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<h1>BEA API</h1>
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<span>Updated <time datetime="2026-01-12">Jan 2026</time></span>
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<h2>Bureau of Economic Analysis (BEA) API with Python</h2>
<p>The <a href="https://www.bea.gov/resources/for-developers">BEA API</a> provides access to national accounts data including GDP, consumer spending, and industry statistics. This tutorial demonstrates how to use Python to retrieve and analyze data from the BEA API.</p>
<p>This notebook offers two examples: the first fetches NIPA table data to calculate consumer spending growth by category, and the second shows how to navigate API metadata to find dataset parameters.</p>
<hr class="section-bar accent-orange">
<h3>Background</h3>
<h4>BEA</h4>
<p>The Bureau of Economic Analysis is part of the U.S. Department of Commerce. BEA produces the <a href="https://www.bea.gov/national-data">National Income and Product Accounts</a> (NIPA)—the official GDP estimates and related tables that break down the economy by spending category, industry, and income type. You can read more about BEA <a href="https://www.bea.gov/about">here</a>.</p>
<h4>API Registration</h4>
<p>To use the BEA API, you need to <a href="https://apps.bea.gov/API/signup/">register for a free API key</a>. The examples below assume the API key is stored in a separate config file (see the <a href="getstarted.html">Getting Started guide</a> for details).</p>
<h4>Python</h4>
<p>The examples use Python 3.x with the requests and pandas packages.</p>
<hr class="section-bar accent-orange">
<h3>Example 1: Fetch NIPA Table</h3>
<p>This example requests NIPA table 2.3.6, which breaks down real Personal Consumption Expenditures (PCE) by major product type: services, nondurable goods, and durable goods. PCE is the largest component of GDP, so understanding what's driving consumer spending growth is a common analytical task.</p>
<span class="step-label accent-orange">Import Libraries</span>
<p>In[1]:</p>
<pre><code class="python">import requests
import pandas as pd
from config import bea_key as api_key # File with API key</code></pre>
<span class="step-label accent-orange">Request Data</span>
<p>The <code>requests</code> library accepts a <code>params</code> dictionary that it encodes into the URL query string. This is cleaner than concatenating URL fragments by hand.</p>
<p>In[2]:</p>
<pre><code class="python">url = 'https://apps.bea.gov/api/data/'
params = {
'UserID': api_key,
'method': 'GetData',
'datasetname': 'NIPA',
'TableName': 'T20306', # Real PCE by Major Type of Product (Table 2.3.6)
'Frequency': 'Q',
'Year': ','.join(map(str, range(2021, 2026))),
'ResultFormat': 'json'
}
r = requests.get(url, params=params)</code></pre>
<span class="step-label accent-orange">Process the Data</span>
<p>The API returns JSON with one row per series per quarter. We parse it into a dataframe, clean the values (BEA includes commas in numbers), and pivot so each series code becomes a column.</p>
<p>In[3]:</p>
<pre><code class="python"># Parse API response into a dataframe
df = pd.DataFrame(r.json()['BEAAPI']['Results']['Data'])
df['Value'] = df.DataValue.str.replace(',', '').astype(float)
df['Date'] = pd.to_datetime(df.TimePeriod, format='mixed')
# Pivot so each series code is a column
data = df.set_index(['Date', 'SeriesCode'])['Value'].unstack()</code></pre>
<p>The series codes come from the table's column headers: <code>DPCERX</code> is total real PCE, <code>DSERRX</code> is services, <code>DNDGRX</code> is nondurable goods, and <code>DDURRX</code> is durable goods. You can find these codes in the API response's <code>SeriesCode</code> field or on the <a href="https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey">BEA interactive tables</a>.</p>
<span class="step-label accent-orange">Calculate Contributions</span>
<p>To understand what's driving consumer spending, we calculate how much each category contributed to the total growth rate. First, we annualize the quarterly growth rate—raising <code>(1 + quarterly rate)</code> to the 4th power converts it to an annual pace. Then, each category's contribution equals its share of the quarterly change times the total growth rate.</p>
