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<oembed><version>1.0</version><provider_name>ARC</provider_name><provider_url>https://eaa-online.org/arc</provider_url><author_name>MARTIEN LUBBERINK</author_name><author_url>https://eaa-online.org/arc/blog/members/23/</author_url><title>Pandas and Hierarchical Indexing - ARC</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="StojQomi8j"&gt;&lt;a href="https://eaa-online.org/arc/blog/2018/04/28/pandas-and-hierarchical-indexing/"&gt;Pandas and Hierarchical Indexing&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://eaa-online.org/arc/blog/2018/04/28/pandas-and-hierarchical-indexing/embed/#?secret=StojQomi8j" width="600" height="338" title="&#x201C;Pandas and Hierarchical Indexing&#x201D; &#x2014; ARC" data-secret="StojQomi8j" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Pandas' ability to index data offers additional power to the way you work with data. More interesting is pandas' hierarchical indexing feature, it allows you to slice and dice data in convenient ways. &nbsp; Python and pandas allow you to manage data more efficiently and effectively than, say, Stata. One important problem with Stata is [&hellip;]</description><thumbnail_url>https://eaa-online.org/app/uploads/sites/3/2018/05/hqdefault.jpg</thumbnail_url><thumbnail_width>480</thumbnail_width><thumbnail_height>360</thumbnail_height></oembed>
