特别靠谱先生
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 px
df = 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 color
fig = 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 size
fig = 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_data
fig = 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 symbol
fig = 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_name
fig = 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 text
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")
fig.show()
#追加图表标题 title
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")
fig.show()
#以鸢尾花类型-species作为动画播放模式 animation_frame
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")
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 visibility
fig = 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 12
fig = px.bar(df, x="nation", y="count", color="medal",
title="long-form input")
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 2
df = 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 pd
df = 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 4
fig = px.sunburst(df, path=['day', 'time', 'sex'],
values='total_bill')
fig.show()
import numpy as np
df = 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 np
df = px.data.gapminder().query("year == 2007")
df["world"] = "world" # in order to have a single root node
fig = 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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