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    <description>Recent content in Pint on Ciência Programada</description>
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    <managingEditor>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</managingEditor>
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      <title>Analyzing Olympic Data with Pandas and Plotly</title>
      <link>https://cienciaprogramada.com/2021/08/olympics-data-pandas-plotly/</link>
      <pubDate>Sun, 08 Aug 2021 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
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      <description>The Olympics are coming to an end&amp;hellip; and, besides nostalgia and stories of perseverance, every Olympics leaves behind an enormous amount of data! In this article, we will see how to use Pandas to analyze part of that data, looking at how athletes&amp;rsquo; times have evolved in track and field events.</description>
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      <title>ENEM: The Killer Brick Problem</title>
      <link>https://cienciaprogramada.com/2021/01/enem-killer-brick-problem/</link>
      <pubDate>Fri, 08 Jan 2021 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
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      <description>Is it possible to study for the college entrance exam and still learn Python? Of course! And that is one of the goals of the Brazilian Exam Problems category on this site. In it, we use computational tools to analyze and expand problems from the ENEM, Brazil&amp;rsquo;s national high school exam. In today&amp;rsquo;s article, we analyze a problem from the 2019 exam. And, along the way, we learn to make 3D and 4D(?!) plots with Matplotlib. All because of a killer brick.</description>
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      <title>Studying Our Atmosphere with Python</title>
      <link>https://cienciaprogramada.com/2020/12/studying-atmosphere-python/</link>
      <pubDate>Sat, 05 Dec 2020 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
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      <description>Colorless, made of invisible constituents, and odorless (we hope!). We often forget that we are surrounded by the gases that make up our atmosphere. In this article, we will see how to easily get a list of the main constituents of air with Python and web scraping. We will also do some calculations, such as density and mean molar mass, and, of course, present everything in spectacular plots.</description>
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      <title>Plots with units in Python: pint and matplotlib</title>
      <link>https://cienciaprogramada.com/2020/09/python-plots-pint-matplotlib/</link>
      <pubDate>Sat, 05 Sep 2020 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
      <guid>https://cienciaprogramada.com/2020/09/python-plots-pint-matplotlib/</guid>
      <description>In this article we detail how to get visually elegant plots that correctly show units on their axes. We will combine the pint, numpy and matplotlib libraries.</description>
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      <title>Python, Units, and Beer(?!): The pint Package</title>
      <link>https://cienciaprogramada.com/2020/09/python-units-beer-pint/</link>
      <pubDate>Tue, 01 Sep 2020 00:00:00 +0000</pubDate>
      <author>contato[@]cienciaprogramada.com.br (Francisco Bustamante)</author>
      <guid>https://cienciaprogramada.com/2020/09/python-units-beer-pint/</guid>
      <description>Whenever we work with measurements and experimental data, we need to express our values with their respective units. In this article, we will see a Python library for working with units and what that has to do with beer.</description>
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