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<h3>Part 1</h3>
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<p>A <strong>basic example</strong> of retrieving CPI inflation data using the sdmx1 library, converting to pandas, and plotting results.</p>
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<h3>Part 2</h3>
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<p>How to <strong>find datasets and parameters</strong> using dataflow and codelist methods, plus <strong>direct API access</strong> with requests.</p>
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<p>Practical examples: <strong>WEO forecasts, commodity prices</strong>, ECB bond yields, and <strong>bulk downloads</strong> with requests.</p>
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<span>Updated <time datetime="2026-01-11">Jan 2026</time></span>
<span class="meta-sep">·</span>
<span class="trail-badge trail-intermediate">◆ Intermediate</span>
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<h2>IMF API with Python: Finding Datasets and Parameters</h2>
<p>Part 1 showed a basic example of retrieving data. Here in Part 2, we explore how to discover available datasets and find the correct parameter codes using the sdmx1 library's <code>dataflow()</code> and <code>codelist</code> methods.</p>
<hr class="section-bar accent-blue">
<h3>Searching for Datasets</h3>
<p>The <code>dataflow()</code> method returns information about all available datasets. We can search through these to find datasets of interest:</p>
<p>In[1]:</p>
<pre><code class="python">import sdmx
import pandas as pd
IMF_DATA = sdmx.Client('IMF_DATA')
# Get all dataflows
f = IMF_DATA.dataflow()
# Search for datasets containing "Trade"
{i: v for i, v in f.dataflow.items() if 'Trade' in v.name['en']}</code></pre>
<p>Out[1]:</p>
<pre>{'ITG_WCA': <DataflowDefinition IMF.STA:ITG_WCA(2.0.4): International Trade in Goods, World and Country Aggregates>,
'CTOT': <DataflowDefinition IMF.RES:CTOT(5.0.1): Commodity Terms of Trade (CTOT)>,
'ITG': <DataflowDefinition IMF.STA:ITG(4.0.0): International Trade in Goods (ITG)>,
'TEG': <DataflowDefinition IMF.STA:TEG(3.0.2): Trade in Low Carbon Technology Goods (TEG)>,
'ITS': <DataflowDefinition IMF.RES:ITS(3.0.1): International Trade in Services (ITS)>,
'IMTS': <DataflowDefinition IMF.STA:IMTS(1.0.0): International Trade in Goods (by partner country) (IMTS)>}</pre>
<p>The dictionary comprehension filters datasets whose English name contains "Trade". Each result shows the dataset ID (e.g., <code>IMTS</code>) and a description.</p>
<hr class="section-bar accent-blue">
<h3>Getting Dataset Structure</h3>
<p>Once you identify a dataset, retrieve its structure to find the available dimensions. The dimensions determine how you construct the key for your data request:</p>
<p>In[2]:</p>
<pre><code class="python"># Get metadata for 'IMTS'
f = IMF_DATA.dataflow('IMTS')
dsd = f.structure['DSD_IMTS']
dsd.dimensions.components</code></pre>
<p>Out[2]:</p>
<pre>[<Dimension COUNTRY>,
<Dimension INDICATOR>,
<Dimension COUNTERPART_COUNTRY>,
<Dimension FREQUENCY>,
<TimeDimension TIME_PERIOD>]</pre>
<p>This tells us the IMTS dataset has four dimensions plus time: country, indicator, counterpart country (trading partner), and frequency. The key string must specify values for each dimension in this order.</p>
<hr class="section-bar accent-blue">
<h3>Finding Dimension Codes</h3>
<p>Use the <code>codelist</code> attribute to find valid codes for each dimension. Here we look up available indicators:</p>
<p>In[3]:</p>
<pre><code class="python"># Indicator codes
codes = f.codelist.CL_IMTS_INDICATOR
sdmx.to_pandas(codes)</code></pre>
<p>Out[3]:</p>
<pre>CL_IMTS_INDICATOR
XG_FOB_USD Exports of goods, Free on board (FOB), US dollar
MG_FOB_USD Imports of goods, Free on board (FOB), US dollar
MG_CIF_USD Imports of goods, Cost insurance freight (CIF)...
