使用 Bokeh 库在 Python 中进行数据可视化

17 Mar 2025 | 5 分钟阅读

Bokeh 是一个 Python 库，用于通过高性能的交互式图表进行数据可视化。它使用 HTML 和 JavaScript 语言创建图表。Bokeh 库的输出可以生成在多个平台上，例如浏览器、HTML、服务器和 notebook。也可以在 Django 和 Flask 应用程序中创建 Bokeh 图表。

Bokeh 库为用户提供了两种可视化接口

models：这是一个低级接口，为应用程序开发人员提供了高度的灵活性。
plotting：这是一个高级接口，用于创建视觉符号。

在本教程中，我们将学习如何使用 Bokeh 库在 Python 中创建不同类型的数据可视化图表。

安装

要安装 Bokeh 库，我们可以使用以下命令

代码 1：创建散点圆标记

我们可以使用 Bokeh 库在图上创建散点圆标记。为此，我们将使用 circle() 函数。

示例

# First, we will import the required modules
from bokeh.plotting import figure as fig
from bokeh.plotting import output_notebook as ON
from bokeh.plotting import show
  
# output to the notebook
ON()
  
# creating figure
plot1 = fig(plot_width = 500, plot_height = 500, title = "Scatter Markers")
  
# adding a circle renderer with size, color and alpha
plot1.circle([1, 2, 3, 4, 5, 6], [2, 1, 6, 8, 0], 
         size = 12, color = "green", alpha = 1)
  
# show the results
show(plot1)

输出

Data Visualization in Python using Bokeh Library

代码 2：创建单条线

我们可以使用 Bokeh 库在 Python 中创建单条线。为此，我们将使用 line() 函数。

示例

# First, we will import the required modules
from bokeh.plotting import figure as fig
from bokeh.plotting import output_notebook as ON
from bokeh.plotting import show
  
# output to the notebook
ON()
  
# creating figure
plot1 = fig(plot_width = 500, plot_height = 500, title = "Scatter Markers")
   
# adding a line renderer
plot1.line([1, 2, 3, 4, 5, 6], [2, 1, 6, 8, 0], 
        line_width = 4, color = "red")
  
# show the results
show(plot1)

输出

代码 3：创建条形图

条形图用于用矩形条表示分类数据。条形的长度与其表示的值成正比。

示例：垂直条形图

# First, we will import the required modules
from bokeh.plotting import figure as fig
from bokeh.plotting import output_file as OF
from bokeh.plotting import show
   
# file to save the model
output_file("jtp.html")
       
# Now, instantiate the figure object
graph1 = fig(title = "Bokeh Bar Graph")
   
# x-coordinates to be plotted
x = [1, 2, 3, 4, 5, 6, 7, 8]
   
# y-coordinates of the top edges
top1 = [5, 1, 4, 3, 2, 7, 6, 8]
   
# width / thickness of the bars 
width1 = 0.7
   
# plot the graph
graph1.vbar(x,
           top = top1,
           width = width1,
           color = "green")
   
# display the model
show(graph1)

输出

示例 2：水平条形图

# First, we will import the required modules
from bokeh.plotting import figure as fig
from bokeh.plotting import output_file as OF
from bokeh.plotting import show
   
# file to save the model
output_file("jtp.html")
       
# Now, instantiate the figure object
graph1 = fig(title = "Bokeh Bar Graph")
   
# x-coordinates to be plotted
x = [1, 2, 3, 4, 5, 6, 7, 8]
   
# y-coordinates of the top edges
right1 = [5, 1, 4, 3, 2, 7, 6, 8]
   
# width / thickness of the bars 
height1 = 0.7
   
# plot the graph
graph1.hbar(x,
           right = right1,
           height = height1,
           color = "green")
   
# display the model
show(graph1)

输出

代码 4：创建箱线图

箱线图用于在图上表示统计数据，有助于总结数据集中多个组的统计特性。

示例

import numpy as nmp
import pandas as pnd

from bokeh.plotting import figure as fig
from bokeh.plotting import show

# generate some time series for six different categories
cats_1 = list("xyzprq")
y_y = nmp.random.randn(2000)
g_1 = nmp.random.choice(cats_1, 2000)
for K, J in enumerate(cats_1):
    y_y[g_1 == l] += K // 2
data_frame = pnd.DataFrame(dict(score = y_y, group = g_1))

