pydeflate 1.2.10

Package to convert current prices figures to constant prices and vice versa

Pydeflate is a Python package to convert flows data to constant prices. This can be done from any source currency to any desired base year and currency. Pydeflate can also be used to convert constant data to current prices and to convert from one currency to another (in current and constant prices). Users can choose the source of the exchange and deflator/prices data (IMF, World Bank or OECD DAC).

• Free software: MIT license

• Documentation: https://pydeflate.readthedocs.io.

Installation

pydeflate can be installed from PyPI. From the command line:

pip install pydeflate --upgrade

Alternatively, the source code is available on GitHub.

Usage

Basic usage

Convert data expressed in current USD prices to constant EUR prices for a given base year:

import pydeflate
import pandas as pd

# example data
data = {'iso_code': ['FRA','USA', 'GTM'],
        'year': [2017, 2017, 2017],
        'value': [50, 100, 200]}

# create an example dataframe, in current USD prices
df = pd.DataFrame.from_dict(data)

# convert to EUR 2015 constant prices
df_constant = pydeflate.deflate(
    df = df,
    base_year = 2015,
    source = 'wb',
    method = 'gdp',
    source_currency = "USA", # since data is in USD
    target_currency = "EMU", # we want the result in constant EUR
    id_column = "iso_code",
    id_type = "ISO3", # specifying this is optional in most cases
    date_column = "year",
    source_col = "value",
    target_col = "value_constant",
)

print(df_constant)

This results in a dataframe containing a new column value_constant in 2015 constant prices. In the background, pydeflate takes into account:

• changes in princes, through a gdp deflator in this case

• changes in exchange rates overtime

Pydeflate can also handle data that is expressed in local currency units. In that case, users can specify LCU as the source currency.

import pydeflate
import pandas as pd

#example data
data = {'country': ['United Kingdom','United Kingdom', 'Japan'],
        'date': [2011, 2015, 2015],
        'value': [100, 100, 100]}

#create an example dataframe, in current local currency units
df = pd.DataFrame.from_dict(data)

#convert to USD 2018 constant prices
df_constant = pydeflate.deflate(
    df = df,
    base_year = 2018,
    source = 'imf',
    method = 'pcpi',
    source_currency = "LCU", #since data is in LCU
    target_currency = "USA", #to get data in USD
    id_column = "iso_code",
    date_column = "date",
    source_col = "value",
    target_col = "value", #to not create a new column
)

print(df_constant)

Users can also convert a dataset expressed in constant prices to current prices using pydeflate. To avoid introducing errors, users should know which methodology/ data was used to create constant prices by the original source. The basic usage is the same as before, but the to_current parameter is set to True.

For example, to convert DAC data expressed in 2016 USD constant prices to current US dollars:

import pydeflate
import pandas as pd

#example data
data = {'dac_code': [302, 4, 4],
        'date': [2010, 2016, 2018],
        'value': [100, 100, 100]}

#create an example dataframe, in current local currency units
df = pd.DataFrame.from_dict(data)

#convert to USD 2018 constant prices
df_current = pydeflate.deflate(
    df = df,
    base_year = 2016,
    source = 'oecd_dac',
    source_currency = "USA", #since data is in USD constant
    target_currency = "LCU", #to get the current LCU figures
    id_column = "dac_code",
    id_type = "DAC",
    date_column = "date",
    source_col = "value",
    target_col = "value_current",
    to_current = True,
)

print(df_current)

Data source and method options

A source and a method for the exchange and price/gdp deflators must be chosen. The appropriate combination depends on the objectives of the project or the nature of the original data.

In terms of price or GDP deflators, pydeflate provides the following methods:

• World Bank (“wb”):

  • gdp: in order to use GDP deflators.

  • gdp_linked: to use the World Bank’s GDP deflator series which has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, sources or methodologies.

  • cpi: to use Consumer Price Index data

• International Monetary Fund World Economic Outlook (“imf”):

  • gdp: in order to use GDP deflators.

  • pcpi: in order to use Consumer Price Index data.

  • pcpie: to use end-of-period Consumer Price Index data (e.g. for December each year).

• OECD Development Assistance Committee (“oecd_dac”):

  • None: for consistency with how the DAC calculates deflators, only their methodology is accepted/used with this data.

The source of the exchange rate data depends on the source selected. Both imf and wb use data from the International Monetary Fund (LCU per US$, yearly average). The OECD Development Assistance Committee data uses different exchange rates. When oecd_dac is selected as the source, the OECD DAC exchange rates (LCU per US$) are used. Exchange rates between two non USD currency pairs are derived from the LCU to USD exchange rates selected.

Additional features

Pydeflate relies on data from the World Bank, IMF and OECD for its calculations. This data is updated periodically. If the version of the data stored in the user’s computer is older than 50 days, pydeflate will show a warning on import.

Users can always update the underlying data by using:

import pydeflate

pydeflate.update_all_data()

Pydeflate also provides users with a tool to exchange figures from one currency to another, without applying any deflators. This should only be used on numbers expressed in current prices, however.

For example, to convert numbers in current Local Currency Units (LCU) to current Canadian Dollars:

import pydeflate
import pandas as pd

#example data
data = {'iso_code': ['GBR','CAN', 'JPN'],
        'date': [2011, 2015, 2015],
        'value': [100, 100, 100]}

#create an example dataframe, in current local currency units
df = pd.DataFrame.from_dict(data)

#convert to USD 2018 constant prices
df_can = pydeflate.exchange(
    df = df,
    source_currency = "LCU", #since data is in LCU
    target_currency = "CAN", #to get data in Canadian Dollars
    rates_source = 'wb', #this is the same as IMF exchange rates
    value_column = 'value',
    target_column = 'value_CAN',
    id_column = "iso_code",
    id_type = "ISO3"
    date_column = "date",
)

print(df_can)

Credits

This package relies on data from the following sources:

• OECD DAC: https://www.oecd.org/dac/ (Official Development assistance data (DAC1), DAC deflators, and exchange rates used by the DAC)

• IMF World Economic Outlook: https://www.imf.org/en/Publications/WEO (GDP and price deflators)

• World Bank DataBank: https://databank.worldbank.org/home.aspx (exchange rates, GDP and price deflators)

This data is provided based on the terms and conditions set by the original sources

Gbemisola Joel-Osoba provided extensive feedback and testing of version 1.