world-economic-outlook 0.1.1

An IMF World Economic Outlook (WEO) API Client

World Economic Outlook

World Economic Outlook is a Python library for downloading and processing IMF datasets. It offers easy programmatic and CLI access to over 70 IMF datasets, and supports storing data in multiple formats.

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Features

• Download data from over 70 IMF datasets.
• Save data as .csv, .json, and .txt files.
• Push data to SQLite databases.
• ISO alpha-2 and alpha-3 compatible.
• Easy-to-use command line interface (CLI).

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Installation

Requires Python 3.9+.

pip install world-economic-outlook

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Quick Start

Note: This library is robust to the new API changes made by the IMF. Many previous datasets have changed in format and naming convention. For example, the International Financial Statistics (IFS) has been diffused into other datasets.

Programmatic Approach

Datasets are accessed programmatically using <dataset>(*args).

1. Download full datasets and save them locally

This example shows how to download the most recently updated WEO dataset and save it as a .csv.

from world_economic_outlook import weo

weo("*", save_path="weo.csv")

This example shows how to download the Fiscal Monitor (FM) dataset and save it as a .txt.

from world_economic_outlook import fm

fm("*", save_path="fm.txt")

This example shows how to download the Exchange Rates (ER) dataset and save it as a .json.

from world_economic_outlook import er

er("*", save_path="er.json")

This example shows how to download WEO datasets by vintage and save them as a .csv.

from world_economic_outlook import vweo

vweo(["April 2025", "October 2024"], save_path="vweo.csv")

2. Use arguments for more targeted data retrievals

This example shows how to download exchange rate data for a specific country using the Exchange Rates (ER) dataset. Some datasets are large, containing lots of different features, making narrowed down data retrievals useful. Accepted API-specific arguments and definitions can be found from the IMF's Data Explorer page.

from world_economic_outlook import er

records = er(
    isos="AR",                        # Argentina
    indicator="XDC_USD",              # Local currency per dollar (USDARS)
    type_of_transformation="EOP_RT",  # End of period rate
    frequency="M",                    # Monthly frequency
    start_date="2000",                # Since 2000
    use_iso_alpha2=True               # Output iso in ISO alpha-2 format
)

print(records[-1])

Output:

{
    'date': '2025-08-01',
    'frequency': 'M',
    'indicator': 'XDC_USD',
    'iso': 'AR',
    'type_of_transformation': 'EOP_RT',
    'value': 1337.5
}

3. Pushing data to a SQLite database

This example shows how to download trade data for selected countries using the International Trade in Goods (IMTS) dataset. Data are stored in a SQLite database.

from world_economic_outlook import imts

imts(
    isos=["FIN", "SWE", "DNK", "NOR"]
    isos_star=["FIN", "SWE", "DNK", "NOR"]
    indicator=[
        "MG_CIF_USD",        # Imports
        "XG_FOB_USD",        # Exports
        "TBG_USD",           # Trade balance
    ],
    frequency="M",
    database="database.db",
    table="imts"
)

4. Querying data from a SQLite database

This example shows how to query stored trade data from the previous example.

from simple_sqlite3 import Database

with Database("database.db") as db:
    sql = f"""
    SELECT * FROM imts
    WHERE iso = 'FIN'
    AND iso_star = 'SWE'
    AND indicator IN ("MG_CIF_USD", 'XG_FOB_USD', 'TBG_USD')
    """

    records = db.query(sql)

print(records)

Output:

{
    'date': '2025-05-01',
    'frequency': 'M',
    'indicator': 'MG_CIF_USD',
    'iso': 'FIN',
    'iso_star': 'SWE',
    'value': 1153284502.0
}
{
    'date': '2025-05-01',
    'frequency': 'M',
    'indicator': 'XG_FOB_USD',
    'iso': 'FIN',
    'iso_star': 'SWE',
    'value': 849917785.0
 }

 {
    'date': '2025-05-01',
    'frequency': 'M',
    'indicator': 'TBG_USD',
    'iso': 'FIN',
    'iso_star': 'SWE',
    'value': -303366717.0
 }

Command Line Interface (CLI)

The world-economic-outlook library includes a powerful CLI tool for retrieving data, searching IMF datasets, and exploring metadata.

Basic Usage

After installation, open a terminal and use the CLI via the imf command:

>>> imf --help

World Economic Outlook CLI

Usage:
  imf <command> [options]

Commands:
  <dataset>         {weo, er, cpi, ...}
  list              List all datasets
  show              Show dataset info
  search            Search datasets & indicators
  help              Show this help message

Options:
  --help [-h]       Show this help message

Examples of CLI Commands

• list
  List all available datasets.

  >>> imf list
  

• show <dataset>
  Show detailed info for a specific dataset.

  >>> imf show weo
  

• search <term>
  Search datasets and codelists by keywords.

  >>> imf search "inflation"
  

• <dataset>
  Download data from a specific dataset (e.g., weo, er, cpi, etc.).

  >>> imf weo --isos "USA" --indicator "NGDP_RPCH"
  

Dataset-Specific Commands & Options

To see available arguments for a dataset, run:

>>> imf <dataset> --help

For example, running this for vweo returns:

Vintage World Economic Outlook (VWEO)

Usage:
  imf vweo [options]

Options:
  --vintage           April 2025, October 2024, April 2024, October 2023, ...
  --isos              ABW, AFG, AGO, ALB, AND, ...
  --indicator         BCA, BCA_NGDPD, BF, BFD, BFF, ...
  --start_year        e.g. 2020
  --end_year          e.g. 2028
  --database [-d]     e.g. database.db
  --table [-t]        e.g. vweo
  --save_path [-s]    e.g. vweo.json
  --full_output       True/False
  --use_iso_alpha2    True/False
  --info              Show dataset description
  --help [-h]         Show this help message

Examples of Dataset-Specific CLI Commands & Options

• --info
  Show detailed info of specified dataset.

  >>> imf vweo --info
  

• --{*args}
  Pass arguments to specify dataset retrieval.

  Download the April 2025 WEO dataset and save it as vweo.csv

  >>> imf vweo --vintage "April 2025" --save_path "vweo.csv".
  

  Print 2026 growth forecasts for China, the EU and the US. Note: the IMF uses G998 as the country code for the European Union in the WEO dataset.

  >>> imf weo --isos "CHN G998 USA" --indicator "NGDP_RPCH" --start_date 2025 --end_date 2026
  

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Compatibility

The library is intended to be minimalistic in its dependencies and work smoothly with other widely used libraries, such as pandas. This example fetches real GDP growth figures for France and the UK, converts records into a dataframe and plots the results.

import pandas as pd
from world_economic_outlook import weo

records = weo(
    isos=["FRA", "GBR"],
    indicator="NGDP_RPCH",
    start_date="2015",
    end_date="2025"
)

df = pd.DataFrame(records)

df.pivot(index="date", columns="iso", values="value").plot()

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License

This project is developed by Rob Suomi and licensed under the MIT License.
See the LICENSE file for details.