关于Python可视化Dash工具之plotly基本图形示例详解

 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 == 2<a style="color:transparent">来源gao*daima.com搞@代#码网</a>007").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()