2)
py.offline.plot(fig, filename='test.html', auto_open=True)


6）并行类别图

import numpy as npimport plotly as pyimport plotly.express as pximport plotly.graph_objs as go
tips = px.data.tips()
fig = px.parallel_categories(    tips, color="size", color_continuous_scale=px.colors.sequential.Inferno)
py.offline.plot(fig, filename='test.html', auto_open=True)


54

极坐标




1）极坐标散点图

import plotly as pyimport plotly.express as pximport plotly.graph_objs as go
wind = px.data.wind()
fig = px.scatter_polar(wind, r="frequency", theta="direction")
py.offline.plot(fig, filename='test.html', auto_open=True)


2）极坐标条形图

import plotly as pyimport plotly.express as pximport plotly.graph_objs as go
wind = px.data.wind()
fig = px.scatter_polar(wind, r="frequency", theta="direction")
py.offline.plot(fig, filename='test.html', auto_open=True)


55

地理




1）地理坐标散点图

import plotly as pyimport plotly.express as pximport plotly.graph_objs as go
gapminder = px.data.gapminder()
fig = px.scatter_geo(    gapminder, locations="iso_alpha", color="continent", hover_name="country",     size="pop", animation_frame="year", projection="natural earth")
py.offline.plot(fig, filename='test.html', auto_open=True)


2）地理区域图

import plotly as pyimport plotly.express as pximport plotly.graph_objs as go
gapminder = px.data.gapminder()
fig = px.choropleth(    gapminder, locations="iso_alpha", color="lifeExp", hover_name="country",     animation_frame="year",color_continuous_scale=px.colors.sequential.Plasma, range_color=[20,80])
py.offline.plot(fig, filename='test.html', auto_open=True)   


56

箭头图





1）基本的箭头图

import plotly.figure_factory as ffimport numpy as np
x,y = np.meshgrid(np.arange(0, 2, .2), np.arange(0, 2, .2))u = np.cos(x)*yv = np.sin(x)*y
fig = ff.create_quiver(x, y, u, v)
py.offline.plot(fig, filename='test.html', auto_open=True)


2）带中心点的箭头图

import plotly as pyimport plotly.figure_factory as ffimport plotly.graph_objects as goimport numpy as np
x, y = np.meshgrid(np.arange(-2, 2, .2), np.arange(-2, 2, .25))z = x*np.exp(-x**2 - y**2)v, u = np.gradient(z, .2, .2)
# 创建箭头图fig = ff.create_quiver(    x, y, u, v,    scale=.25, arrow_scale=.4, name='quiver', line_width=1)
# 往图中添加点fig.add_trace(    go.Scatter(x=[-.7, .75], y=[0,0], mode='markers', marker_size=12, name='points'))
py.offline.plot(fig, filename='test.html', auto_open=True)

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