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<h3>Part 1</h3>
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<p>A <strong>basic example</strong> of retrieving manufacturing data from the M3 survey, processing with pandas, and plotting results.</p>
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<h3>Part 2</h3>
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<p>Retrieve <strong>international trade data</strong> from the Census API to analyze U.S. exports by trading partner.</p>
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<article class="prose">
<div class="tutorial-meta">
<span>Updated <time datetime="2026-01-15">Jan 2026</time></span>
<span class="meta-sep">·</span>
<span class="trail-badge trail-beginner">■ Beginner</span>
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<h2>Census International Trade API with Python</h2>
<p>The <a href="https://www.census.gov/data/developers/data-sets/international-trade.html">Census International Trade API</a> provides access to detailed U.S. import and export data by country, commodity, and time period. This tutorial demonstrates how to retrieve export data and identify top U.S. trading partners.</p>
<hr class="section-bar accent-green">
<h3>Background</h3>
<h4>International Trade Statistics</h4>
<p>The Census Bureau collects data on U.S. merchandise trade through customs declarations. The international trade dataset includes monthly statistics on exports and imports by partner country and product category.</p>
<h4>Key Variables</h4>
<p>The main variables we'll use are:</p>
<ul>
<li><code>CTY_CODE</code> - Country code identifying the trading partner. Codes 1000-8000 represent individual countries (excluding regional aggregates).</li>
<li><code>CTY_NAME</code> - Country name for display purposes.</li>
<li><code>ALL_VAL_MO</code> - Total value of exports for the month in U.S. dollars.</li>
</ul>
<p>The full list of variables is available in the <a href="https://api.census.gov/data/timeseries/intltrade/exports/hs/variables.html">API documentation</a>.</p>
<hr class="section-bar accent-green">
<h3>Example: Top U.S. Export Partners</h3>
<span class="step-label accent-green">Setup</span>
<p>Import the required packages and load your Census API key from a local config file (see the <a href="getstarted.html">Getting Started guide</a> for details). You can register for a free API key at the <a href="https://api.census.gov/data/key_signup.html">Census Bureau website</a>.</p>
<p>In[1]:</p>
<pre><code class="python">import requests
import pandas as pd
import matplotlib.pyplot as plt
import config
key = config.census_key</code></pre>
<span class="step-label accent-green">Request Data from the API</span>
<p>Construct the API request to retrieve export values by country for a specific month. The endpoint <code>intltrade/exports/hs</code> provides Harmonized System trade data.</p>
<p>In[2]:</p>
<pre><code class="python">url = 'https://api.census.gov/data/timeseries/intltrade/exports/hs'
params = {
'get': 'CTY_CODE,CTY_NAME,ALL_VAL_MO',
'key': key,
'time': '2025-10'
}
data = requests.get(url, params=params).json()</code></pre>
<p>The response contains a list of lists, with the first row being column headers:</p>
<p>In[3]:</p>
<pre><code class="python">data[:3]</code></pre>
<p>Out[3]:</p>
<pre>[['CTY_CODE', 'CTY_NAME', 'ALL_VAL_MO', 'time'],
['0001', 'OPEC', '5765070418', '2025-10'],
['0003', 'PACIFIC RIM', '35817684498', '2025-10']]</pre>
<span class="step-label accent-green">Process the Data</span>
<p>Convert to a DataFrame, filter to individual countries (codes 1000-8000), and convert values to numeric billions.</p>
<p>In[4]:</p>
<pre><code class="python">df = pd.DataFrame(data[1:], columns=data[0])
# Some codes are non-numeric (e.g. '-'); keep only numeric codes
df = df.loc[df['CTY_CODE'].str.isdigit()]
df['CTY_CODE'] = df['CTY_CODE'].astype(int)
df = df[(df['CTY_CODE'] >= 1000) & (df['CTY_CODE'] < 8000)]
# Convert to billions
df['ALL_VAL_MO'] = df['ALL_VAL_MO'].astype(float) / 1e9</code></pre>
<span class="step-label accent-green">Select Top Partners</span>
<p>Sort by export value and select the top 20 trading partners.</p>
<p>In[5]:</p>
<pre><code class="python">top20 = (df.sort_values('ALL_VAL_MO', ascending=True)
.tail(20)
.set_index('CTY_NAME')['ALL_VAL_MO'])</code></pre>
<span class="step-label accent-green">Create the Visualization</span>
<p>Plot a horizontal bar chart showing the top export destinations.</p>
<p>In[6]:</p>
<pre><code class="python">fig, ax = plt.subplots(figsize=(8, 8))
top20.plot(kind='barh', ax=ax, color='#4a7c59')
ax.set_xlabel('')
ax.set_ylabel('')
ax.axis('off')
# Add value labels
for i, (country, val) in enumerate(top20.items()):
ax.text(-0.5, i, country, ha='right', va='center', fontsize=10)
ax.text(val + 0.3, i, f'${val:.1f}B', ha='left', va='center', fontsize=10)
ax.set_title('Top 20 U.S. Export Destinations, October 2025\n', fontsize=12)
plt.tight_layout()
plt.show()</code></pre>
<p>Out[6]:</p>
<picture><source srcset="images/census_example2.webp" type="image/webp"><img decoding="async" src="images/census_example2.png" alt="Top 20 U.S. Export Partners" loading="lazy" width="528" height="595"/></picture>
<hr class="section-bar accent-green">
<h3>Further Resources</h3>
<ul>
<li><a href="https://www.census.gov/data/developers/data-sets/international-trade.html">Census International Trade API</a> — main documentation</li>
<li><a href="https://api.census.gov/data/timeseries/intltrade/exports/hs/variables.html">API Variables</a> — full list of available fields</li>
<li><a href="https://www.census.gov/foreign-trade/schedules/b/index.html">Schedule B Codes</a> — product classification codes for detailed commodity analysis</li>
<li><a href="censusapi.html">Census API Part 1</a> — manufacturing data from the M3 survey</li>
</ul>
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<p><time datetime="2026">2026</time>, by Brian Dew</p>
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