ppi-toolkit 0.2.1

A Python library for retrieving, storing, and analyzing PPI data.

PPI Toolkit

A Python library for retrieving, storing, and analyzing Producer Price Index (PPI) data from the U.S. Bureau of Labor Statistics (BLS). The library supports:

• Downloading BLS PPI metadata and current commodity data
• Storing the data in a local SQLite database
• Searching PPI data series by fuzzy matching or direct lookups
• Computing annualized percentage changes (1-month, 3-month, 6-month, and 12-month horizons)

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Features

• Automated Data Ingestion
  Fetch metadata and commodity data directly from the BLS text files, then store them in a local SQLite database.

• Clean, Well-Structured Database

  • metadata table for series information (e.g. series_id, group_code, series_title, etc.)
  • commodities table for monthly observations (year, period, value, etc.)

• Fuzzy Text Search
  Quickly locate series by approximate matching on their titles.

• Annualized Changes Calculation
  Compute monthly annualized percentage changes over 1-, 3-, 6-, and 12-month intervals.

• Easy Plotting Hooks (Optional)
  Integration points for generating charts using matplotlib, plotly, etc.

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Quickstart

Below is a minimal example of how to install and use the library:

1. Install the Library

pip install ppi-toolkit

2. Set up the database

from ppi_toolkit.database import PPIDataBase

db = PPIDataBase()
db.setup_and_import_data()

3. Search for a series

from ppi_toolkit.search import PPISearcher

searcher = PPISearcher()  # Uses ~/ppi.db by default
results = searcher.search_titles("steel scrap")

for match in results:
    print(match)
    # Example output: {'series_id': 'WPS101211', 'series_title': 'Carbon steel scrap', 'score': 90}

4. Join and analyze data

from ppi_toolkit.joiner import PPIJoiner
from ppi_toolkit.analyzer import PPIAnalyzer

# 1. Join data from both tables
joiner = PPIJoiner()
df_joined = joiner.get_joined_data("WPS011101")
print(df_joined.head())

# 2. Compute annualized changes
analyzer = PPIAnalyzer()
df_trends = analyzer.compute_annualized_changes("WPS011101", 2018, 1, 2023, 12)
print(df_trends[["date", "value", "ann_1m", "ann_3m", "ann_6m", "ann_12m"]].tail())

5. Plot Data

from ppi_toolkit.plotting import plot_annualized_trends

df_trends = analyzer.compute_annualized_changes(
    series_id="WPS011101",
    start_year=2018, start_month=1,
    end_year=2023, end_month=6
)

# 2. Plot the results
plot_annualized_trends(
    df_trends,
    series_title="Iron and Steel Scrap",
    save_path="steel_scrap.png"
)

License

This project is released under the MIT License. Feel free to use and modify it in personal or commercial projects, respecting the license terms.

Acknowledgements

• Data provided courtesy of the Bureau of Labor Statistics Producer Price Index.