我們最後來講python另外一個非常出色的可視化工具，使用plotly建立出色的互動式圖，最後，不再需要Matplotlib！
Plotly具有三個重要功能：
· 懸停：将滑鼠懸停在圖表上時，将彈出注釋
· 互動性：無需任何其他設定即可使圖表互動(例如，穿越時空的旅程)
· 漂亮的地理空間圖：Plotly具有一些内置的基本地圖繪制功能，但是另外，可以使用mapbox內建來生成驚人的圖表。
點圖
我們通過運作fig = x。(PARAMS)然後調用fig.show()來調用繪圖：
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
散點圖-漫步時光
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
并行類别-一種可視化類别的有趣方式
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!
條形圖—互動式過濾器的示例
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-幸福如何随着時間而變化
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)
結束語
在本文中，我們學習了如何成為真正的Python可視化高手，了解了如何在快速探索方面提高效率，以及在再次召開董事會會議時如何建立更精美的圖表。 還有互動式地圖，這在繪制地理空間資料時特别有用哦。
本文翻譯自Fabian Bosler的文章《Learn how to create beautiful and insightful charts with Python — the Quick, the Pretty, and the Awesome》 參考https://towardsdatascience.com/plotting-with-python-c2561b8c0f1f)
完 謝謝觀看