<p>In[4]:</p>
<pre><code class="python"># Annualize quarterly growth: (1 + q/q rate)^4 - 1
pce_growth = (((data['DPCERX'].pct_change() + 1) ** 4) - 1) * 100
# Map series codes to readable names
categories = {
'DSERRX': 'Services',
'DNDGRX': 'Nondurable Goods',
'DDURRX': 'Durable Goods'
}
# Each category's share of the quarterly change × total growth rate
shares = data[categories].diff().div(data['DPCERX'].diff(), axis=0)
contributions = (shares.multiply(pce_growth, axis=0)
.dropna().loc['2022':]
.rename(categories, axis=1))
contributions.index.name = ''</code></pre>
<span class="step-label accent-orange">Visualize Results</span>
<p>A stacked bar chart shows the contribution of each category to overall consumer spending growth.</p>
<p>In[5]:</p>
<pre><code class="python"># Create chart
ax = (contributions.plot(kind='bar', stacked=True, figsize=(6.7, 4),
rot=0, color=['mediumblue', 'deepskyblue', 'darkorange'],
width=0.8, zorder=3))
ax.legend(ncols=3, loc='upper center')
ax.set_ylim(-2, 6)
ax.axhline(0, lw=0.5, color='gray', zorder=0)
ax.grid(axis='y', zorder=0, color='lightgray')
ax.set_xticklabels([f'Q1\n{i.year}' if i.month == 1 else f'Q{(i.month+2)/3:.0f}'
for i in contributions.index])
title = 'Contribution to Real Consumer Spending Growth, by Category'
subtitle = 'quarterly change, annualized, in percent'
ax.text(-0.02, 1.09, title, transform=ax.transAxes, fontsize=12);
ax.text(-0.01, 1.03, subtitle, transform=ax.transAxes, fontsize=9, style='italic');
footer = 'Source: Bureau of Economic Analysis, NIPA Table 2.3.6'
ax.text(-0.03, -0.2, footer, transform=ax.transAxes, fontsize=10);</code></pre>
<p>Out[5]:</p>
<picture><source srcset="images/bea_ex1.webp" type="image/webp"><img decoding="async" src="images/bea_ex1.png" alt="Consumer Spending Growth Chart" loading="lazy" width="584" height="442"/></picture>
<hr class="section-bar accent-orange">
<h3>Example 2: Collect API Parameters</h3>
<p>This example shows how to navigate API metadata to find dataset parameters. Using the metadata, we can discover the codes for tables and industries we want to request.</p>
<span class="step-label accent-orange">Fetch Table List</span>
<p>Request the list of available tables in the GDPbyIndustry dataset. Table 25 (Composition of Gross Output) is what we want for this example.</p>
<p>In[6]:</p>
<pre><code class="python">url = 'https://apps.bea.gov/api/data/'
params = {
'UserID': api_key,
'method': 'GetParameterValues',
'DataSetName': 'GDPbyIndustry',
'ParameterName': 'TableID',
'ResultFormat': 'json'
}
r = requests.get(url, params=params)
# Show the results as a table
pd.DataFrame(r.json()['BEAAPI']['Results']['ParamValue']).set_index('Key').head()</code></pre>
<p>Out[6]:</p>
<table class="dataframe">
<thead>
<tr>
<th></th>
<th>Desc</th>
</tr>
<tr>
<th>Key</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>Value Added by Industry (A) (Q)</td>
</tr>
<tr>
<th>5</th>
<td>Value added by Industry as a Percentage of Gross Domestic Product (A) (Q)</td>
</tr>
<tr>
<th>6</th>
<td>Components of Value Added by Industry (A)</td>
</tr>
<tr>
<th>7</th>
<td>Components of Value Added by Industry as a Percentage of Value Added (A)</td>
</tr>
<tr>
<th>8</th>
<td>Chain-Type Quantity Indexes for Value Added by Industry (A) (Q)</td>
</tr>
</tbody>
</table>
<span class="step-label accent-orange">Fetch Industry List</span>
<p>Request the list of industry codes. Industry code 23 is the Construction industry.</p>
<p>In[7]:</p>
<pre><code class="python"># Reuse the same params, just change which parameter we're looking up
params['ParameterName'] = 'Industry'
r = requests.get(url, params=params).json()
# Show the results as a table
pd.DataFrame(r['BEAAPI']['Results']['ParamValue']).set_index('Key').head(10)</code></pre>
<p>Out[7]:</p>
<table class="dataframe">
<thead>
<tr>
<th></th>
<th>Desc</th>
</tr>
<tr>
<th>Key</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>11</th>
<td>Agriculture, forestry, fishing, and hunting (A,Q)</td>
