Overview

This project simplifies gathering and processing of Uruguayan economic statistics. Data is retrieved from (mostly) government sources, processed into a familiar tabular format, tagged with useful metadata and can be transformed in several ways (converting to dollars, calculating rolling averages, resampling to other frequencies, etc.).

If this screenshot gives you anxiety, this package should be of interest.

A webapp with a limited but interactive version of econuy is available at econ.uy. Check out the repo as well.

The most basic econuy workflow goes like this:

from econuy import load_dataset, load_datasets_parallel

data1 = load_dataset("cpi")

Installation

PyPI:

pip install econuy

Git:

git clone https://github.com/rxavier/econuy.git
cd econuy
python setup.py install

Usage

Full API documentation available at RTD

Cache directory

econuy saves and reads data to a directory which by default is at the system home / .cache / econuy. This can be modified for all data loading by setting ECONUY_DATA_DIR or directly in load_dataset(data_dir=...).

Dataset load branching

Check that the dataset exists in the REGISTRY.
Cache check:

If skip_cache=True, download dataset
If skip_cache=False (default):
- Check whether the dataset exists in the cache.
  - If it exists:
    - Recency check:
      - If it was created in the last day, return existing dataset.
      - If it was created prior to the last day and skip_update=False, download dataset.
      - If it was created prior to the last day and skip_update=True, return existing dataset.
  - If it does not exist, download dataset

If the dataset was downloaded, try to update the cache:

Validation:
- If force_overwrite=True, overwrite dataset.
- If force_overwrite=False (default):
  - If the new dataset is similar to the cached dataset, overwrite dataset.
  - If the new dataset is not similar to the cached dataset, do not overwrite dataset.

Loading and transforming data

from econuy import load_dataset, load_datasets_parallel


# load a single dataset
data1 = load_dataset("cpi")

# load a single dataset and chain transformations
data2 = (
    load_dataset("fiscal_balance_nonfinancial_public_sector")
    .select(names="Ingresos: SPNF")
    .resample("QE-DEC", "sum")
    .decompose(method="x13", component="t-c")
    .filter(start_date="2014-01-01")
    )

This returns a Dataset object, which contains a Metadata object.

You can also load multiple datasets fast:

# load multiple datasets using threads or processes
data3 = load_datasets_parallel(["nxr_monthly", "ppi"])

Finding datasets

from econuy.utils.operations import REGISTRY


REGISTRY.list_available()
REGISTRY.list_by_area("activity")

Dataset metadata

Datasets include the following metadata per indicator:

Indicator name
Area
Frequency
Currency
Inflation adjustment
Unit
Seasonal adjustment
Type (stock or flow)
Cumulative periods

Transformation methods

Dataset objects have multiple methods to transform their underlying data and update their metadata.

resample() - resample data to a different frequency, taking into account whether data is of stock or flow type.
chg_diff() - calculate percent changes or differences for same period last year, last period or at annual rate.
decompose() - seasonally decompose series into trend or seasonally adjusted components.
convert() - convert to US dollars, constant prices or percent of GDP.
rebase() - set a period or window as 100, scale rest accordingly
rolling() - calculate rolling windows, either average or sum.

External binaries and libraries

unrar libraries

The patool package is used in order to access data provided in .rar format. This package requires that you have the unrar binaries in your system, which in most cases you should already have. You can can get them from here if you don't.

Caveats

This project is heavily based on getting data from online sources that could change without notice, causing methods that download data to fail. While I try to stay on my toes and fix these quickly, it helps if you create an issue when you find one of these (or even submit a fix!).

Release files for econuy 0.23.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for econuy 0.23.0
File	Size	Uploaded
econuy-0.23.0.tar.gz	104.7 kB	Feb 1, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for econuy 0.23.0
File	Interpreter	ABI	Platform	Reset
econuy-0.23.0-py3-none-any.whl 112.1 kB Feb 1, 2025	Python 3	none	any	Details

Total release size: 216.7 kB

Release files / econuy-0.23.0.tar.gz

Download URL	econuy-0.23.0.tar.gz
Size	104.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`75effb8b38d40c2a679a71ca6b36f601fbd2fd8f5e2600b1f0fd498b9b06870b`
BLAKE2b-256 checksum How to use checksums	`63dda432d8e239d69c27d43f7b774d12ffadb01fb6a11410b822f0685812e771`
Upload date	Feb 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.8`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/rxavier/econuy
Publishing commit: github.com/rxavier/econuy/tree/97cb320d4341919bf9e0e1b468b910b6eddfd0cb

Workflow

Publishing configuration: github.com/rxavier/econuy/blob/97cb320d4341919bf9e0e1b468b910b6eddfd0cb/.github/workflows/publish.yml
Publishing logs: github.com/rxavier/econuy/actions/runs/13085000776/attempts/1

Artifact

Subject: econuy-0.23.0.tar.gz
SHA-256 checksum: 75effb8b38d40c2a679a71ca6b36f601fbd2fd8f5e2600b1f0fd498b9b06870b Verifying attestations

Release files / econuy-0.23.0-py3-none-any.whl

Download URL	econuy-0.23.0-py3-none-any.whl
Size	112.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`79833c0676f5424866402be26c2b4d470f5a47130900d977948e784dbab1fe55`
BLAKE2b-256 checksum How to use checksums	`fb3e4a74fd13e8c55d21322221a74ed5bf0e4ddd5125f2ffea45074b26b50a40`
Upload date	Feb 1, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.8`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/rxavier/econuy
Publishing commit: github.com/rxavier/econuy/tree/97cb320d4341919bf9e0e1b468b910b6eddfd0cb

Workflow

Publishing configuration: github.com/rxavier/econuy/blob/97cb320d4341919bf9e0e1b468b910b6eddfd0cb/.github/workflows/publish.yml
Publishing logs: github.com/rxavier/econuy/actions/runs/13085000776/attempts/1

Artifact

Subject: econuy-0.23.0-py3-none-any.whl
SHA-256 checksum: 79833c0676f5424866402be26c2b4d470f5a47130900d977948e784dbab1fe55 Verifying attestations