bblocks-data-importers 0.4.1

Tools to import data from different sources. Part of the bblocks project.

bblocks-data-importers

Seamless Pythonic access to international development data

In any data analysis workflow, the first and often most time-consuming step is accessing the data—especially when it comes from multiple external sources, each with its own formats, APIs, and quirks. And international development data is as messy as it comes.

The bblocks-data-importers package simplifies this process by providing a unified interface to download and work with datasets from major international development institutions such as the World Bank, IMF, UN agencies, WHO, and more. Instead of writing custom code for each source, you get consistent, analysis-ready outputs with minimal setup.

Key features:

• Access trusted global datasets from providers like the World Bank, IMF, WHO and others
• Get structured, tidy outputs ready for analysis in tools like pandas
• Avoid source-specific issues with built-in error handling and validation
• Use a consistent interface across all data sources for simpler, cleaner code

Whether you're building dashboards, developing policy briefs, or running large-scale analysis, bblocks-data-importers removes the friction from the very first step—getting the data—so you can focus on what matters most: understanding it.

Read the documentation for more details.

Installation

The package can be installed in various ways

pip install bblocks-data-importers

Or install the main bblocks package with an extra:

pip install bblocks[data-importers]

Usage

Once installed, using a data importer is straightforward. Each supported data source—such as the World Bank, IMF, or WHO—has its own dedicated importer class with a consistent interface.

Let’s walk through a basic example using the World Economic Outlook (WEO) importer.

Step 1: Know the data you need

Before using an importer, it’s helpful to know what the dataset contains and where it comes from. In this case, the World Economic Outlook (WEO) is a flagship publication from the International Monetary Fund (IMF), released twice a year. It provides macroeconomic data and forecasts for countries and regions across the globe, making it an essential resource for economists, researchers, and policy analysts.

Each bblocks importer includes documentation on the data source, the settings available for the importer (such as filters), and how to use the importer effectively. You can refer to the docstrings or the docs in the next page for guidance on each importer.

Step 2: Import the package

Each dataset importer in bblocks-data-importers has its own dedicated class. To work with World Economic Outlook data, you’ll need to import the corresponding WEO importer:

from bblocks.data_importers import WEO

Step 3: Instantiate the importer

Now create an instance of the importer:

weo = WEO()

At this stage, no data is downloaded yet. Importers are designed to load data lazily, meaning the dataset is only fetched when you explicitly request it—typically using .get_data(). This avoids unnecessary memory usage and ensures your code runs efficiently, especially when working with large or multiple datasets.

Step 4: Fetch the data

Use the get_data method to get all the data available from the WEO report

df = weo.get_data()

# Preview the first few rows
df.head()

# Output:
#       entity_code indicator_code  year  value   unit   indicator_name                    entity_name    ...
# 0     111         NGDP_D          1980  39.372  Index  Gross domestic product, deflator  United States  ...
# 1     111         NGDP_D          1981  43.097  Index  Gross domestic product, deflator  United States  ...
# 2     111         NGDP_D          1982  45.760  Index  Gross domestic product, deflator  United States  ...
# 3     111         NGDP_D          1983  48.312  Index  Gross domestic product, deflator  United States  ...
# 4     111         NGDP_D          1984  50.920  Index  Gross domestic product, deflator  United States  ...

Step 5: Clear the cache (optional)

Importers use caching during a session to avoid unnecessary downloads. To clear the cache manually:

weo.clear_cache()

The cache is automatically cleared when the session ends.

You're now ready to explore global datasets using a clean, consistent interface—no scraping or manual downloads required.

Read the documentation to see all available importers and learn about all the functionality and our design philosophy.

Contributing

Contributions are welcome! Please see the CONTRIBUTING page for details on how to get started, report bugs, fix issues, and submit enhancements.