TBG_USD Trade balance goods, US dollar
Name: International Trade in Goods (by partner country) (IMTS) Indicator, dtype: object</pre>
<p>We can also search within a codelist. Here we find the code for "World" in the country list:</p>
<p>In[4]:</p>
<pre><code class="python"># Country codes - search for specific values
codes = f.codelist.CL_IMTS_COUNTRY
res = sdmx.to_pandas(codes)
res.loc[res.str.contains('World')]</code></pre>
<p>Out[4]:</p>
<pre>CL_IMTS_COUNTRY
G001 World
Name: International Trade in Goods (by partner country) (IMTS) Country, dtype: object</pre>
<hr class="section-bar accent-blue">
<h3>Advanced Example: UK Imports from the EU</h3>
<p>Using what we've learned, let's calculate the European Union's share of UK goods imports over time. The key <code>GBR.MG_CIF_USD.G001+G998.M</code> requests UK imports from both World (<code>G001</code>) and EU (<code>G998</code>) at monthly frequency:</p>
<p>In[5]:</p>
<pre><code class="python"># Retrieve data: UK imports from World (G001) and EU (G998)
data_msg = IMF_DATA.data('IMTS', key='GBR.MG_CIF_USD.G001+G998.M')
# Convert to pandas
df = sdmx.to_pandas(data_msg).reset_index()
df = df.set_index(['TIME_PERIOD', 'COUNTERPART_COUNTRY'])['value'].unstack()
df.index = pd.to_datetime(df.index, format='%Y-M%m')
df = df.sort_index()</code></pre>
<p>With both series in our DataFrame, we can calculate the EU share and apply a 12-month moving average to smooth seasonal variation:</p>
<p>In[6]:</p>
<pre><code class="python"># Calculate EU share of UK imports of goods
eu_share = ((df['G998'] / df['G001']) * 100).rolling(12).mean()
# Create a line plot
title = "U.K. imports of goods: European Union share of total"
recent = f"{eu_share.index[-1].strftime('%B %Y')}: {eu_share.iloc[-1]:.1f}%"
ax = eu_share.plot(title=title)
ax = ax.set_xlabel(recent)</code></pre>
<p>Out[6]:</p>
<picture><source srcset="images/uk_imports_eu_share.webp" type="image/webp"><img decoding="async" src="images/uk_imports_eu_share.png" alt="UK Imports EU Share" loading="lazy" width="569" height="454"/></picture>
<hr class="section-bar accent-blue">
<h3>Tips</h3>
<p>A few patterns that make working with the API more efficient:</p>
<ul>
<li><strong>Wildcards</strong>: Leave a key position empty to request all values for that dimension. For example, <code>USA.CPI._T.IX.</code> (trailing dot, no frequency) returns annual, quarterly, and monthly data.</li>
<li><strong>Date precision</strong>: The <code>startPeriod</code> and <code>endPeriod</code> parameters accept month-level strings like <code>'2024-01'</code>, not just years.</li>
<li><strong>Suppress warnings</strong>: Add <code>import logging; logging.getLogger('sdmx').setLevel(logging.ERROR)</code> to silence the <code>xml.Reader</code> diagnostic message.</li>
</ul>
<hr class="section-bar accent-blue">
<h3>Direct API Access with Requests</h3>
<p>The sdmx1 library is a convenience wrapper around standard HTTP calls. If you prefer to avoid the dependency—or want to understand what's happening underneath—you can query the same API directly with <code>requests</code>. Ask for CSV format and you get a pandas-ready response:</p>
<p>In[7]:</p>
<pre><code class="python">import requests
import io
# The same CPI query from Part 1, without sdmx1
url = ("https://api.imf.org/external/sdmx/3.0/data/"
"dataflow/IMF.STA/CPI/~/"
"BRA+CHL+COL.CPI._T.IX.M"
"?c[TIME_PERIOD]=ge:2018-M01")
resp = requests.get(url, headers={"Accept": "text/csv"})
df = pd.read_csv(io.StringIO(resp.text))</code></pre>
<p>Out[7]:</p>
<pre>288 rows, 3 countries
COUNTRY TIME_PERIOD OBS_VALUE
BRA 2018-M01 4930.72
BRA 2018-M02 4946.50
BRA 2018-M03 4950.95
BRA 2018-M04 4961.84</pre>
<p>The URL follows a consistent pattern:</p>
<pre>https://api.imf.org/external/sdmx/3.0/data/dataflow/{agency}/{dataflow}/{version}/{key}
• Agency: IMF.STA (Statistics Dept) or IMF.RES (Research Dept)
• Dataflow: CPI, WEO, BOP, IFS, etc.
• Version: ~ means "latest"
• Key: dimension values separated by dots; + joins multiple values</pre>
<p>From here, the reshaping and calculation are the same as Part 1:</p>
<p>In[8]:</p>
<pre><code class="python"># Reshape and calculate inflation (same steps as Part 1)
result = df.set_index(['TIME_PERIOD', 'COUNTRY'])['OBS_VALUE'].unstack()
result.index = pd.to_datetime(result.index.str.replace('-M', '-'))
result = result.sort_index()
inflation = (result.pct_change(12) * 100).round(1).dropna()
inflation.tail(4)</code></pre>
<p>Out[8]:</p>
<pre>COUNTRY BRA CHL COL
2025-09-01 5.2 4.4 5.2
2025-10-01 4.7 3.4 5.5
2025-11-01 4.5 3.4 5.3
2025-12-01 4.3 3.4 5.1</pre>
<p>A few gotchas to watch for with direct API access:</p>
<ul>
<li><strong>Version wildcard</strong>: Use <code>~</code> for latest version. Using <code>*</code> causes a 500 error.</li>
<li><strong>Time filtering</strong>: Use <code>?c[TIME_PERIOD]=ge:2018-M01</code> for the content constraint. The <code>startPeriod</code> parameter may not be supported on all endpoints.</li>
<li><strong>Country codes</strong>: Must be ISO alpha-3 (e.g., <code>USA</code> not <code>US</code>). Using an invalid code returns 200 with zero data rows—no error.</li>
<li><strong>Monthly dates</strong>: Time periods are formatted <code>2024-M01</code> rather than <code>2024-01</code>, so a <code>.str.replace('-M', '-')</code> is needed before <code>pd.to_datetime()</code>.</li>
</ul>
<p>Part 3 covers practical examples with popular datasets including economic forecasts, commodity prices, and accessing data from other providers.</p>
<hr class="section-bar accent-blue">
<h3>Additional Resources</h3>
<ul>
<li><a href="https://pypi.org/project/sdmx1/">sdmx1 on PyPI</a> - Installation and basic documentation</li>
<li><a href="https://sdmx1.readthedocs.io/">sdmx1 documentation</a> - Full library reference</li>
<li><a href="https://data.imf.org/">IMF Data Portal</a> - Browse available datasets interactively</li>
</ul>
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