我们最后来讲python另外一个非常出色的可视化工具，使用plotly创建出色的交互式图，最后，不再需要Matplotlib！
Plotly具有三个重要功能：
· 悬停：将鼠标悬停在图表上时，将弹出注释
· 交互性：无需任何其他设置即可使图表互动(例如，穿越时空的旅程)
· 漂亮的地理空间图：Plotly具有一些内置的基本地图绘制功能，但是另外，可以使用mapbox集成来生成惊人的图表。
点图
我们通过运行fig = x。(PARAMS)然后调用fig.show()来调用绘图：
fig = px.scatter(
data_frame=data[data['Year'] == 2018],
x="Log GDP per capita",
y="Life Ladder",
size="Gapminder Population",
color="Continent",
hover_name="Country name",
size_max=60
)
fig.show()
Plotly scatter plot, plotting Log GDP per capita against Life Ladder, where color indicates continent and size of the marker the population
散点图-漫步时光
fig = px.scatter(
data_frame=data,
x="Log GDP per capita",
y="Life Ladder",
animation_frame="Year",
animation_group="Country name",
size="Gapminder Population",
color="Continent",
hover_name="Country name",
facet_col="Continent",
size_max=45,
category_orders={'Year':list(range(2007,2019))}
)
fig.show()
Visualization of how the plotted data changes over the years
并行类别-一种可视化类别的有趣方式
fig = px.bar(
data,
x="Continent",
y="Gapminder Population",
color="Mean Log GDP per capita",
barmode="stack",
facet_col="Year",
category_orders={"Year": range(2007,2019)},
hover_name='Country name',
hover_data=[
"Mean Log GDP per capita",
"Gapminder Population",
"Life Ladder"
]
)
fig.show()
Seems like not all countries with high life expectations are happy!
条形图—交互式过滤器的示例
fig = px.bar(
data,
x="Continent",
y="Gapminder Population",
color="Mean Log GDP per capita",
barmode="stack",
facet_col="Year",
category_orders={"Year": range(2007,2019)},
hover_name='Country name',
hover_data=[
"Mean Log GDP per capita",
"Gapminder Population",
"Life Ladder"
]
)
fig.show()
Filtering a bar chart is easy. Not surprisingly, South Korea is among the wealthy countries in Asia.
Choropleth plot-幸福如何随着时间而变化
fig = px.choropleth(
data,
locations="ISO3",
color="Life Ladder",
hover_name="Country name",
animation_frame="Year")
fig.show()
Map visualization of how happiness evolves over the years. Syria and Afghanistan are at the very end of the Life Ladder range (unsurprisingly)
结束语
在本文中，我们学习了如何成为真正的Python可视化高手，了解了如何在快速探索方面提高效率，以及在再次召开董事会会议时如何创建更精美的图表。 还有交互式地图，这在绘制地理空间数据时特别有用哦。
本文翻译自Fabian Bosler的文章《Learn how to create beautiful and insightful charts with Python — the Quick, the Pretty, and the Awesome》 参考https://towardsdatascience.com/plotting-with-python-c2561b8c0f1f)
完 谢谢观看