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Plotly绘图，快速入门

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皮大大

发布于 2024-06-29 09:46:22

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代码可运行

运行总次数：2

代码可运行

公众号：尤而小屋编辑：Peter 作者：Peter

大家好，我是Peter~

本文基于一份公开的数据讲解plotly的多种图形的绘制，包含：

散点图
分组散点图
气泡图
3D散点图
线形图
柱状图
分组柱状图
堆叠柱状图
箱型图
饼图
甜甜圈图
直方图
核密度图
热力图
子图

部分图预览：

1 plotly图形

Plotly是一个用于创建交互式图表的Python库，它支持多种图表类型，如折线图、散点图、饼图、热力图等。Plotly的特点如下：

高度可定制：用户可以根据需要调整图表的各种属性，如颜色、字体、轴标签等，以创建符合需求的可视化效果。
交互性：生成的图表具有交互性，用户可以通过鼠标悬停、拖动、缩放等操作查看数据详情和变化趋势。
跨平台：支持在Web、Jupyter Notebook、Python脚本等多种环境中使用，并且可以将图表导出为HTML、PNG、SVG等格式。
集成其他库：可以与其他流行的Python数据处理和可视化库（如Pandas、NumPy、Matplotlib等）结合使用，方便数据处理和图形绘制。
多语言支持：除了Python，Plotly还支持R、JavaScript、MATLAB等多种编程语言，方便不同背景的用户使用。

总之，Plotly是一个功能强大、易于使用的可视化库，适用于数据分析、科学计算、商业智能等领域。

2 导入库

In 1:

import os
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
%matplotlib inline

from datetime import date, time, datetime
import plotly.graph_objs as go
import plotly.offline as pyo
import plotly.figure_factory as ff
import plotly.express as px
from plotly import tools
from plotly.subplots import make_subplots
from plotly.offline import iplot

import warnings
warnings.filterwarnings('ignore')

3 读取数据

In 2:

df = pd.read_csv("StudentsPerformance.csv")
df.head()

4 散点图Scatter charts

In 3:

# 基于px
fig = px.scatter(df,x="reading score",y="writing score")

fig.show()

另一种方式：

In 4:

# 基于go.Scatter
scatter = [go.Scatter(x = df['reading score'],
                      y = df['writing score'],
                      mode ='markers')] 

fig = go.Figure(scatter)

# iplot(fig)
fig.show()

对散点进行自定义：

In 5:

# 进阶版

data = [go.Scatter(x = df['reading score'],
                   y = df['writing score'],
                   mode = 'markers',
                   marker = dict(size = 12,
                                 color = 'rgb(0, 189, 255)',
                                 symbol = 'diamond',
                                 opacity = 0.75,
                                 line={'color': 'black',
                                       'width': 1.5}))]

layout = go.Layout(title=dict(text='Reading Score & Writing Score',
                              y=0.9,
                              x=0.5,
                              xanchor= 'center',
                              yanchor= 'top'),
                   xaxis={'title':'Reading Score'},
                   yaxis=dict(title = 'Writing Score'),
                   hovermode = 'closest',
                   template = 'plotly_white')

fig = go.Figure(data = data, layout = layout)
# iplot(fig)
fig.show()

5 分组散点图

In 6:

trace_male = (go.Scatter(x=df[df['gender']=='male']['math score'],  # x-y轴数据
                         y = df[df['gender']=='male']['writing score'],
                         showlegend=True,  # 显示legend
                         text='Male', # 标题和名称
                         name='Male',
                         mode='markers',  # 符号标记类型
                         marker = dict(color= 'cornflowerblue',  # 符号属性的自定义：颜色、大小、透明度
                                       size = 9,
                                       opacity = 0.55)))

trace_female = (go.Scatter(x=df[df['gender'] == 'female']['math score'],
                           y = df[df['gender'] == 'female']['writing score'],
                           showlegend=True,
                           text='Female',
                           name = 'Female',
                           mode = 'markers',
                           marker = dict(color = 'darkorange',
                                         size = 9,
                                         opacity = 0.55)))
        
data=[trace_male,trace_female]  # 生成的数据

# 布局
layout= go.Layout(title = 'Math Score & Writing Score',
                  xaxis = dict(title = 'Math Score'),
                  yaxis = dict(title = 'Writing Score'),
                  width = 900,
                  height = 600,
                  template = 'simple_white')

