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‎content/posts/170524-bar-charts-bokeh.markdown

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@@ -21,23 +21,27 @@ but Python 3 is strongly recommended for new applications. In addition
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to Python throughout this tutorial we will also use the following
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[application dependencies](/application-dependencies.html):
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* [Flask](/flask.html) web framework, 0.12
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* [Bokeh](/bokeh.html) data visualization library, version 0.12.5
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* [Flask](/flask.html) web framework,
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[version 0.12.2](https://github.com/pallets/flask/releases/tag/0.12.2)
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* [Bokeh](/bokeh.html) data visualization library,
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[version 0.12.5](https://github.com/bokeh/bokeh/releases/tag/0.12.5)
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* [pandas](/pandas.html) data structures and analysis library,
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version 0.20.1
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[version 0.20.1](https://github.com/pandas-dev/pandas/releases/tag/v0.20.1)
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* [pip](https://pip.pypa.io/en/stable/) and
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[virtualenv](https://virtualenv.pypa.io/en/latest/), which come
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packaged with Python 3, to install and isolate the Flask and Bokeh
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libraries from any other Python projects you might be working on
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packaged with Python 3, to install and isolate the Flask, Bokeh,
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and pandas libraries from any other Python projects you might be
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working on
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If you need help getting your
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[development environment](/development-environments.html) configured
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before running this code, take a look at
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[this guide for setting up Python 3 and Flask on Ubuntu 16.04 LTS](/blog/python-3-flask-green-unicorn-ubuntu-1604-xenial-xerus.html)
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All code in this blog post is available open source under the MIT license
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on GitHub under the blog-code-examples repository. Use it and abuse it
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as you like for your own applications.
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on GitHub under the
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[bar-charts-bokeh-flask-python-3 directory of the blog-code-examples repository](https://github.com/fullstackpython/blog-code-examples).
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Use the source code and abuse it as you like for your own applications.
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## Installing Bokeh and Flask
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chart to the rendered page.
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`app.py`:
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```python
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from flask import Flask, render_template
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app = Flask(__name__)
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@app.route("/<int:bars_count>/")
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def chart(bars_count):
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return render_template("chart.html", bars_count=bars_count)
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if __name__ == "__main__":
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app.run(debug=True)
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```
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...explain code...
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`templates/chart.html`:
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```
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<!DOCTYPE html>
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<html>
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<head>
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<title>Bar charts with Bokeh!</title>
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</head>
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<body>
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<h1>Bugs found over the past {{ bars_count }} days</h1>
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</body>
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</html>
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```
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Short explanation of above HTML.
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```
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$(barchart) python app.py
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```
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Go to "localhost:5000" in your web browser.
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...Screenshots of it working...
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## Generating the Bar Chart
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## What's next?
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Nice work creating a nifty configurable bar chart in Bokeh! Next
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up you can modify the color scheme, change the input data source or try
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to create other types of charts.
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There is a lot more than Bokeh can do, so be sure to check out the
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[official project documentation](http://bokeh.pydata.org/en/latest/) ,
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[GitHub repository](https://github.com/bokeh/bokeh),
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the [Full Stack Python Bokeh page](/bokeh.html) or take a look at
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[other topics on Full Stack Python](/table-of-contents.html).
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Questions? Let me know via
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[a GitHub issue ticket on the Full Stack Python repository](https://github.com/mattmakai/fullstackpython.com/issues),
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on Twitter
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[@fullstackpython](https://twitter.com/fullstackpython)
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or [@mattmakai](https://twitter.com/mattmakai).
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See something wrong in this blog post? Fork
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[this page's source on GitHub](https://github.com/mattmakai/fullstackpython.com/blob/master/content/posts/170524-bar-charts-bokeh.markdown)
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and submit a pull request.
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‎theme/templates/blog/bar-charts-bokeh-flask-python-3.html

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<a href="/ubuntu.html" class="list-group-item smaller-item">Ubuntu</a>
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<a href="/wsgi-servers.html" class="list-group-item smaller-item">WSGI Servers</a>
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<a href="/green-unicorn-gunicorn.html" class="list-group-item smaller-item">Green Unicorn (Gunicorn)</a>
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<a href="http://bokeh.pydata.org/en/latest/" class="list-group-item smaller-item">Official Bokeh Docs {% include "blog/external-link.html" %}</a>

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