Python Bokeh–可视化虹膜数据集
原文:https://www.geesforgeks.org/python-bokeh-visiting-the-iris-dataset/
Bokeh 是一个 Python 交互式数据可视化工具。它使用 HTML 和 JavaScript 来渲染它的图。它以现代网络浏览器为呈现目标，提供优雅、简洁的新颖图形结构和高性能交互性。
Bokeh 可用于可视化鸢尾花数据集。使用plotting模块进行可视化。这里我们将使用博克给我们的虹膜数据集。
正在下载数据集:
要下载 Iris 数据集，请在命令行上运行以下命令:
bokeh sampledata
或者，我们也可以执行以下 Python 代码:
import bokeh
bokeh.sampledata.download()
分析数据集:
在 Bokeh 提供的样本数据中，有一个文件 iris.csv，这就是 iris 数据集。下面是 iris.csv 文件的一瞥:
sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 setosa
4.9 3 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5 3.6 1.4 0.2 setosa
数据集包含 5 个属性，它们是:
- 萼片长厘米
- 萼片_宽度(厘米)
- 花瓣长度(厘米)
- 花瓣宽度(厘米)
- 种有 3 种类型的花种:
- 感觉很好
- 杂色的
- 处女座
每个物种有 50 个记录，条目总数为 150 个。
可视化数据集:
我们将绘制图表来可视化所有 3 个物种的数据聚类。
示例 1 : 这里将绘制以花瓣长度为 x 轴、花瓣宽度为 y 轴的图形。
- 导入所需的模块:
- 图，输出文件和显示来自 bokeh .标绘
- 来自 bokeh.sampledata.iris 的花
- 用标题实例化一个图形对象。
- 给 x 轴和 y 轴命名。
- 绘制所有 3 个物种的图表。
- 显示模型。
# importing the modules
from bokeh.sampledata.iris import flowers
from bokeh.plotting import figure, show, output_file
# file to save the model
output_file("gfg.html")
# instantiating the figure object
graph = figure(title = "Iris Visualization")
# labeling the x-axis and the y-axis
graph.xaxis.axis_label = "Petal Length (in cm)"
graph.yaxis.axis_label = "Petal Width (in cm)"
# plotting for setosa petals
x = flowers[flowers["species"] == "setosa"]["petal_length"]
y = flowers[flowers["species"] == "setosa"]["petal_width"]
color = "blue"
legend_label = "setosa petals"
graph.circle(x, y,
color = color,
legend_label = legend_label)
# plotting for versicolor petals
x = flowers[flowers["species"] == "versicolor"]["petal_length"]
y = flowers[flowers["species"] == "versicolor"]["petal_width"]
color = "yellow"
legend_label = "versicolor petals"
graph.circle(x, y,
color = color,
legend_label = legend_label)
# plotting for virginica petals
x = flowers[flowers["species"] == "virginica"]["petal_length"]
y = flowers[flowers["species"] == "virginica"]["petal_width"]
color = "red"
legend_label = "virginica petals"
graph.circle(x, y,
color = color,
legend_label = legend_label)
# relocating the legend table to
# avoid abstruction of the graph
graph.legend.location = "top_left"
# displaying the model
show(graph)
输出:
示例 2 : 这里将绘制一个散点图，萼片和花瓣都以长度为 x 轴，宽度为 y 轴。
- 导入所需的模块:
- 图，输出文件和显示来自 bokeh .标绘
- 来自 bokeh.sampledata.iris 的花
- 用标题实例化一个图形对象。
- 给 x 轴和 y 轴命名。
- 绘制所有 3 个物种的图表。
- 显示模型。
# importing the modules
from bokeh.sampledata.iris import flowers
from bokeh.plotting import figure, show, output_file
# file to save the model
output_file("gfg.html")
# instantiating the figure object
graph = figure(title = "Iris Visualization")
# labeling the x-axis and the y-axis
graph.xaxis.axis_label = "Length (in cm)"
graph.yaxis.axis_label = "Width (in cm)"
# plotting for setosa petals
x = flowers[flowers["species"] == "setosa"]["petal_length"]
y = flowers[flowers["species"] == "setosa"]["petal_width"]
marker = "circle_cross"
line_color = "blue"
fill_color = "lightblue"
fill_alpha = 0.4
size = 10
legend_label = "setosa petals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# plotting for setosa sepals
x = flowers[flowers["species"] == "setosa"]["sepal_length"]
y = flowers[flowers["species"] == "setosa"]["sepal_width"]
marker = "square_cross"
line_color = "blue"
fill_color = "lightblue"
fill_alpha = 0.4
size = 10
legend_label = "setosa sepals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# plotting for versicolor petals
x = flowers[flowers["species"] == "versicolor"]["petal_length"]
y = flowers[flowers["species"] == "versicolor"]["petal_width"]
marker = "circle_cross"
line_color = "yellow"
fill_color = "lightyellow"
fill_alpha = 0.4
size = 10
legend_label = "versicolor petals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# plotting for versicolor sepals
x = flowers[flowers["species"] == "versicolor"]["sepal_length"]
y = flowers[flowers["species"] == "versicolor"]["sepal_width"]
marker = "square_cross"
line_color = "yellow"
fill_color = "lightyellow"
fill_alpha = 0.4
size = 10
legend_label = "versicolor sepals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# plotting for virginica petals
x = flowers[flowers["species"] == "virginica"]["petal_length"]
y = flowers[flowers["species"] == "virginica"]["petal_width"]
marker = "circle_cross"
line_color = "red"
fill_color = "lightcoral"
fill_alpha = 0.4
size = 10
legend_label = "virginica petals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# plotting for virginica sepals
x = flowers[flowers["species"] == "virginica"]["sepal_length"]
y = flowers[flowers["species"] == "virginica"]["sepal_width"]
marker = "square_cross"
line_color = "red"
fill_color = "lightcoral"
fill_alpha = 0.4
size = 10
legend_label = "virginica sepals"
graph.scatter(x, y,
marker = marker,
line_color = line_color,
fill_color = fill_color,
fill_alpha = fill_alpha,
size = size,
legend_label = legend_label)
# relocating the legend table to
# avoid abstruction of the graph
graph.legend.location = "top_left"
# displaying the model
show(graph)
输出:
