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<h1>Treasury API</h1>
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<article class="prose">
<div class="tutorial-meta">
<span>Updated <time datetime="2026-01-11">Jan 2026</time></span>
<span class="meta-sep">·</span>
<span class="trail-badge trail-beginner">■ Beginner</span>
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<h2>US Treasury Fiscal Data API with Python</h2>
<p>The <a href="https://fiscaldata.treasury.gov/api-documentation/">US Treasury Fiscal Data API</a> offers data on federal finances, including revenue, spending, debt, and more. With Python, you can retrieve and analyze this data programmatically.</p>
<p>This example uses the Python <code>requests</code> and <code>pandas</code> packages to retrieve treasury data from the Monthly Treasury Statement and analyze federal revenue composition. If you're new to Python, the <a href="getstarted.html">Setup guide</a> covers setup and the basics.</p>
<hr class="section-bar accent-red">
<h3>Background</h3>
<h4>Treasury Fiscal Data</h4>
<p>The Bureau of the Fiscal Service provides public access to federal financial data through <a href="https://fiscaldata.treasury.gov/">fiscaldata.treasury.gov</a>. The API allows access to datasets including the Monthly Treasury Statement, Daily Treasury Statement, debt data, and more.</p>
<h4>API</h4>
<p>The Fiscal Data API is a RESTful API that returns JSON data. You can filter results, specify date ranges, and paginate through large datasets. No registration or API key is required.</p>
<hr class="section-bar accent-red">
<h3>Example: Federal Revenue by Type</h3>
<p>How has the composition of federal revenue changed recently? Corporate income taxes and customs duties are two categories worth watching—corporate taxes are a large and cyclical revenue source, while customs duties have grown sharply with recent changes in trade policy.</p>
<span class="label step-label accent-red">Import Packages</span>
<p>In[1]:</p>
<pre><code class="python">import requests
import pandas as pd</code></pre>
<span class="label step-label accent-red">Request Data from the API</span>
<p>We request data from table 9 of the Monthly Treasury Statement (MTS), which breaks down federal receipts and outlays by type. The <code>filter</code> parameter uses the API's <code>:in:</code> operator to return only rows matching our categories of interest. You can find the available field names and filter operators in the <a href="https://fiscaldata.treasury.gov/api-documentation/#filters">API documentation</a>.</p>
<p>In[2]:</p>
<pre><code class="python">url = 'https://api.fiscaldata.treasury.gov/services/api/fiscal_service'
url += '/v1/accounting/mts/mts_table_9'
params = {
'filter': 'classification_desc:in:(Corporation Income Taxes,Customs Duties,Total)',
'page[size]': 800
}
r = requests.get(url, params=params)</code></pre>
<span class="label step-label accent-red">Clean the Data</span>
<p>The API returns one row per category per month. We rename the columns for readability and remove line 33, which is total outlays—the table includes both receipts and outlays rows labeled "Total", so we filter by the source line number to keep only receipts.</p>
<p>In[3]:</p>
<pre><code class="python">rn = {'current_month_rcpt_outly_amt': 'Value', 'src_line_nbr': 'Line',
'classification_desc': 'Category'}
df = pd.DataFrame(r.json()['data']).rename(rn, axis=1).query('Line != "33"')
df['Date'] = pd.to_datetime(df['record_date'])
df['Value'] = df['Value'].astype(float)
df = df.sort_values('Date')</code></pre>
<span class="label step-label accent-red">Aggregate to Trailing 12 Months</span>
<p>Monthly fiscal data is seasonal—April is always a big revenue month (tax day), for example. A trailing 12-month sum smooths out this seasonality and shows the annual pace.</p>
<p>In[4]:</p>
<pre><code class="python"># Pivot so each category is a column, convert to trillions
data = (df.set_index(['Date', 'Category'])['Value']
.unstack().astype(float) / 1_000_000)
# Rolling 12-month sum to smooth seasonality
data = data.rolling(12).sum().dropna()</code></pre>
<span class="label step-label accent-red">Plot the Results</span>
<p>Calculate each category as a share of total federal revenue and plot from 2023 onward.</p>
<p>In[5]:</p>
<pre><code class="python">c = ['Corporation Income Taxes', 'Customs Duties']
shares = data[c].divide(data['Total'], axis=0) * 100
shares.index.name = ''
shares.columns.name = ''
title = ('Corporate Income Taxes and Customs Duties\n'
'Share of Total Federal Revenue,\n'
'in percent, trailing 12 months')
shares.loc['2023-01-01':].plot(title=title, figsize=(4.5, 5))</code></pre>
<p>Out[5]:</p>
<picture><source srcset="images/treasury_tax.webp" type="image/webp"><img decoding="async" src="images/treasury_tax.png" alt="Federal Revenue by Type" loading="lazy" width="415" height="501"/></picture>
<hr class="section-bar accent-red">
<h3>Further Resources</h3>
<ul>
<li><a href="https://fiscaldata.treasury.gov/api-documentation/">Fiscal Data API Documentation</a> — endpoints, filtering, and pagination</li>
<li><a href="https://fiscaldata.treasury.gov/datasets/">Available Datasets</a> — browse all Treasury datasets including debt, interest rates, and daily statements</li>
<li><a href="blsapi.html">BLS API Guide</a> — employment and inflation data</li>
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