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關(guān)于Python可視化Dash工具之plotly基本圖形示例詳解
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Plotly Express是對(duì) Plotly.py 的高級(jí)封裝，內(nèi)置了大量實(shí)用、現(xiàn)代的繪圖模板，用戶(hù)只需調(diào)用簡(jiǎn)單的API函數(shù)，即可快速生成漂亮的互動(dòng)圖表，可滿(mǎn)足90%以上的應(yīng)用場(chǎng)景。

本文借助Plotly Express提供的幾個(gè)樣例庫(kù)進(jìn)行散點(diǎn)圖、折線(xiàn)圖、餅圖、柱狀圖、氣泡圖、?；鶊D、玫瑰環(huán)圖、堆積圖、二維面積圖、甘特圖等基本圖形的實(shí)現(xiàn)。

代碼示例

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制作標(biāo)準(zhǔn)散點(diǎn)圖
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.show()
 
 
#以鳶尾花類(lèi)型-species作為不同顏色區(qū)分標(biāo)志 color
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")
fig.show()
 
#追加petal_length作為散點(diǎn)大小，變位氣泡圖 size
fig = px.scatter(df, x="sepal_width", y="sepal_length",
         color="species",size='petal_length')
fig.show()
 
#追加petal_width作為額外列，在懸停工具提示中顯示為額外數(shù)據(jù) hover_data
fig = px.scatter(df, x="sepal_width", y="sepal_length",
         color="species", size='petal_length',
         hover_data=['petal_width'])
fig.show()
 
#以鳶尾花類(lèi)型-species區(qū)分散點(diǎn)的形狀 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作為額外列，在懸停工具提示中以粗體顯示。 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()
 
#以鳶尾花類(lèi)型編碼-species_id作為散點(diǎn)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()
 
#追加圖表標(biāo)題 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="鳶尾花分類(lèi)展示")
fig.show()
 
#以鳶尾花類(lèi)型-species作為動(dòng)畫(huà)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="鳶尾花分類(lèi)展示",
         animation_frame="species")
fig.show()
 
#固定X、Y最大值最小值范圍range_x，range_y，防止動(dòng)畫(huà)播放時(shí)超出數(shù)值顯示
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="鳶尾花分類(lèi)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)
# 顯示中國(guó)的人均壽命
fig = px.line(df, x="year", y="lifeExp", title='中國(guó)人均壽命')
fig.show()
 
# 以不同顏色顯示亞洲各國(guó)的人均壽命
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" 達(dá)到按國(guó)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圖
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()
 
# 氣泡圖
df = px.data.gapminder()
# X軸以對(duì)數(shù)形式展現(xiàn)
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軸以標(biāo)準(zhǔn)形式展現(xiàn)
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()
 
# 餅狀圖
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()
 
# 二維面積圖
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()
 
# 甘特圖
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()
 
# 玫瑰環(huán)圖
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圖
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()
 
# ?；鶊D
tips = px.data.tips()
fig = px.parallel_categories(tips, color="size",
               color_continuous_scale=px.colors.sequential.Inferno)
fig.show()

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