# 添加数据和布局
fig = go.Figure(data=data,layout=layout)   
#iplot(fig)
fig.show()

颜色渐变条设置：

In 7:

data = [go.Scatter(x = df['reading score'],
                   y = df['writing score'],
                   mode = 'markers',
                   text=df['math score'],
                   marker=dict(size = 14,
                               color = df['math score'], # 颜色
                               showscale = True,
                               colorscale = 'Cividis',
                               colorbar = dict(title='Math Score'),
                               opacity = 0.6))]

layout = go.Layout(title=dict(text='Reading Score - Writing Score - Math Score',
                              y = 0.9,
                              x = 0.5,
                              xanchor = 'center',
                              yanchor = 'top'),
                   xaxis = dict(title = 'Reading Score'),
                   yaxis =dict(title = 'Writing Score'),
                   template='simple_white')

fig = go.Figure(data=data,layout=layout)
# iplot(fig)
fig.show()

基于for循环的散点图：

In 8:

df['parental level of education'].value_counts() # 不同的学历水平

Out8:

some college          226
associate's degree    222
high school           196
some high school      179
bachelor's degree     118
master's degree        59
Name: parental level of education, dtype: int64

In 9:

data = []
for i in df['parental level of education'].unique():
    data.append(go.Scatter(x = df[df['parental level of education'] == i]['reading score'],
                           y = df[df['parental level of education'] == i]['math score'],
                           mode = 'markers',
                           name = str(i),
                           showlegend = True,
                           marker = dict(size = 12,
                                          opacity = 0.65)))

layout = go.Layout(title = 'Scores by Level of Education',
                   xaxis = dict(title='Reading Score'),
                   yaxis = dict(title='Math Score'),
                   template = 'plotly_white')

fig = go.Figure(data=data, layout = layout)
iplot(fig)

6 气泡图Bubble charts

In 10:

data = [go.Scatter(x = df['reading score'],
                   y = df['writing score'],
                   mode = 'markers',
                   text = df['math score'],
                   marker = dict(size = df['math score'] * 0.5,  # 关键代码：通过指定字段来控制颜色大小
                                 color = '#FFAE00',
                                 showscale = False,
                                 opacity = 0.5,
                                 line = dict(color = 'black', 
                                             width = 0.5)))]

layout = go.Layout(title=dict(text = 'Reading Score - Writing Score - Math Score',
                              y = 0.9,
                              x = 0.5,
                              xanchor = 'center',
                              yanchor = 'top'),
                   xaxis = dict(title = 'Reading Score'),
                   yaxis = dict(title = 'Writing Score'),
                   template ='plotly_white')

fig = go.Figure(data = data, layout = layout)
#iplot(fig)
fig.show()

7 3D 散点图-3D Scatter plots

In 11:

df.columns

Out11:

Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',
       'test preparation course', 'math score', 'reading score',
       'writing score'],
      dtype='object')

In 12:

data = go.Scatter3d(x = df['math score'],
                    y = df['reading score'],
                    z = df['writing score'],
                    mode = 'markers',
                    marker = dict(color = df['math score'],
                                  showscale = True,
                                  colorbar = dict(title = 'Weight'),
                                  colorscale = 'picnic',
                                  opacity = 0.7))

layout = go.Layout(title = dict(text='Math-Reading-Writing',
                                y = 0.9,
                                x = 0.5,
                                xanchor = 'center',
                                yanchor = 'top'),
                   scene = dict(xaxis = dict(title = 'math score'),
                                yaxis = dict(title = 'reading score'),
                                zaxis = dict(title = 'writing score')),
                   font = dict(size = 12),
                   template = 'plotly_white')

fig = go.Figure(data = data, layout = layout)
# iplot(fig)
fig.show()

