#!/usr/bin/env python
# coding: utf-8
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# Bokeh Tutorial
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# 00. Introduction and Setup
# # Tutorial Overview
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# The tutorial is broken into several sections, which are each presented in their own notebook:
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# 1. [Basic Plotting](01%20-%20Basic%20Plotting.ipynb)
# 2. [Styling and Theming](02%20-%20Styling%20and%20Theming.ipynb)
# 3. [Data Sources and Transformations](03%20-%20Data%20Sources%20and%20Transformations.ipynb)
# 4. [Adding Annotations](04%20-%20Adding%20Annotations.ipynb)
# 5. [Presentation and Layouts](05%20-%20Presentation%20Layouts.ipynb)
# 6. [Linking and Interactions](06%20-%20Linking%20and%20Interactions.ipynb)
# 7. [Bar and Categorical Data Plots](07%20-%20Bar%20and%20Categorical%20Data%20Plots.ipynb)
# 8. [Graph and Network Plots](08%20-%20Graph%20and%20Network%20Plots.ipynb)
# 9. [Geographic Plots](09%20-%20Geographic%20Plots.ipynb)
# 10. [Exporting and Embedding](10%20-%20Exporting%20and%20Embedding.ipynb)
# 11. [Running Bokeh Applications](11%20-%20Running%20Bokeh%20Applications.ipynb)
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# As well as some extra topic appendices:
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# A1. [Models and Primitives](A1%20-%20Models%20and%20Primitives.ipynb)
# A2. [Visualizing Big Data with Datashader](A2%20-%20Visualizing%20Big%20Data%20with%20Datashader.ipynb)
# A3. [High-Level Charting with Holoviews](A3%20-%20High-Level%20Charting%20with%20Holoviews.ipynb)
# A4. [Additional Resources](A4%20-%20Additional%20Resources.ipynb)
# ## What is Bokeh
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# Bokeh is an interactive visualization library that targets modern web browsers for presentation. It is good for:
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# * 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...)
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# And most importantly:
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# ## NO JAVASCRIPT REQUIRED
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# Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
# ## What can I *do* with Bokeh
# In[ ]:
# Standard imports
from bokeh.io import output_notebook, show
output_notebook()
# In[ ]:
# Plot a complex chart with interactive 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(by=['cyl', 'mfr'])
source = ColumnDataSource(group)
p = figure(width=800, 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="darkgrey", hover_fill_color=index_cmap)
p.add_tools(HoverTool(tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")]))
show(p)
# In[ ]:
# Create and deploy interactive data applications
from IPython.display import IFrame
IFrame('https://demo.bokeh.org/sliders', width=900, height=500)
# # Getting set up
# In[ ]:
from IPython.core.display import Markdown
Markdown(open("README.md").read())
# ### Setup-test, run the next cell. Hopefully you should see output that looks something like this:
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# IPython - 7.9.0
# Pandas - 0.25.2
# Bokeh - 1.4.0
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# If this isn't working for you, see the [`README.md`](README.md) in this directory.
# In[ ]:
from IPython import __version__ as ipython_version
from pandas import __version__ as pandas_version
from bokeh import __version__ as bokeh_version
print("IPython - %s" % ipython_version)
print("Pandas - %s" % pandas_version)
print("Bokeh - %s" % bokeh_version)
# # Next Section
# Click on this link to go to the next notebook: [01 - Basic Plotting](01%20-%20Basic%20Plotting.ipynb)
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