关于Python可视化Dash工具之plotly基本图形示例详解

Plotly Express是对 Plotly.py 的高级封装，内置了大量实用、现代的绘图模板，用户只需调用简单的API函数，即可快速生成漂亮的互动图表，可满足90%以上的应用场景。

本文借助Plotly Express提供的几个样例库进行散点图、折线图、饼图、柱状图、气泡图、桑基图、玫瑰环图、堆积图、二维面积图、甘特图等基本图形的实现。

代码示例

import plotly.express as pxdf = px.data.iris()#Index([’sepal_length’, ’sepal_width’, ’petal_length’, ’petal_width’, ’species’,’species_id’],dtype=’object’)# sepal_length sepal_width ... species species_id# 0 5.1 3.5 ... setosa 1# 1 4.9 3.0 ... setosa 1# 2 4.7 3.2 ... setosa 1# .. ... ... ... ... ...# 149 5.9 3.0 ... virginica 3# plotly.express.scatter(data_frame=None, x=None, y=None, # color=None, symbol=None, size=None,# hover_name=None, hover_data=None, custom_data=None, text=None,# facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None,# error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,# animation_frame=None, animation_group=None,# category_orders=None, labels=None, orientation=None,# color_discrete_sequence=None, color_discrete_map=None, color_continuous_scale=None, # range_color=None, color_continuous_midpoint=None,# symbol_sequence=None, symbol_map=None, opacity=None, # size_max=None, marginal_x=None, marginal_y=None,# trendline=None, trendline_color_override=None, # log_x=False, log_y=False, range_x=None, range_y=None,# render_mode=’auto’, title=None, template=None, width=None, height=None)# 以sepal_width，sepal_length制作标准散点图fig = px.scatter(df, x='sepal_width', y='sepal_length')fig.show() #以鸢尾花类型-species作为不同颜色区分标志 colorfig = px.scatter(df, x='sepal_width', y='sepal_length', color='species')fig.show() #追加petal_length作为散点大小，变位气泡图 sizefig = px.scatter(df, x='sepal_width', y='sepal_length', color='species',size=’petal_length’)fig.show() #追加petal_width作为额外列，在悬停工具提示中显示为额外数据 hover_datafig = px.scatter(df, x='sepal_width', y='sepal_length', color='species', size=’petal_length’, hover_data=[’petal_width’])fig.show() #以鸢尾花类型-species区分散点的形状 symbolfig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’])fig.show() #追加petal_width作为额外列，在悬停工具提示中以粗体显示。 hover_namefig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’], hover_name='species')fig.show() #以鸢尾花类型编码-species_id作为散点的文本值 textfig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’], hover_name='species', text='species_id')fig.show() #追加图表标题 titlefig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’], hover_name='species', text='species_id',title='鸢尾花分类展示')fig.show() #以鸢尾花类型-species作为动画播放模式 animation_framefig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’], hover_name='species', text='species_id',title='鸢尾花分类展示', animation_frame='species')fig.show() #固定X、Y最大值最小值范围range_x，range_y，防止动画播放时超出数值显示fig = px.scatter(df, x='sepal_width', y='sepal_length', symbol='species' ,color='species', size=’petal_length’, hover_data=[’petal_width’], hover_name='species', text='species_id',title='鸢尾花分类展示', animation_frame='species',range_x=[1.5,4.5],range_y=[4,8.5])fig.show() df = px.data.gapminder().query('country==’China’')# Index([’country’, ’continent’, ’year’, ’lifeExp’, ’pop’, ’gdpPercap’, ’iso_alpha’, ’iso_num’],dtype=’object’)# country continent year ... gdpPercap iso_alpha iso_num# 288 China Asia 1952 ... 400.448611 CHN 156# 289 China Asia 1957 ... 575.987001 CHN 156# 290 China Asia 1962 ... 487.674018 CHN 156# plotly.express.line(data_frame=None, x=None, y=None, # line_group=None, color=None, line_dash=None,# hover_name=None, hover_data=None, custom_data=None, text=None,# facet_row=None, facet_col=None, facet_col_wrap=0, # facet_row_spacing=None, facet_col_spacing=None,# error_x=None, error_x_minus=None, error_y=None, error_y_minus=None,# animation_frame=None, animation_group=None,# category_orders=None, labels=None, orientation=None,# color_discrete_sequence=None, color_discrete_map=None,# line_dash_sequence=None, line_dash_map=None,# log_x=False, log_y=False,# range_x=None, range_y=None,# line_shape=None, render_mode=’auto’, title=None, # template=None, width=None, height=None)# 显示中国的人均寿命fig = px.line(df, x='year', y='lifeExp', title=’中国人均寿命’)fig.show() # 以不同颜色显示亚洲各国的人均寿命df = px.data.gapminder().query('continent == ’Asia’')fig = px.line(df, x='year', y='lifeExp', color='country',hover_name='country')fig.show() # line_group='country' 达到按国家去重的目的df = px.data.gapminder().query('continent != ’Asia’') # remove Asia for visibilityfig = px.line(df, x='year', y='lifeExp', color='continent', line_group='country', hover_name='country')fig.show() # bar图df = px.data.gapminder().query('country == ’China’')fig = px.bar(df, x=’year’, y=’lifeExp’)fig.show() df = px.data.gapminder().query('continent == ’Asia’')fig = px.bar(df, x=’year’, y=’lifeExp’,color='country' )fig.show() df = px.data.gapminder().query('country == ’China’')fig = px.bar(df, x=’year’, y=’pop’, hover_data=[’lifeExp’, ’gdpPercap’], color=’lifeExp’, labels={’pop’:’population of China’}, height=400)fig.show() fig = px.bar(df, x=’year’, y=’pop’, hover_data=[’lifeExp’, ’gdpPercap’], color=’pop’, labels={’pop’:’population of China’}, height=400)fig.show() df = px.data.medals_long()# # nation medal count# # 0 South Korea gold 24# # 1 China gold 10# # 2 Canada gold 9# # 3 South Korea silver 13# # 4 China silver 15# # 5 Canada silver 12# # 6 South Korea bronze 11# # 7 China bronze 8# # 8 Canada bronze 12fig = px.bar(df, x='nation', y='count', color='medal', )fig.show() # 气泡图df = px.data.gapminder()# X轴以对数形式展现fig = px.scatter(df.query('year==2007'), x='gdpPercap', y='lifeExp', size='pop', color='continent',hover_name='country', log_x=True, size_max=60)fig.show() # X轴以标准形式展现fig = px.scatter(df.query('year==2007'), x='gdpPercap', y='lifeExp', size='pop', color='continent',hover_name='country', log_x=False, size_max=60)fig.show() # 饼状图px.data.gapminder().query('year == 2007').groupby(’continent’).count()# country year lifeExp pop gdpPercap iso_alpha iso_num# continent# Africa 52 52 52 52 52 52 52# Americas 25 25 25 25 25 25 25# Asia 33 33 33 33 33 33 33# Europe 30 30 30 30 30 30 30# Oceania 2 2 2 2 2 2 2df = px.data.gapminder().query('year == 2007').query('continent == ’Americas’')fig = px.pie(df, values=’pop’, names=’country’, title=’Population of European continent’)fig.show() df.loc[df[’pop’] < 10000000, ’country’] = ’Other countries’fig = px.pie(df, values=’pop’, names=’country’,title=’Population of European continent’, hover_name=’country’,labels=’country’)fig.update_traces(textposition=’inside’, textinfo=’percent+label’)fig.show() df.loc[df[’pop’] < 10000000, ’country’] = ’Other countries’fig = px.pie(df, values=’pop’, names=’country’,title=’Population of European continent’, hover_name=’country’,labels=’country’,color_discrete_sequence=px.colors.sequential.Blues)fig.update_traces(textposition=’inside’, textinfo=’percent+label’)fig.show() # 二维面积图df = px.data.gapminder()fig = px.area(df, x='year', y='pop', color='continent',line_group='country')fig.show() fig = px.area(df, x='year', y='pop', color='continent',line_group='country', color_discrete_sequence=px.colors.sequential.Blues)fig.show() df = px.data.gapminder().query('year == 2007')fig = px.bar(df, x='pop', y='continent', orientation=’h’, hover_name=’country’, text=’country’,color=’continent’)fig.show() # 甘特图import pandas as pddf = pd.DataFrame([ dict(Task='Job A', Start=’2009-01-01’, Finish=’2009-02-28’, Completion_pct=50, Resource='Alex'), dict(Task='Job B', Start=’2009-03-05’, Finish=’2009-04-15’, Completion_pct=25, Resource='Alex'), dict(Task='Job C', Start=’2009-02-20’, Finish=’2009-05-30’, Completion_pct=75, Resource='Max')])fig = px.timeline(df, x_start='Start', x_end='Finish', y='Task', color='Completion_pct')fig.update_yaxes(autorange='reversed')fig.show() fig = px.timeline(df, x_start='Start', x_end='Finish', y='Resource', color='Resource')fig.update_yaxes(autorange='reversed')fig.show() # 玫瑰环图df = px.data.tips()# total_bill tip sex smoker day time size# 0 16.99 1.01 Female No Sun Dinner 2# 1 10.34 1.66 Male No Sun Dinner 3# 2 21.01 3.50 Male No Sun Dinner 3# 3 23.68 3.31 Male No Sun Dinner 2# 4 24.59 3.61 Female No Sun Dinner 4fig = px.sunburst(df, path=[’day’, ’time’, ’sex’], values=’total_bill’)fig.show() import numpy as npdf = px.data.gapminder().query('year == 2007')fig = px.sunburst(df, path=[’continent’, ’country’], values=’pop’, color=’lifeExp’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’, color_continuous_midpoint=np.average(df[’lifeExp’], weights=df[’pop’]))fig.show() df = px.data.gapminder().query('year == 2007')fig = px.sunburst(df, path=[’continent’, ’country’], values=’pop’, color=’pop’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’)fig.show() # treemap图import numpy as npdf = px.data.gapminder().query('year == 2007')df['world'] = 'world' # in order to have a single root nodefig = px.treemap(df, path=[’world’, ’continent’, ’country’], values=’pop’, color=’lifeExp’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’, color_continuous_midpoint=np.average(df[’lifeExp’], weights=df[’pop’]))fig.show() fig = px.treemap(df, path=[’world’, ’continent’, ’country’], values=’pop’, color=’pop’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’, color_continuous_midpoint=np.average(df[’lifeExp’], weights=df[’pop’]))fig.show() fig = px.treemap(df, path=[’world’, ’continent’, ’country’], values=’pop’, color=’lifeExp’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’)fig.show() fig = px.treemap(df, path=[ ’continent’, ’country’], values=’pop’, color=’lifeExp’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’)fig.show() fig = px.treemap(df, path=[ ’country’], values=’pop’, color=’lifeExp’, hover_data=[’iso_alpha’], color_continuous_scale=’RdBu’)fig.show() # 桑基图tips = px.data.tips()fig = px.parallel_categories(tips, color='size', color_continuous_scale=px.colors.sequential.Inferno)fig.show()

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