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python.html
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<!DOCTYPE html>
<html>
<head>
<meta id="meta" name="viewport" content="width=device-width, initial-scale=1.0" />
<title> BD Economics | Python for Macroeconomic Data Analysis </title>
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<meta charset="UTF-8">
<meta name="description" content="Tools for Economists">
<meta name="keywords" content="IMF API, Economics Dashboard, Trade Network, Trade Networks, Macroeconomics Dashboard, Macroeconomic Dashboard, Markets Dashboard, Market Dashboard, U.S. Economy, U.S. Economy Dashboard, US economy, US economy dashboard, US economy charts, US economy charts pdf, NetworkX trade, international trade networks, network analysis of trade, International Monetary Fund, IMF Statistics Department, IMF Application Programming Interface, International Monetary Fund Application Programming Interface, International Monetary Fund API, Current Population Survey, CPS with python, Current Population Survey microdata, Economic microdata">
<meta name="author" content="Brian Dew">
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<body>
<nav>
<ul class="ul_nav" id="menu">
<li class="nav_main"> <a href="index.html">BD Economics</a> </li>
<li><a href="about.html">About</a> </li>
<li><a href="http://briandew.wordpress.com">Blog</a> </li>
<li><a href="python.html" class="active">Tools ↓</a>
<ul class="hidden">
<li><a href="imfapi1.html">IMF API</a></li>
<li><a href="blsapi.html">BLS API</a></li>
<li><a href="cps.html">CPS Microdata</a></li>
</ul>
</li>
<li>
<a href="chartbook.html">Reports ↓</a>
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<li><a href="chartbook.pdf">US Chartbook (PDF)</a></li>
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</ul>
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<header>
<h3>Tools</h3>
</header>
<section>
<article>
<h3> Overview </h3>
<p>Python is easy to read and write and backed by a wonderful community. It is an incredibly useful tool for working with economic data. I’ve written a few examples of how this open-source programming language can be used to work with real-world economic data.</p>
<p>I use the <a href="https://conda.io/docs/user-guide/install/download.html">miniconda distribution</a> of Python, which already includes key packages (such as NumPy, Pandas, SciPy, etc) and tools (Jupyter notebook, Conda).</p>
</article>
<article class="article_links">
<h3> Guides: Python for Economic Analysis </h3>
<a href="imfapi1.html">
<div class="card">
<div class="card_head_blue">
<h2>IMF API</h2>
</div>
<div class="card_tail">
<p>Three-part guide to using the International Monetary Fund's Application Programming Interface to machine-read data.</p>
</div>
</div>
</a>
<a href="https://briandew.wordpress.com/2016/06/15/trade-network-analysis-why-centrality-matters/">
<div class="card">
<div class="card_head_orange">
<h2>NetworkX</h2>
</div>
<div class="card_tail">
<p>Analyze and visualize the trade network for specific goods using a algorithms from the NetworkX package.</p>
</div>
</div>
</a>
<a href="blsapi.html">
<div class="card">
<div class="card_head_green">
<h2>BLS API</h2>
</div>
<div class="card_tail">
<p>A guide to collecting data from the U.S. Bureau of Labor Statistics API using Python's requests package.</p>
</div>
</div>
</a>
</a>
<a href="cps.html">
<div class="card">
<div class="card_head_purple">
<h2>CPS micro</h2>
</div>
<div class="card_tail">
<p>An example of reading U.S. Current Population Survey (CPS) microdata and benchmarking against published data.</p>
</div>
</div>
</a>
</article>
</section>
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<p>March 11, 2018<br> by Brian Dew</p>
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