cpi

A Python library that quickly adjusts U.S. dollars for inflation using the Consumer Price Index (CPI).

Installation

The library can be installed from the Python Package Index with any of the standard Python installation tools.

pipenv install cpi

Working with Python

Adjusting for inflation is as simple as providing a dollar value followed by the year it is from to the inflate method. By default it is adjusted to its value in the most recent year available using “CPI-U” index recommended as a default by the Bureau of Labor Statistics.

import cpi

cpi.inflate(100, 1950)
1017.0954356846472

If you’d like to adjust to a different year, submit it as an integer to the optional to keyword argument.

cpi.inflate(100, 1950, to=1960)
122.82157676348547

You can also adjust month to month. You should submit the months as datetime.date objects.

from datetime import date

cpi.inflate(100, date(1950, 1, 1), to=date(2018, 1, 1))
1072.2936170212768

You can adjust values using any of the other series published by the BLS as part of its “All Urban Consumers (CU)” survey. They offer more precise measures for different regions and items.

Submit one of the 60 areas tracked by the agency to inflate dollars in that region. You can find a complete list in the repository.

cpi.inflate(100, 1950, area="Los Angeles-Long Beach-Anaheim, CA")
1081.054852320675

You can do the same to inflate the price of 400 specific items lumped into the basket of goods that make up the overall index. You can find a complete list in the repository.

cpi.inflate(100, 1980, items="Housing")
309.77681874229353

And you can do both together.

cpi.inflate(100, 1980, items="Housing", area="Los Angeles-Long Beach-Anaheim, CA")
344.5364396654719

Each of the 7,800 variations on the CU survey has a unique identifier. If you know which one you want, you can submit it directly.

cpi.inflate(100, 2000, series_id="CUUSS12ASETB01")
165.15176374077112

If you’d like to retrieve the CPI value itself for any year, use the get method.

cpi.get(1950)
24.1

You can also do that by month.

cpi.get(date(1950, 1, 1))
23.5

The same keyword arguments are available.

cpi.get(1980, items="Housing", area="Los Angeles-Long Beach-Anaheim, CA")
83.7

If you’d like to retrieve a particular CPI series for inspection, use the series attribute’s get method. No configuration returns the default series.

cpi.series.get()

Alter the configuration options to retrieve variations based on item, area and other metadata.

cpi.series.get(items="Housing", area="Los Angeles-Long Beach-Anaheim, CA")

If you know a series’s identifier code, you can submit that directly to get_by_id.

cpi.series.get_by_id("CUURS49ASAH")

Once retrieved, the complete set of index values for a series is accessible via the indexes property.

series = cpi.series.get(items="Housing", area="Los Angeles-Long Beach-Anaheim, CA")
series.indexes

That’s it!

Working with the command line

The Python package also installs a command-line interface for inflate that is available on the terminal.

It works the same as the Python library. First give it a value. Then a source year. By default it is adjusted to its value in the most recent year available.

inflate 100 1950
1017.09543568

If you’d like to adjust to a different year, submit it as an integer to the --to option.

inflate 100 1950 --to=1960
122.821576763

You can also adjust month to month. You should submit the months as parseable date strings.

inflate 100 1950-01-01 --to=2018-01-01
1054.75319149

Here are all its options.

inflate --help
Usage: inflate [OPTIONS] VALUE YEAR_OR_MONTH

  Returns a dollar value adjusted for inflation.

Options:
  --to TEXT      The year or month to adjust the value to.
  --series_id TEXT  The CPI data series used for the conversion. The default is the CPI-U.
  --help         Show this message and exit.

Working with pandas

An inflation-adjusted column can quickly be added to a pandas DataFrame using the apply method. Here is an example using data tracking the median household income in the United States from The Federal Reserve Bank of St. Louis.

import cpi
import pandas as pd

df = pd.read("test.csv")
df["ADJUSTED"] = df.apply(
    lambda x: cpi.inflate(x.MEDIAN_HOUSEHOLD_INCOME, x.YEAR), axis=1
)

The lists of CPI series and each’s index values can be converted to a DataFrame using the to_dataframe method.

Here’s how to get the series list:

series_df = cpi.series.to_dataframe()

Here’s how to get a series’s index values:

series_obj = cpi.series.get(items="Housing", area="Los Angeles-Long Beach-Anaheim, CA")
index_df = series_obj.to_dataframe()

Source

The adjustment is made using data provided by The Bureau of Labor Statistics at the U.S. Department of Labor.

Currently the library only supports inflation adjustments using series from the “All Urban Consumers (CU)” survey. The so-called “CPI-U” survey is the default, which is an average of all prices paid by all urban consumers. It is available from 1913 to the present. It is not seasonally adjusted. The dataset is identified by the BLS as “CUUR0000SA0.” It is used as the default for most basic inflation calculations. All other series measuring all urban consumers are available by taking advantage of the library’s options. The alternative survey of “Urban Wage Earners and Clerical Workers” is not yet available.

Updating the CPI

Since the BLS routinely releases new CPI new values, this library must periodically download the latest data. This library does not do this automatically. You must update the BLS dataset stored alongside the code yourself by running the following method:

cpi.update()