数据可视化利器 Bokeh Tutorial
@(数据科学)[小树枝来了, 帮助, Markdown, 数据分析]
Tutorial Overview
The tutorial is broken into several sections, which are each presented in their own notebook:
https://bokeh.pydata.org/en/latest/docs/user_guide/quickstart.html#userguide-quickstart
在线jupyter演示：
https://hub.mybinder.org/user/bokeh-bokeh-notebooks-hlweyrv2/notebooks/tutorial/00%20-%20Introduction%20and%20Setup.ipynb
GitHub：
https://github.com/bokeh/bokeh
What is Bokeh
Bokeh is an interactive visualization library that targets modern web browsers for presentation. It is good for:
- Interactive visualization in modern browsers
- Standalone HTML documents, or server-backed apps
- Expressive and versatile graphics
- Large, dynamic or streaming data
- Easy usage from python (or Scala, or R, or...)
And most importantly:
NO JAVASCRIPT REQUIRED
The goal of Bokeh is to provide elegant, concise construction of novel graphics in the style of D3.js, from the comfort of high level languages such as Python, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
示例
# Standard imports
from bokeh.io import output_notebook, show
output_notebook()
# Plot a complex chart with intearctive hover in a few lines of code
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg_clean as df
from bokeh.transform import factor_cmap
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
group = df.groupby(('cyl', 'mfr'))
source = ColumnDataSource(group)
p = figure(plot_width=800, plot_height=300, title="Mean MPG by # Cylinders and Manufacturer",
x_range=group, toolbar_location=None, tools="")
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
p.xaxis.major_label_orientation = 1.2
index_cmap = factor_cmap('cyl_mfr', palette=['#2b83ba', '#abdda4', '#ffffbf', '#fdae61', '#d7191c'],
factors=sorted(df.cyl.unique()), end=1)
p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=source,
line_color="white", fill_color=index_cmap,
hover_line_color="black", hover_fill_color=index_cmap)
p.add_tools(HoverTool(tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")]))
show(p)
