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以下文章来源于DataCharm，作者宁海涛

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Python-joypy 制作

Python 制作峰峦图有直接的第三方库joypy进行绘制，该库可以直接通过pip安装。可视化代码如下：

<span>import</span><span> matplotlib.pyplot as plt
plt.rcParams[</span><span>"</span><span>font.family</span><span>"</span>] = [<span>"</span><span>Times New Roman</span><span>"</span><span>]
colors </span>= [<span>"</span><span>#791E94</span><span>"</span>,<span>"</span><span>#58C9B9</span><span>"</span>,<span>"</span><span>#519D9E</span><span>"</span>,<span>"</span><span>#D1B6E1</span><span>"</span><span>]
fig,axs </span>= joypy.joyplot(data_ed, by=<span>"</span><span>source</span><span>"</span>,fill=True, legend=True,alpha=.8<span>,
                         range_style</span>=<span>"</span><span>own</span><span>"</span>,xlabelsize=22,ylabelsize=22<span>,
                         grid</span>=<span>"</span><span>both</span><span>"</span>, linewidth=.8,linecolor=<span>"</span><span>k</span><span>"</span>, figsize=(12,6),color=<span>colors,
                       )ax </span>= plt.gca()<span>#</span><span>设置x刻度为时间形式x = np.arange(6)</span>
xlabel=[<span>"</span><span>8-21</span><span>"</span>,<span>"</span><span>8-28</span><span>"</span>,<span>"</span><span>9-4</span><span>"</span>,<span>"</span><span>9-11</span><span>"</span>,<span>"</span><span>9-18</span><span>"</span>,<span>"</span><span>9-25</span><span>"</span><span>]
ax.set_xlim(left</span>=-.5,right=5.5<span>)
ax.set_xticks(x)ax.set_xticklabels(xlabel)ax.text(.</span>47,1.1,<span>"</span><span>Joyplot plots of media shares (TV, Online News and Google Trends)</span><span>"</span><span>,
        transform </span>= ax.transAxes,ha=<span>"</span><span>center</span><span>"</span>, va=<span>"</span><span>center</span><span>"</span>,fontsize = 25,color=<span>"</span><span>black</span><span>"</span><span>)
ax.text(.</span>5,1.03,<span>"</span><span>Python Joyplot Test</span><span>"</span><span>,
        transform </span>= ax.transAxes,ha=<span>"</span><span>center</span><span>"</span>, va=<span>"</span><span>center</span><span>"</span>,fontsize = 15,color=<span>"</span><span>black</span><span>"</span><span>)
ax.text(.</span>90,-.11,<span>"</span><span>
Visualization by DataCharm</span><span>"</span>,transform =<span> ax.transAxes,
        ha</span>=<span>"</span><span>center</span><span>"</span>, va=<span>"</span><span>center</span><span>"</span>,fontsize = 12,color=<span>"</span><span>black</span><span>"</span><span>)
plt.savefig(r</span><span>"</span><span>F:DataCharmArtist_charts_make_<a href="https://www.gaodaima.com/tag/python" title="查看更多关于python的文章" target="_blank">python</a>_RjoyplotsJoyplot_python.png</span><span>"</span><span>,
            width</span>=7,height=5,dpi=900,bbox_inches=<span>"</span><span>tight</span><span>"</span>)

可视化结果如下：

关于 joypy库其他详细的参数设置，可以去官网(https://github.com/sbebo/joypy) 下载 Joyplot.ipynb 文件查看，最好查看所绘制数据的格式，有助于更好绘制峰峦图。

R-ggridges 绘制

借助于R语言丰富且强大的第三方绘图包，在应对不同类型图表时，机会都会有对应的包进行绘制。本次就使用ggridges包（https://wilkelab.org/ggridges/）进行峰峦图的绘制。官网的例子如下：

ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = Month, fill = stat(x))) +<span>
  geom_density_ridges_gradient(scale </span>= 3, rel_min_height = 0.01, gradient_lwd = 1.) +<span>
  scale_x_continuous(expand </span>= c(0, 0)) +<span>
  scale_y_discrete(expand </span>= expand_scale(mult = c(0.01, 0.25))) +<span>
  scale_fill_viridis_c(name </span>= <span>"</span><span>Temp. [F]</span><span>"</span>, option = <span>"</span><span>C</span><span>"</span>) +<span>
  labs(
    title </span>= <span>"</span><span>Temperatures in Lincoln NE</span><span>"</span><span>,
    subtitle </span>= <span>"</span><span>Mean temperatures (Fahrenheit) by month for 2016</span><span>"</span><span>
  ) </span>+  theme_ridges(font_size = 13, grid = TRUE) +<span> 
  theme(axis.title.y </span>= element_blank())

结果如下：

这里我们没有使用 geom_density_ridges_gradient()进行绘制，使用了 geom_ridgeline() 进行类似于山脊线图的绘制。

绘制代码如下：

<span>library(ggthemes)
library(hrbrthemes)plot </span><- ggplot(all_data, aes(x = date, y = source)) +<span>
  geom_ridgeline(aes(height </span>= value, fill = factor(hurricane)), size = 0.1, scale = 0.8, alpha = 0.8) +<span>
  labs(title </span>= <span>"</span><span>Ridgeline plots of media shares (TV, Online News and Google Trends)</span><span>"</span><span>,
       subtitle </span>= <span>"</span><span>ggridges ridgeline plot test</span><span>"</span><span>,
       caption </span>= <span>"</span><span>Visualization by DataCharm</span><span>"</span><span>,
       y </span>=<span> NULL,
       x </span>= NULL) +<span>
  scale_x_date(expand </span>= c(0,0)) +<span>
  scale_fill_manual(values </span>= c(<span>"</span><span>#791E94</span><span>"</span>,<span>"</span><span>#58C9B9</span><span>"</span>,<span>"</span><span>#D1B6E1</span><span>"</span>,<span>"</span><span>#519D9E</span><span>"</span>),name=<span>"</span><span>Hurricane</span><span>"</span>)+<span>
  theme_ipsum()</span>+<span>
  theme(text </span>= element_text(family = <span>"</span><span>Poppins</span><span>"</span>,face = <span>"</span><span>bold</span><span>"</span><span>),
        axis.text.y </span>= element_text(vjust = -2<span>))
plot</span>

可视化结果如下：

上述所涉及到的函数都是基本，在熟悉ggpot2 绘图体系后可以轻松理解。更多有趣的可视化作品，大家可以去官网查看。

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转载请注明原文链接：Python-joypy和 R-ggridges 峰峦图制作

Python-joypy 制作

R-ggridges 绘制

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