8 线形图line charts

生成模拟数据

In 13:

df1 = pd.DataFrame({'date': [date(year = 2015 + i, month = 1, day = 1) for i in range(10)],
                    'students': np.random.randint(25,60,10),
                    'lecturers': np.random.randint(10, 20, 10)})

df1

Out13:

	date	students	lecturers
0	2015-01-01	46	13
1	2016-01-01	26	17
2	2017-01-01	42	19
3	2018-01-01	31	10
4	2019-01-01	56	15
5	2020-01-01	34	13
6	2021-01-01	27	14
7	2022-01-01	42	18
8	2023-01-01	54	11
9	2024-01-01	44	19

In 14:

# 基于px

fig = px.line(df1,x="date",y="students")
fig.show()

另一种方法：

In 15:

# 基于go
line = [go.Scatter(x = df1['date'],
                   y = df1['students'],
                   mode = 'lines')] 

fig = go.Figure(data = line)
fig.show()

自定义标题、xy轴名称等：

In 16:

data = go.Scatter(x = df1['date'],
                  y = df1['students'],
                  mode = 'lines',
                  name = 'students')

layout = go.Layout(title={'text': "Number of Students by Years",
                          'y':0.9,
                          'x':0.5,
                          'xanchor': 'center',
                          'yanchor': 'top'},
                   xaxis = dict(title = 'Year'),
                   yaxis = dict(title = 'Student'),
                   template = 'plotly_white'  # 布局使用的模板
                  )

fig = go.Figure(data = data, layout = layout)
# iplot(fig)
fig.show()

同时使用多个数据，更新模板：

In 17:

# 先生成两个数据
student = go.Scatter(x = df1['date'],
                     y = df1['students'],
                     mode = 'lines', 
                     name = 'students',
                     marker = dict(color = 'darkorange'))

lecturer = go.Scatter(x = df1['date'], 
                      y = df1['lecturers'],
                     mode = 'lines', 
                      name = 'lecturers',
                     marker = dict(color = 'royalblue'))

# 布局的设置
layout = go.Layout(title={'text': "Number of Students & Lecturers by Years",
                          'y':0.9,
                          'x':0.5,
                          'xanchor': 'center',
                          'yanchor': 'top'},
                   xaxis = dict(title = 'Year'),
                   template = 'plotly_dark')  # 换成黑色主题

# data数据以列表的形式
fig = go.Figure(data = [student, lecturer], layout = layout)
fig.show()

9 柱状图bar charts

In 18:

x = df.groupby('gender').agg({'math score':'mean'}).reset_index()['gender']
x

Out18:

0    female
1      male
Name: gender, dtype: object

In 19:

data = go.Bar(x = df.groupby('gender').agg({'math score':'mean'}).reset_index()['gender'], # xy轴数据
              y = df.groupby('gender').agg({'math score':'mean'}).reset_index()['math score'],
              width = [0.5, 0.5], # 每个柱子的宽度
              # 待显示的文本、位置、字体大小及颜色
              text =round(df.groupby('gender').agg({'reading score':'mean'}).reset_index()['reading score'],2),
              textposition="inside",  # inside\outside\auto
              textfont=dict(size=26,color="deeppink"),
              # 柱体设置
              marker = dict(color = 'cornflowerblue',
                            opacity = 0.7,
                            line_color = 'black',
                            line_width = 1))

