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


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

pipenv install cpi

Working with Python

Adjusting prices for inflation is as simple as providing a dollar value along with its year of origin to the inflate method.

import cpi

cpi.inflate(100, 1950)

By default the value is adjusted to the most recent year. Unless otherwise specified, “CPI-U” index for all urban consumers is used to make the conversion, the method recommended by the U.S. Bureau of Labor Statistics.

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

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

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))

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")

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")

And you can do both together.

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

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")

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


You can also do that by month.

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

The same keyword arguments are available.

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

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.


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.


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")

That’s it!

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

Working from 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

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

inflate 100 1950 --to=1960

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

Here are all its options.

inflate --help

  Returns a dollar value adjusted for inflation.

  --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.

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()

About our 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 data

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:


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