# now, we will find the quartiles and IQR for each category
groups1 = data_frame.groupby('group')
q_1 = groups1.quantile(q = 0.25)
q_2 = groups1.quantile(q = 0.5)
q_3 = groups1.quantile(q = 0.75)
iqr = q3 - q1
upper = q3 + 1.5 * iqr
lower = q1 - 1.5 * iqr

# now we will find the outliers for each category
def outliers(group2):
    cat_2 = group2.name
    return group2[(group2.score > upper.loc[cat_2]['score']) | (group2.score < lower.loc[cat_2]['score'])]['score']
out = groups.apply(outliers).dropna()

# here, we will prepare outlier data to plot, we would be needing coordinates for every outlier.
if not out.empty:
    out_x = list(out.index.get_level_values(0))
    out_y = list(out.values)

plot1 = fig(tools = "", background_fill_color = "#efefef", x_range = cats_1, toolbar_location = None)

# if no outliers, we will shrink the lengths of stems to be no longer than the minimums or maximums
q_min = groups1.quantile(q = 0.00)
q_max = groups1.quantile(q = 1.00)
upper.score = [min([x,y]) for (x,y) in zip(list(q_max.loc[:, 'score']), upper.score)]
lower.score = [max([x,y]) for (x,y) in zip(list(q_min.loc[:, 'score']), lower.score)]

# stems
plot1.segment(cats_1, upper.score, cats_1, q_3.score, line_color = "black")
plot1.segment(cats_1, lower.score, cats_1, q_1.score, line_color = "black")

# boxes
plot1.vbar(cats_1, 0.7, q_2.score, q_3.score, fill_color = "#E08E79", line_color = "black")
plot1.vbar(cats_1, 0.7, q_1.score, q_2.score, fill_color = "#3B8686", line_color = "black")

# whiskers (almost-0 height rects simpler than segments)
plot1.rect(cats_1, lower.score, 0.2, 0.01, line_color = "black")
plot1.rect(cats_1, upper.score, 0.2, 0.01, line_color = "black")

# outliers
if not out.empty:
    plot1.circle(outx, outy, size = 6, color = "#F38630", fill_alpha = 0.6)

plot1.xgrid.grid_line_color = None
plot1.ygrid.grid_line_color = "white"
plot1.grid.grid_line_width = 2
plot1.xaxis.major_label_text_font_size = "16px"

show(plot1)

输出

代码 5：创建散点图

散点图用于绘制数据集中两个变量的值。它有助于找出所选两个变量之间的相关性。

示例

# First, we will import the required modules
from bokeh.plotting import figure as fig
from bokeh.plotting import output_file as OF
from bokeh.plotting import show
   
# file for saving the model
OF("jtp.html")
        
# instantiate the figure object
graph1 = fig(title = "Bokeh Scatter Graph")
      
# the points to be plotted on Scatter Plot
x1 = [1.4, 5.1, 5.9, 2.3, 5.6, 8.6, 4.5, 2.1, 3.1, 4.3, 5.5, 4.4, 6.9, 2.1, 4, 5.2, 6.3, 7.2, 7.9, 2]
y1 = [3.4, 2.1, 5.7, 8.5, 4.3, 4.2, 5.7, 6.5, 8.9, 9.1, 1.5, 2.1, 6.8, 1, 6, 5.2, 4.5, 7.4, 7.5, 6.3]
     
# plott the graph
graph1.scatter(x1, y1)
      
# display the model
show(graph1)

输出

结论

在本教程中，我们讨论了使用 Bokeh 库在 Python 中可以创建的各种数据可视化类型，即条形图、箱线图、散点标记、散点图和单条线。

下一主题如何使用 Bokeh 库在 Google 地图上绘制符号

使用 Bokeh 库在 Python 中进行数据可视化

安装

代码 1：创建散点圆标记

代码 2：创建单条线

代码 3：创建条形图

代码 4：创建箱线图

代码 5：创建散点图

结论

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Python 问题

使用 Bokeh 库在 Python 中进行数据可视化

安装

代码 1：创建散点圆标记

代码 2：创建单条线

代码 3：创建条形图

代码 4：创建箱线图

代码 5：创建散点图

结论

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