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‎content/pages/09-data/18-pandas.markdown‎

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title: pandas
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category: page
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slug: pandas
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sortorder: 0918
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toc: False
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sidebartitle: pandas
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meta: The Python Data Analysis Library (pandas) is a data structures and analysis library.
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# pandas
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The [Python Data Analysis Library (pandas)](http://pandas.pydata.org/)
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is a data structures and analysis library.
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<a href="http://pandas.pydata.org/" style="border: none;"><img src="/img/logos/pandas.png" width="100%" alt="Python Data Analysis Library pandas project logo." class="technical-diagram" /></a>
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### pandas resources
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* [Intro to pandas data structures](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/),
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[working with pandas data frames](http://www.gregreda.com/2013/10/26/working-with-pandas-dataframes/)
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and
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[Using pandas on the MovieLens dataset](http://www.gregreda.com/2013/10/26/using-pandas-on-the-movielens-dataset/)
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is a well-written three-part introduction to pandas blog series that
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builds on itself as the reader works from the first through the third
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post.
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* [pandas exercises](https://github.com/guipsamora/pandas_exercises) is
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a GitHub repository with Jupyter Notebooks that let you practice
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sorting, filtering, visualizing, grouping, merging and more with pandas.
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* [Modern pandas](https://tomaugspurger.github.io/modern-1.html) is the
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first part in a well-written seven-part introductory series.
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content/posts/170524-bar-charts-bokeh.markdown renamed to content/posts/170526-bar-charts-bokeh.markdown

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title: Building Bar Charts with Bokeh, Flask and Python 3
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slug: bar-charts-bokeh-flask-python-3
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meta: How to build bar charts with the Bokeh data visualization library, Flask and Pyton 3.
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title: Building Responsive Bar Charts with Bokeh, Flask and Python 3
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slug: responsive-bar-charts-bokeh-flask-python-3
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meta: How to build responsive bar charts with the Bokeh data visualization library, Flask and Pyton 3.
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category: post
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date: 2017-05-24
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modified: 2017-05-24
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headerimage: /img/170524-bar-charts-bokeh-flask/header.jpg
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date: 2017-05-26
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modified: 2017-05-26
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headerimage: /img/170526-bar-charts-bokeh-flask/header.jpg
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headeralt: Python, Flask and Bokeh logos.
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[Bokeh](/bokeh.html) is a powerful open source Python library that allows
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developers to generate JavaScript data visualizations for their web
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applications *without writing any JavaScript*. Let's use the
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applications *without writing any JavaScript*. With Bokeh we can create
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incredibly varied visualizations, or traditional ones like the following
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bar chart.
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<img src="/img/170526-bar-charts-bokeh-flask/bar-chart-64.png" width="100%" class="technical-diagram img-rounded" style="border:1px solid #aaa" alt="Responsive bar chart with 64 bars.">
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Let's use the
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[Flask](/flask.html) [web framework](/web-frameworks.html) with Bokeh to
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create custom bar charts in a Python web app.
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The command prompt will change after activating the virtualenv:
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<img src="/img/170524-bar-charts-bokeh-flask/activate-virtualenv.png" width="100%" class="technical-diagram img-rounded" style="border:1px solid #aaa">
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<img src="/img/170526-bar-charts-bokeh-flask/activate-virtualenv.png" width="100%" class="technical-diagram img-rounded" style="border:1px solid #aaa" alt="Activating our Python virtual environment on the command line.">
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Keep in mind that you need to activate the virtualenv in every new terminal
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window that you want this virtualenv to be used for your project.
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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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[this page's source on GitHub](https://github.com/mattmakai/fullstackpython.com/blob/master/content/posts/170526-bar-charts-bokeh.markdown)
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and submit a pull request.
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‎static/img/170526-bar-charts-bokeh-flask/chart-example-64.png‎

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‎static/img/logos/pandas.png‎

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‎theme/templates/choices/pandas.html‎

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<h3>What else would you like to learn about Python and data?</h3>
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<div class="row">
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<div class="col-md-4">
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<div class="well select-next">
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{% include "choices/buttons/databases.html" %}
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</div>
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</div>
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<div class="col-md-4">
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<div class="well select-next">
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{% include "choices/buttons/no-sql-datastore.html" %}
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</div>
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</div>
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<div class="col-md-4">
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<div class="well select-next">
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{% include "choices/buttons/why-use-python.html" %}
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</div>
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</div>
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</div>

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