# 布局设置
layout = go.Layout(title='基于性别的数学平均分',
                   xaxis = dict(title='Gender'),
                   yaxis =dict(title='Math Score'),
                   width = 700,
                   height = 700,
#                    template = 'plotly_white'
                  )

fig=go.Figure(data = data, layout = layout)
fig.update_yaxes(range = [0, 100])

# iplot(fig)
fig.show()

10 分组柱状图grouped bar charts

In 20:

# 生成3个数据轨迹
trace1 = go.Bar(x = df.groupby('gender').agg({'reading score':'mean'}).reset_index()['gender'],   # x-y-显示文本
                y = df.groupby('gender').agg({'reading score':'mean'}).reset_index()['reading score'],
                text = round(df.groupby('gender').agg({'reading score':'mean'}).reset_index()['reading score'],2),
                textposition = 'auto',  # 文本位置
                name = 'Reading Score',  # 名称
                textfont = dict(size = 16),  # 字体
                marker = dict(color = '#06F5E3', opacity = 0.65))  # 标记符号设置

trace2 = go.Bar(x = df.groupby('gender').agg({'writing score':'mean'}).reset_index()['gender'],
                y = df.groupby('gender').agg({'writing score':'mean'}).reset_index()['writing score'],
                text = round(df.groupby('gender').agg({'writing score':'mean'}).reset_index()['writing score'],2),
                textposition = 'auto',
                name = 'Writing Score',
                textfont = dict(size = 16),
                marker=dict(color='#FEAD00',opacity = 0.65))

trace3 = go.Bar(x = df.groupby('gender').agg({'math score':'mean'}).reset_index()['gender'],
                y = df.groupby('gender').agg({'math score':'mean'}).reset_index()['math score'],
                text =round(df.groupby('gender').agg({'math score':'mean'}).reset_index()['math score'],2),
                textposition= 'auto',
                name = 'Math Score',
                textfont = dict(size = 16),
                marker=dict(color='#CC00FE',opacity = 0.65))

layout = go.Layout(title={'text': "Avg Scores by Gender",
                          'x':0.5,'y':0.9,
                          'xanchor': 'center','yanchor': 'top'
                         },
                   barmode='group',
                   legend=dict(x=0.05,
                               y=1.0,
                               bgcolor='rgba(255, 255, 255, 0)',
                               bordercolor='rgba(255, 255, 255, 0)'),
                   xaxis = dict(title = 'Gender'),
                   yaxis = dict(title = 'Score'),
                   template ='plotly_white')

fig = go.Figure(data = [trace1,trace2,trace3],
                layout=layout)

fig.update_yaxes(range=[0,100])
iplot(fig)

11 堆叠柱状图stacked bar charts

In 21:

df.groupby(['parental level of education']).mean() # 基于某个字段的均值

Out21:

	math score	reading score	writing score
parental level of education
associate's degree	67.882883	70.927928	69.896396
bachelor's degree	69.389831	73.000000	73.381356
high school	62.137755	64.704082	62.448980
master's degree	69.745763	75.372881	75.677966
some college	67.128319	69.460177	68.840708
some high school	63.497207	66.938547	64.888268

In 22:

parental_avg = pd.DataFrame(df.groupby(['parental level of education']).mean())
parental_avg = parental_avg.reset_index()

trace1 = go.Bar(x = parental_avg['parental level of education'],  # 字段的唯一值信息
                y = parental_avg['math score'],  
                name = 'math score',
                marker = dict(color ='#F2E80C',opacity = 0.7))

trace2 = go.Bar(x = parental_avg['parental level of education'],
                y = parental_avg['reading score'],
                name ='reading score',
                marker = dict(color ='#44F20C',opacity = 0.7))

trace3 = go.Bar(x = parental_avg['parental level of education'],
                y = parental_avg['writing score'],
                name='writing score',
                marker = dict(color = '#F20CE1',opacity = 0.7))

layout = go.Layout(title = 'Avg Scores by Level of Education',
                   barmode = 'stack',
                   xaxis = dict(title='Level of Education'),
                   yaxis =dict(title='Score'),
                   template = 'plotly_dark')

fig = go.Figure(data = [trace1, trace2, trace3], layout=layout)
fig.show()

