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<h1>IMF API</h1>
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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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<span>Updated <time datetime="2026-01-11">Jan 2026</time></span>
<span class="meta-sep">·</span>
<span class="trail-badge trail-intermediate">◆ Intermediate</span>
<span class="meta-sep">·</span>
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<h2>IMF API with Python: A Basic Example</h2>
<p>The <a href="https://data.imf.org/">IMF's data portal</a> provides access to macroeconomic data covering more than 180 countries. Using Python's <a href="https://pypi.org/project/sdmx1/">sdmx1 library</a>, it is straightforward to retrieve data through the IMF's SDMX API.</p>
<p>The example below retrieves monthly Consumer Price Index (CPI) data for Brazil, Chile, and Colombia, calculates year-over-year inflation rates, and plots the results. This demonstrates the core workflow: connecting to the API, building a request key, fetching data, and converting it to a pandas DataFrame for analysis.</p>
<hr class="section-bar accent-blue">
<h3>Setup</h3>
<p>First, install the sdmx1 library if you haven't already:</p>
<pre><code class="bash">pip install sdmx1</code></pre>
<hr class="section-bar accent-blue">
<h3>Import Libraries</h3>
<p>We need pandas for data manipulation and sdmx for accessing the IMF API:</p>
<p>In[1]:</p>
<pre><code class="python">import pandas as pd
import sdmx</code></pre>
<hr class="section-bar accent-blue">
<h3>Set Parameters</h3>
<p>Define the countries, dataset, and series codes. The key string combines these parameters using dots as separators, with multiple countries joined by <code>+</code> signs:</p>
<p>In[2]:</p>
<pre><code class="python"># parameters
countries = ['BRA', 'CHL', 'COL']
classification = 'CPI'
series = '_T.IX.M'
key = f"{'+'.join(countries)}.{classification}.{series}"</code></pre>
<p>The key <code>BRA+CHL+COL.CPI._T.IX.M</code> requests all-items (<code>_T</code>) price index (<code>IX</code>) data at monthly (<code>M</code>) frequency for the three countries.</p>
<hr class="section-bar accent-blue">
<h3>Retrieve Data</h3>
<p>Create an SDMX client for the IMF and fetch the data. The <code>sdmx.Client</code> connects to the IMF's data endpoint, and the <code>.data()</code> method retrieves observations for the specified dataset and key:</p>
<p>In[3]:</p>
<pre><code class="python"># retrieve data
IMF_DATA = sdmx.Client('IMF_DATA')
data_msg = IMF_DATA.data('CPI', key=key,
params={'startPeriod': '2018'})
# convert to pandas
df = sdmx.to_pandas(data_msg).reset_index()
df = df.set_index(['TIME_PERIOD', 'COUNTRY'])['value'].unstack()</code></pre>
<p>The <code>sdmx.to_pandas()</code> function converts the SDMX message into a pandas structure. After reshaping, we have a DataFrame with dates as rows and countries as columns.</p>
<hr class="section-bar accent-blue">
<h3>Calculate Inflation and Plot</h3>
<p>Convert the index to datetime, calculate year-over-year percent changes, and create a line plot:</p>
<p>In[4]:</p>
<pre><code class="python"># Convert index to datetime and sort
df.index = pd.to_datetime(df.index, format='%Y-M%m')
df = df.sort_index()
# Calculate inflation rate and drop empty rows
res = (df.pct_change(12) * 100).round(1).dropna()
# Plot results
res.plot(title='CPI, one-year percent change',
color=['dodgerblue', 'green', 'darkblue'])</code></pre>
<p>Out[4]:</p>
<picture><source srcset="images/imf_cpi_inflation.webp" type="image/webp"><img decoding="async" src="images/imf_cpi_inflation.png" alt="CPI Inflation Chart" loading="lazy" width="569" height="454"/></picture>
<hr class="section-bar accent-blue">
<h3>Key Concepts</h3>
<p>This example demonstrates the core workflow for accessing IMF data with sdmx1:</p>
<ul>
<li><code>sdmx.Client('IMF_DATA')</code> creates a connection to the IMF's SDMX endpoint;</li>
<li>The <code>.data()</code> method retrieves data for a specific dataflow using a key string;</li>
<li><code>sdmx.to_pandas()</code> converts the SDMX message to a pandas DataFrame;</li>
<li>Multiple countries (or other dimension values) are specified using <code>+</code> as a separator.</li>
</ul>
<p>Part 2 covers how to discover available datasets and find the correct parameter codes for your data of interest.</p>
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