我們最后來(lái)講python另外一個(gè)非常出色的可視化工具，使用plotly創(chuàng)建出色的交互式圖，最后，不再需要Matplotlib！ 

Plotly具有三個(gè)重要功能：

· 懸停：將鼠標(biāo)懸停在圖表上時(shí)，將彈出注釋

· 交互性：無(wú)需任何其他設(shè)置即可使圖表互動(dòng)（例如，穿越時(shí)空的旅程）

· 漂亮的地理空間圖：Plotly具有一些內(nèi)置的基本地圖繪制功能，但是另外，可以使用mapbox集成來(lái)生成驚人的圖表。

點(diǎn)圖

我們通過(guò)運(yùn)行fig = x。（PARAMS）然后調(diào)用fig.show（）來(lái)調(diào)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(diǎn)圖-漫步時(shí)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

并行類別-一種可視化類別的有趣方式

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—交互式過(guò)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-幸福如何隨著時(shí)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(jié)束語(yǔ)

在本文中，我們學(xué)習(xí)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(kāi)董事會(huì)會(huì)議時(shí)如何創(chuàng)建更精美的圖表。 還有交互式地圖，這在繪制地理空間數(shù)據(jù)時(shí)特別有用哦。

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)