12 箱型图Box plots

基础柱状图：

In 23:

data = go.Box(y=df['math score'],  # 待绘图的数据
              name = 'Math Score',
              marker_color='#91E26B')

layout = go.Layout(title={'text': "Math Score", 'y':0.9, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'},
                   width = 600,
                   height = 600)

fig = go.Figure(data = data, layout=layout)
fig.show()

同时绘制子图和箱型图：

In 24:

fig = make_subplots(rows=1,  # 子图1*2
                    cols=2,
                    shared_yaxes=True, # 共享y轴
                    subplot_titles=("Male", "Female")  # 子图名称
                   )

fig.add_trace(go.Box(y =df[df['gender']=='male']['writing score'],  # male----writing
                     showlegend=False,
                     name = 'Writing Score',
                     marker_color='#1760E1'),
              row=1,col=1)

fig.add_trace(go.Box(y =df[df['gender']=='male']['math score'],  # male----math
                     showlegend=False ,
                     name = 'Math Score',
                     marker_color='#17E160'),
              row=1,col=1)

fig.add_trace(go.Box(y =df[df['gender']=='male']['reading score'],  # male----reading
                     showlegend=False ,
                     name = 'Reading Score',
                     marker_color='#E1E117'),
              row=1,col=1)

fig.add_trace(go.Box(y =df[df['gender']=='female']['writing score'],  #  female----writing
                     showlegend=False,
                     name = 'Writing Score',
                     marker_color='#1760E1'),
              row=1,col=2)

fig.add_trace(go.Box(y =df[df['gender']=='female']['math score'] ,   #   female----math
                     showlegend=False,
                     name = 'Math Score',
                     marker_color='#17E160'),
              row=1,col=2)

fig.add_trace(go.Box(y =df[df['gender']=='female']['reading score'],   # female----reading
                     showlegend=False ,
                     name = 'Reading Score',
                     marker_color='#E1E117'),
              row=1,col=2)

fig.update_layout(title={'text': "Scores by Gender",
                         'y':0.9,
                         'x':0.5,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                  width = 800,
                  height= 450,
                  template='plotly')      
fig.show()

13 饼图pie charts

In 25:

colors = ['#28F20C', '#0CF2F2', '#F27F0C', '#F20C52']

fig = go.Figure(data=[go.Pie(labels = df['race/ethnicity'].value_counts().keys(),  # 字段的唯一值信息
                             values = df['race/ethnicity'].value_counts().values)])  # 不同的数量

fig.update_traces(hoverinfo='value',   # 悬停信息
                  textinfo='label',  # 每个扇形显示的信息 
                  textfont_size=16,  # 字体大小和位置
                  textposition ='auto',
                  showlegend=False,  # 不显示图例
                  #marker=dict(colors=colors) # 颜色设置
                 )

fig.update_layout(title={'text': "Race/Ethnicity Gropus",
                         'y':0.9,
                         'x':0.5,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                 template='simple_white')

# iplot(fig)
fig.show()

可以绘制扇形区域图：

In 26:

colors = ['#14CFE8', '#E814C1']

fig = go.Figure(data=[go.Pie(labels = df['lunch'].value_counts().keys(),
                             values = df['lunch'].value_counts().values,
                             pull = [0, 0.25])])  # 控制每个扇形区块的偏离程度

fig.update_traces(hoverinfo ='label',
                  textinfo ='percent',
                  textfont_size = 20,
                  textposition ='auto',
                  marker=dict(colors=colors,
                              line = dict(color = 'black', width = 1.5)))

fig.update_layout(title={'text': "Percentages of Lunch Types",
                         'x':0.5,
                         'y':0.9,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                  template='plotly_white')

