Skip to main content

A lightweigh loader for data from Penn World Table datasets via Dataverse

Project description

PWTLoader

PWTLoader is a Python Package built as a lightweight loader for the main .dta file as well as additional .dta files from the Penn World Tables datasets hosted at dataverse into a pandas pd.

This project uses data from the Penn World Table (PWT), version 10.01, developed by
Robert C. Feenstra, Robert Inklaar, and Marcel P. Timmer
at the Groningen Growth and Development Centre.

Features

  • Allows the download of the latest Penn World Table data from the dataverse
  • Parse and preprocess data for further economic analysis.
  • Work with data in pandas DataFrame format.

Example

Here's an example of how to run PWTLoader

from PWTLoader import PWTLoader

# Create an instance of the loader
pwt = PWTLoader()

# Load the main dataset
df = pwt.load_data()

# If you want additional data
additional_data = pwt.additional_data(merge=True)

# To understand the structure of the additional data
pwt.describe_additional()

# Print out a preview of the data
print(df.head())

📘 Method Overview & Output

PWTLoader.load_data()----------------------------------------------

  • Description:
    Downloads and loads the main .dta file from the latest Penn World Table version hosted on Dataverse.

  • Returns:
    A pandas.DataFrame containing the main dataset.
    You can use the PWTLoader.labels["variable_name"] to access the description of a specific variable in the dataset.

  • Example:

    df = pwt.load_data()
    print(df.head())
    print(pwt.labels["cgdpo"])  # Description of the "cgdpo" variable
    

PWTLoader.additional_data(merge=False)---------------------------

  • Description:
    Loads additional .dta files (from the same version) that complement the main dataset. If merge=True, it merges all compatible files together on common keys (countrycode, year).

  • Parameters:

    • merge (bool) — If True, merges all additional datasets into one. If False, returns each dataset separately.
  • Returns:

    • merge=True → A dict with a single key "merge" and a merged DataFrame.
    • merge=False → A dict of dataframes keyed by a short name (e.g., 'trade-detail', 'na_data', etc.).
  • Example:

    # Get separate dataframes
    addl_data = pwt.additional_data()
    print(addl_data.keys())
    print(addl_data["na_data"]["df"].head())  # Access specific dataframes
    
    # Get merged dataframe
    merged = pwt.additional_data(merge=True)
    print(merged["merged"].head())  # View merged dataframe
    

PWTLoader.describe_additional(data_dict)-------------------------

  • Description:
    Prints a concise summary of additional data files in the given dictionary, including:

    • Dataset name
    • Description
    • Number of rows/columns
  • Parameters:

    • data_dict (dict) — The dictionary returned from additional_data() (with merged=False).
  • Returns:

    • Nothing (just prints to console).
  • Example:

    # Get additional data
    addl_data = pwt.additional_data()
    
    # Print a summary of each dataset
    pwt.describe_additional(addl_data)
    

Installation

You can install PWTLoader directly via pip;

pip install PWTLoader

To install the necessary dependencies, you can use the requirements.txt file:

pip install -r PWTLoader/requirements.txt

Requirements

  • Python 3.7 or higher
  • pandas
  • requests
  • beautifulsoup4
  • html5lib

Overview of Directory Structure

PWTLoader/ │ ├── init.py # Package initialization ├── loader.py # Main logic to load PWT data ├── requirements.txt # Dependencies ├── setup.py # Package setup ├── README.md # Project documentation ├── .gitignore # Files to ignore

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits and Citation

This project uses data from the Penn World Table (PWT), version 10.01, which includes information on relative levels of income, output, input, and productivity, covering 183 countries from 1950 to 2019.

The data can be accessed from Dataverse: https://doi.org/10.34894/QT5BCC.

Citation

Citation

When using this data (for any purpose), please use the following citation:

Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table,” American Economic Review, 105(10), 3150-3182, DOI: 10.1257/aer.20130954.

License

The data is licensed under CC-BY-4.0, which allows you to share and adapt the data as long as you give appropriate credit.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PWTLoader-0.2.3.tar.gz (5.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

PWTLoader-0.2.3-py3-none-any.whl (6.0 kB view details)

Uploaded Python 3

File details

Details for the file PWTLoader-0.2.3.tar.gz.

File metadata

  • Download URL: PWTLoader-0.2.3.tar.gz
  • Upload date:
  • Size: 5.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for PWTLoader-0.2.3.tar.gz
Algorithm Hash digest
SHA256 4e0ed948f502ecd494ab78beead1b8e75be733203c0d11514fc55085d4fc6d10
MD5 4de84d3e345b6bc627e4741289931e5c
BLAKE2b-256 aaaab2288232b2f63f0919165b9ed48a90a557ce1eba5774cbb5a6c5e5165a58

See more details on using hashes here.

File details

Details for the file PWTLoader-0.2.3-py3-none-any.whl.

File metadata

  • Download URL: PWTLoader-0.2.3-py3-none-any.whl
  • Upload date:
  • Size: 6.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for PWTLoader-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 ea65ad4d96745e7bdcefa31ce55a6b429513079fe0c76d7e14b71b2d648ad04f
MD5 541ea6fb3612d9edef5f48021cca240c
BLAKE2b-256 2607e7d2ea730dae5aeaa4e3f395cac00f71baa73b32622040d27071cfbe13b8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page