</tr>
<tr>
<th>111CA</th>
<td>Farms (A,Q)</td>
</tr>
<tr>
<th>113FF</th>
<td>Forestry, fishing, and related activities (A,Q)</td>
</tr>
<tr>
<th>21</th>
<td>Mining (A,Q)</td>
</tr>
<tr>
<th>211</th>
<td>Oil and gas extraction (A,Q)</td>
</tr>
<tr>
<th>212</th>
<td>Mining, except oil and gas (A,Q)</td>
</tr>
<tr>
<th>213</th>
<td>Support activities for mining (A,Q)</td>
</tr>
<tr>
<th>22</th>
<td>Utilities (A,Q)</td>
</tr>
<tr>
<th>23</th>
<td>Construction (A,Q)</td>
</tr>
<tr>
<th>311FT</th>
<td>Food and beverage and tobacco products (A,Q)</td>
</tr>
</tbody>
</table>
<span class="step-label accent-orange">Fetch Industry Data</span>
<p>Using the parameters discovered above, fetch table 25 for industry 23 (Construction). The results are organized into a pandas dataframe.</p>
<p>In[8]:</p>
<pre><code class="python"># Now fetch actual data using the codes we discovered above
params = {
'UserID': api_key,
'method': 'GetData',
'DataSetName': 'GDPbyIndustry',
'TableId': 25, # Composition of Gross Output
'Industry': 23, # Construction
'Frequency': 'A',
'Year': 'ALL',
'ResultFormat': 'json'
}
r = requests.get(url, params=params)
df = pd.DataFrame(r.json()['BEAAPI']['Results'][0]['Data'])
df = df.replace('Construction', 'Gross Output')
df['DataValue'] = df['DataValue'].str.replace(',', '') # strip commas
df = df.set_index([pd.to_datetime(df['Year']),
'IndustrYDescription'])['DataValue'].unstack(1) # note: capital Y is a BEA quirk
df = df.apply(pd.to_numeric)
df.tail()</code></pre>
<p>Out[8]:</p>
<table class="dataframe wide">
<thead>
<tr>
<th></th>
<th>Compensation of employees</th>
<th>Energy inputs</th>
<th>Gross Output</th>
<th>Gross operating surplus</th>
<th>Intermediate inputs</th>
<th>Materials inputs</th>
<th>Purchased-services inputs</th>
<th>Taxes less subsidies</th>
<th>Value added</th>
</tr>
<tr>
<th>Year</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020</th>
<td>597.8</td>
<td>30.3</td>
<td>1804.4</td>
<td>415.3</td>
<td>845.7</td>
<td>636.6</td>
<td>178.8</td>
<td>-54.4</td>
<td>958.7</td>
</tr>
<tr>
<th>2021</th>
<td>636.9</td>
<td>45.7</td>
<td>1985.6</td>
<td>400.8</td>
<td>973.5</td>
<td>749.7</td>
<td>178.1</td>
<td>-25.6</td>
<td>1012.1</td>
</tr>
<tr>
<th>2022</th>
<td>694.3</td>
<td>50.9</td>
<td>2204.0</td>
<td>404.5</td>
<td>1091.6</td>
<td>840.5</td>
<td>200.3</td>
<td>13.6</td>
<td>1112.4</td>
</tr>
<tr>
<th>2023</th>
<td>745.0</td>
<td>51.0</td>
<td>2389.0</td>
<td>465.4</td>
<td>1164.5</td>
<td>854.9</td>
<td>258.6</td>
<td>14.1</td>
<td>1224.6</td>
</tr>
<tr>
<th>2024</th>
<td>799.3</td>
<td>45.4</td>
<td>2511.5</td>
<td>491.2</td>
<td>1206.1</td>
<td>869.9</td>
<td>290.7</td>
<td>14.9</td>
<td>1305.4</td>
</tr>
</tbody>
</table>
<span class="step-label accent-orange">Visualize Results</span>
<p>Create a chart showing the labor income share of gross value added in the construction industry.</p>
<p>In[9]:</p>
<pre><code class="python"># Labor income share of industry value added
data = (df['Compensation of employees'] / df['Value added']) * 100
data.index.name = ''
title = 'Labor Income Share of Gross Value Added, Construction Industry'
ax = data.plot(color='red', title=title);</code></pre>
<p>Out[9]:</p>
<picture><source srcset="images/bea_ex2.webp" type="image/webp"><img decoding="async" src="images/bea_ex2.png" alt="Labor Income Share Chart" loading="lazy" width="568" height="435"/></picture>
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<h3>Further Resources</h3>
<ul>
<li><a href="https://www.bea.gov/resources/for-developers">BEA API Documentation</a> — endpoints, parameters, and usage guide</li>
<li><a href="https://apps.bea.gov/API/signup/">Register for API Key</a> — free registration required for all requests</li>
<li><a href="https://www.bea.gov/resources/methodologies/nipa-handbook">NIPA Handbook</a> — methodology behind the national accounts tables</li>
<li><a href="blsapi.html">BLS API Guide</a> — employment and inflation data</li>
<li><a href="censusapi.html">Census API Guide</a> — manufacturing and trade data</li>
</ul>
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