# iplot(fig)
fig.show()

14 甜甜圈图Donut Charts

主要是通过hole属性来控制：

In 27:

colors = ['#D7DD19', '#6FDD19', '#19DDA5', '#195ADD','#A219DD','#DD1984']

fig = go.Figure(data=[go.Pie(labels = df['parental level of education'].value_counts().keys(),
                             values = df['parental level of education'].value_counts().values)])

fig.update_traces(hoverinfo='label',
                  textinfo='value',
                  hole = 0.4,  # 内圈的大小
                  textfont_size = 22,
                  textposition ='auto',
                  marker=dict(colors = colors,
                              line = dict(color = 'white',
                                          width = 2)))

fig.update_layout(title={'text': "Parental Level of Education",
                         'y':0.9,
                         'x':0.5,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                          template='simple_white')

fig.show()

15 直方图histograms

In 28:

data = [go.Histogram(x = df['math score'],  # 待绘制的数据
                     xbins = dict(start = 0, # 起始值和间隔
                                  end = 100,
                                  size = 5),
                     marker=dict(color ='#FFE400',  # 箱体的颜色、线形颜色和宽度
                                 line = dict(color='black',width=2))
                    )
       ]

layout = go.Layout(title ='直方图绘制',
                   xaxis = dict(title='Score'),
                   yaxis =dict(title='Frequency'),
                   width=700,
                   height=450, 
                   template = 'simple_white'
                  )

fig = go.Figure(data = data, layout = layout)

iplot(fig)

分组的直方图：

In 29:

fig = go.Figure()

# 添加两个数据
fig.add_trace(go.Histogram(x=df[df['gender']=='male']['reading score'], # male数据
                           # 箱体起止点、间隔
                           xbins = dict(start = 0,end =100,size =5),
                           name='Male',
                           marker=dict(color = '#0891EF',opacity = 0.5)))

fig.add_trace(go.Histogram(x=df[df['gender']=='female']['reading score'],
                           xbins = dict(start = 0,end =100, size =5),
                           name='Female',
                           marker =dict(color ='#FF00E0',opacity = 0.5)))

fig.update_layout(title='Reading Scores Histogram',
                  barmode='overlay',
                  xaxis = dict(title='Score'),
                  yaxis =dict(title='Frequency'),
                  width=700,
                  height=450)
fig.show()

16 核密度图Distplots

distplots图是一种用于展示数值数据的统计表示的图形，它结合了直方图、核密度估计或正态曲线以及地毯图。distplots图提供了一种灵活的方式来观察和分析单变量观测值的分布特征。

In 30:

hist_data = []
group_labels=[]

for i in range(len(df['race/ethnicity'].unique())):  # 唯一值长度
    hist_data.append(df[df['race/ethnicity'] == df['race/ethnicity'].unique()[i]]['math score'])  # 唯一值对应的math score数据
    group_labels.append(df['race/ethnicity'].unique()[i])  # 唯一值数据

# 绘制核密度图
fig = ff.create_distplot(hist_data, group_labels, bin_size=5)

fig.update_layout(title={'text': "Math Scores Distplot",
                         'y':0.9,
                         'x':0.5,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                  barmode='overlay',
                  template='plotly_white')

fig.show()

17 热力图heatmap

In 31:

fig = go.Figure(go.Heatmap(x=df['gender'],
                    y= df['test preparation course'],
                    z = df['math score'].values.tolist()))
fig.show()

绘制进阶版的热力图：

In 32:

data = [go.Heatmap(x=df['gender'],
                   y= df['parental level of education'],
                   z = df['math score'].values.tolist(),  # 颜色条
                   colorscale = 'Magma')]

layout = go.Layout(title={'text': "Gender & Level of Education",
                          'y':0.9,
                          'x':0.5,
                          'xanchor': 'center',
                          'yanchor': 'top'},
                   xaxis = dict(title='Gender'),
                   yaxis =dict(title='Level of Education'),
                   width=600,
                   height=450,
                   template='plotly_white')

fig = go.Figure(data = data, layout = layout)
iplot(fig)

