用 Python 绘制动态可视化图表，太酷了！
Python的Plotly图形库，让你可以毫不费力地生成动画图表和交互式图表。安装模块
pip install plotly
可视化动态图
Plotly动画工具仅需一行代码就能让人观看数据随时间的变化情况，如下图所示：
import plotly.express as px
from vega_datasets import data
df = data.disasters()
df = df[df.Year > 1990]
fig = px.bar(df,
y="Entity",
x="Deaths",
animation_frame="Year",
orientation='h',
range_x=[0, df.Deaths.max()],
color="Entity")
# improve aesthetics (size, grids etc.)
fig.update_layout(width=1000,
height=800,
xaxis_showgrid=False,
yaxis_showgrid=False,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
title_text='Evolution of Natural Disasters',
showlegend=False)
fig.update_xaxes(title_text='Number of Deaths')
fig.update_yaxes(title_text='')
fig.show()
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(
df,
x="gdpPercap",
y="lifeExp",
animation_frame="year",
size="pop",
color="continent",
hover_name="country",
log_x=True,
size_max=55,
range_x=[100, 100000],
range_y=[25, 90],
# color_continuous_scale=px.colors.sequential.Emrld
)
fig.update_layout(width=1000,
height=800,
xaxis_showgrid=False,
yaxis_showgrid=False,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
太阳图
sunburst chart）是一种可视化group by语句的好方法。如果你想通过一个或多个类别变量来分解一个给定的量，那就用太阳图吧。group by语句可以通过可视化来更有效地展示。
parents参数）并分配对应的值即可，这在我们案例中即为group by语句的输出。import plotly.graph_objects as go
import plotly.express as px
import numpy as np
import pandas as pd
df = px.data.tips()
fig = go.Figure(go.Sunburst(
labels=["Female", "Male", "Dinner", "Lunch", 'Dinner ', 'Lunch '],
parents=["", "", "Female", "Female", 'Male', 'Male'],
values=np.append(
df.groupby('sex').tip.mean().values,
df.groupby(['sex', 'time']).tip.mean().values),
marker=dict(colors=px.colors.sequential.Emrld)),
layout=go.Layout(paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0),
title_text='Tipping Habbits Per Gender, Time and Day')
fig.show()
group by语句的值import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import numpy as np
df = px.data.tips()
fig = go.Figure(go.Sunburst(labels=[
"Female", "Male", "Dinner", "Lunch", 'Dinner ', 'Lunch ', 'Fri', 'Sat',
'Sun', 'Thu', 'Fri ', 'Thu ', 'Fri ', 'Sat ', 'Sun ', 'Fri ', 'Thu '
],
parents=[
"", "", "Female", "Female", 'Male', 'Male',
'Dinner', 'Dinner', 'Dinner', 'Dinner',
'Lunch', 'Lunch', 'Dinner ', 'Dinner ',
'Dinner ', 'Lunch ', 'Lunch '
],
values=np.append(
np.append(
df.groupby('sex').tip.mean().values,
df.groupby(['sex',
'time']).tip.mean().values,
),
df.groupby(['sex', 'time',
'day']).tip.mean().values),
marker=dict(colors=px.colors.sequential.Emrld)),
layout=go.Layout(paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'))
fig.update_layout(margin=dict(t=0, l=0, r=0, b=0),
title_text='Tipping Habbits Per Gender, Time and Day')
fig.show()
指针图
import plotly.graph_objects as go
fig = go.Figure(go.Indicator(
domain = {'x': [0, 1], 'y': [0, 1]},
value = 4.3,
mode = "gauge+number+delta",
title = {'text': "Success Metric"},
delta = {'reference': 3.9},
gauge = {'bar': {'color': "lightgreen"},
'axis': {'range': [None, 5]},
'steps' : [
{'range': [0, 2.5], 'color': "lightgray"},
{'range': [2.5, 4], 'color': "gray"}],
}))
fig.show()
桑基图
import plotly.express as px
from vega_datasets import data
import pandas as pd
df = data.movies()
df = df.dropna()
df['Genre_id'] = df.Major_Genre.factorize()[0]
fig = px.parallel_categories(
df,
dimensions=['MPAA_Rating', 'Creative_Type', 'Major_Genre'],
color="Genre_id",
color_continuous_scale=px.colors.sequential.Emrld,
)
fig.show()
平行坐标图
import plotly.express as px
from vega_datasets import data
import pandas as pd
df = data.movies()
df = df.dropna()
df['Genre_id'] = df.Major_Genre.factorize()[0]
fig = px.parallel_coordinates(
df,
dimensions=[
'IMDB_Rating', 'IMDB_Votes', 'Production_Budget', 'Running_Time_min',
'US_Gross', 'Worldwide_Gross', 'US_DVD_Sales'
],
color='IMDB_Rating',
color_continuous_scale=px.colors.sequential.Emrld)
fig.show()
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