world-economic-outlook 0.0.7

An IMF World Economic Outlook (WEO) API Client

World Economic Outlook

Easily fetch IMF World Economic Outlook (WEO), International Financial Statistics (IFS), and Direction of Trade Statistics (DOTS) data.

World Economic Outlook is a Python library and CLI tool for downloading, saving, and processing major IMF datasets. It provides a convenient interface for managing data, saving it locally, and pushing it to a database.

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Features

• Download WEO, IFS, and DOTS data from the IMF.
• Save data as JSON, CSV, TXT, or original XLS (WEO only).
• Push data to a SQLite database.
• Command-line interface (CLI) for all major actions.
• Programmatic API for automation and scripting.
• Wrapper functions for flexible workflows.
• Query data conveniently using SQL.

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Installation

Requires Python 3.9+.

Install the published package:

pip install world-economic-outlook

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Quick Start

Programmatic Usage

1. Using the Wrappers

This example demonstrates how to use the wrappers to download WEO, IFS, and DOTS data. By default, data is returned in memory if neither save_path nor database is specified. You can also save to a file or database.

Download data and return records in-memory (no file/database):

from world_economic_outlook import weo, ifs, dots

# Download WEO vintage and return records in-memory
records = weo("2025 April")

# Download IFS data and return records in-memory
records = ifs(["DE"])

# Download DOTS data and return records in-memory
records = dots(["US", "U2"])

If neither save_path nor database is specified, data is always returned in-memory as a list of dicts.

Download data and save:

from world_economic_outlook import weo, ifs, dots

# Download WEO vintage and save
weo("2025 April", save_path="weo.json")

# Download IFS data and save
ifs(["DE"], save_path="ifs.csv")

# Download DOTS data and save
dots(["US", "U2"], save_path="dots.txt")

Download data, save and return records (in-memory):

from world_economic_outlook import weo, ifs, dots

# Download WEO vintage, save, and return records (in-memory)
records = weo(
    vintage="2025 April",
    database="database.db",
    table="weo",
    save_path="weo.json"
)

# Download IFS data, save, and return records (in-memory)
records = ifs(
    isos=["GB", "FR"],
    database="database.db",
    table="ifs",
    save_path="ifs.csv"
)

# Download DOTS data, save and return records (in-memory)
records = dots(
    isos=["FI", "SE", "NO", "DK"],
    database="database.db",
    table="dots",
    save_path="dots.txt",
)

2. Fetching Specific Macroeconomic Data

This example shows how to fetch monthly exchange rate data for Mexico, Brazil, Chile, Colombia and Argentina between 2018 and 2022. Data is returned in-memory by default.

from world_economic_outlook import ifs

records = ifs(
    isos=["MX", "BR", "CL", "CO", "AR"],
    indicators=["EDNA_USD_XDC_RATE"],
    start_date="2018-01-01",
    end_date="2022-01-01",
    freq="M"
)

print(records[:5])

3. Fetching IFS Metadata

This example shows how to fetch IFS Indicator and Area Metadata as records, and optionally save them to a file and/or database. The save_path, database, and table arguments are optional, allowing you to persist metadata for reuse without needing to fetch it again in subsequent runs.

from world_economic_outlook import ifs_indicator_metadata, ifs_area_metadata

# Download indicator metadata (optional, not required for basic data fetching)
indicator_records = ifs_indicator_metadata(
    save_path="ifs_indicator_metadata.csv",
    database="database.db",
    table="ifs_indicator_metadata"
)

# Download area metadata (optional, not required for basic data fetching)
area_records = ifs_area_metadata(
    save_path="ifs_area_metadata.csv",
    database="database.db",
    table="ifs_area_metadata"
)

print(indicator_records[:5])
print(area_records[:5])

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Command-Line Interface (CLI)

The CLI provides a convenient way to interact with all supported IMF datasets. After installation, use the imf command:

Download WEO Data and return records in-memory

imf weo "2025 April"

Returns data in-memory and prints it as JSON to stdout if neither --save nor --database is specified. Warning: Printing large datasets may take a long time and produce a lot of output.

Download WEO Data and save as JSON

imf weo "2025 April" -s weodata.json

Download IFS Data and return records in-memory

imf ifs GB FR -i EDNA_USD_XDC_RATE

Download IFS Data and save as CSV

imf ifs JP CH GB U2 -i EDNA_USD_XDC_RATE -s exchange_rates.csv

Download DOTS Data and save to database

imf dots FI SE NO DK -d database.db -t trade

CLI Help

imf --help
imf weo --help
imf ifs --help
imf dots --help

CLI Command Overview

Subcommand	Flag / Argument	Type	Description	Required	Default
weo	vintage	str	Vintage (e.g., '2025 April')	Yes	—
	-s, --save	str	Path to save WEO data as .xls, .json, .csv, or .txt	No	—
	-d, --database	str	Path to SQLite database file	No*	—
	-t, --table	str	Table name in database	No*	—
ifs	isos	str list	List of ISO country codes (e.g., FI SE NO DK)	Yes	—
	-i, --indicators	str list	IFS indicator codes	No	—
	--start	str	Start period (e.g., 2023-01)	No	—
	--end	str	End period (e.g., 2025-03)	No	—
	-d, --database	str	Path to SQLite database file	No*	—
	-t, --table	str	Table name in database	No*	—
	-f, --frequency	str	Frequency: M, Q, or A	No	M
	-s, --save	str	Path to save IFS data as .json, .csv, or .txt	No	—
dots	isos	str list	List of ISO country codes (e.g., FI SE NO DK)	Yes	—
	--start	str	Start period (e.g., 2023-01)	No	—
	--end	str	End period (e.g., 2025-03)	No	—
	-d, --database	str	Path to SQLite database file	No*	—
	-t, --table	str	Table name in database	No*	—
	-f, --frequency	str	Frequency: M, Q, or A	No	M
	-s, --save	str	Path to save DOTS data as .json, .csv, or .txt	No	—

* If either --database or --table is provided, both must be specified.

For all commands, if neither --save nor --database is specified, data is returned in-memory. For weo, either --save or --database is recommended for persistence.

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Exporting Data

Say we have already retrieved exchange rate data from the IMF IFS Database:

imf ifs JP CH GB U2 -i EDNA_USD_XDC_RATE -d database.db -t exchange_rates

Then we can easily query the database and save the output into a json using the CLI:

db query -d database.db -s "SELECT * FROM exchange_rates" -f exrates.json

Alternatively, we can export the entire table into a csv:

db export -d database.db -t exchange_rates -f exrates.csv

Note: For larger database tables, bulk exporting the table may take a while.

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License

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