18 子图make_subplots

18.1 子图之线形图

In 33:

# 子图设置
fig = make_subplots(rows = 1, # 行列设置
                    cols = 2,
                    shared_yaxes = True,  # 共享y轴
                    subplot_titles = ("2015-2019", "2020-2024"))  # 子标题设置

# 添加2个轨迹数据
fig.add_trace(go.Scatter(x = df1['date'][0:5],  # xy数据
                         y = df1['students'][0:5],
                         mode = 'lines',  # 模式：线形
                         showlegend = False,  # 不显示legend
                         name = 'students15-19', # 标题
                         line = dict(color = '#18FF01',  # 线属性
                                     width = 3,
                                     dash = 'dashdot')),
              row=1, col=1)
                                      
fig.add_trace(go.Scatter(x = df1['date'][5:10],
                         y = df1['students'][5:10],
                         mode = 'lines',
                         showlegend = False,
                         name = 'students20-24',
                         line = dict(color = '#01AAFF',
                                     width = 3,
                                     dash = 'dash')),
              row=1, col=2)

# 子图中y轴名称设置
fig.update_yaxes(title_text = "Students", row=1, col=1)
fig.update_yaxes(title_text = "Students", row=1, col=2)

# 布局设置
fig.update_layout(title=dict(text ='Number of Students by Years',
                             y = 0.9,
                             x = 0.5,
                             xanchor = 'center',
                             yanchor = 'top'),
                  template = 'plotly_dark')  # 主题

# iplot(fig)
fig.show()

18.2 子图之箱型图

绘制多个箱型图：

In 34:

data = [go.Box(x =df['reading score'],
               showlegend=True,
               name = 'Reading Score'),
        go.Box(x=df['writing score'],
               showlegend=True,
               name = 'Writing Score'),
        go.Box(x=df['math score'],
               showlegend=True,
               name = 'Math Score')]

layout = go.Layout(title={'text': "Scores",
                          'y':0.9,
                          'x':0.5,
                          'xanchor': 'center',
                          'yanchor': 'top'},
                   width = 700,
                   height=450,
                   #template='plotly_dark'
                  )

fig = go.Figure(data = data, layout = layout)
fig.show()

18.3 不同图形的子图

In 35:

colors = ['#4BA7CF','#CF5B4B','#B764D6','#E3885B','#5BE3E1']

fig = make_subplots(rows=1,cols=2,  # 子图1*2
                    subplot_titles=('Countplot','Percentages'), # 子图标题
                    specs=[[{"type": "xy"},  # 每个子图的类型
                            {'type':'domain'}]])
# 子图1：柱状图
fig.add_trace(go.Bar( y = df['race/ethnicity'].value_counts().values.tolist(), 
                      x = df['race/ethnicity'].value_counts().index, 
                      text=df['race/ethnicity'].value_counts().values.tolist(),  # 显示的数据
                      textfont=dict(size = 18, color = 'white'),
                      name='race/ethnicity',
                      textposition = 'auto',
                      showlegend=False,
                      marker=dict(color = colors)),
              row = 1, col = 1)

# 子图2：饼图
fig.add_trace(go.Pie(labels=df['race/ethnicity'].value_counts().keys(),
                     values=df['race/ethnicity'].value_counts().values,
                     textfont = dict(size = 18,
                                     color = 'white'),
                     textposition='auto',
                     showlegend = False,
                     name = 'race/ethnicity',
                     marker=dict(colors = colors)),
              row = 1, col = 2)

fig.update_layout(title={'text': 'Race/Ethnicity',
                         'y':0.9,
                         'x':0.5,
                         'xanchor': 'center',
                         'yanchor': 'top'},
                  template='plotly_dark'
                 )

fig.show()