easymoney 1.5.0

Project Summary

This package is primarily intended to be used in the domain of Data Science to simplify the process of analysing data sets which contain money information. Often, the financial information within, and very often between, data sets is separated in time (inflation), currency (conversion) as well as the ways in which these sources refer to the currency being used (e.g., country codes vs. currency codes). Conventionally, this has required handcrafting a solution to control for these differences on a case-by-case basis. EasyMoney is intended to streamline this process to make comparisons across these dimensions extremely simple and straightforward.

Feature Summary

• Computing Inflation

• Currency Conversion

• Adjusting a given currency for Inflation

• ‘Normalizing’ a currency, i.e., adjust for inflation and then convert to a base currency.

• Relating ISO Alpha2/3 Country Codes, Currency Codes as well as a Region’s Name to one another.

• This tool automatically obtains the latest inflation and exchange rate information from online databases.

NOTICE: THIS TOOL IS FOR INFORMATION PURPOSES ONLY.

Import the tool

from easymoney.money import EasyPeasy

Create an instance of the EasyPeasy Class

The standard way to do this is as follows:

ep = EasyPeasy()

However, fuzzy searching can also easily be enabled.

ep = EasyPeasy(fuzzy_threshold=True)

Prototypical Conversion Problems

1. Currency Converter

ep.currency_converter(amount=100000, from_currency="USD", to_currency="EUR", pretty_print=True)

# 94,553.71 EUR

2. Adjust for Inflation and Convert to a base currency

ep.normalize(amount=100000, region="CA", from_year=2010, to_year="latest", pretty_print=True)

# 76,357.51 EUR

3. Convert Currency in a more Natural Way

ep.currency_converter(amount=100, from_currency="Canada", to_currency="Ireland", pretty_print=True)

# 70.26 EUR

Handling Common Currencies

1. Currency Conversion

ep.currency_converter(amount=100, from_currency="France", to_currency="Germany", pretty_print=True)

# 100.00 EUR

EasyMoney understands that these two nations share a common currency.

2. Normalization

ep.normalize(amount=100, region="France", from_year=2010, to_year="latest", base_currency="USD", pretty_print=True)

# 111.67 USD

ep.normalize(amount=100, region="Germany", from_year=2010, to_year="latest", base_currency="USD", pretty_print=True)

# 113.06 USD

EasyMoney also understands that, while these two nations may share a common currency, the rate of inflation in these regions could differ.

Region Information

EasyPeasy’s region_map() method exposes some of the functionality from the pycountries package in a streamlined manner.

ep.region_map('GB', map_to='alpha_3')

# GBR

ep.region_map('GB', map_to='currency_alpha_3')

# GBP

If fuzzy searching is enabled, the search term does not have to exactly match those stored in the databases cached by an EasyPeasy instance.

For example, it is possible to find the ISO Alpha 2 country code for ‘Germany’ by passing ‘German’.

ep.region_map('German', map_to='alpha_2')

# DE

Options

It’s easy to explore the terminology understood by EasyPeasy, as well as the dates for which data is available.

ep.options()

Above, the ‘InflationDates’ and ‘ExchangeDates’ columns provide the range of dates for which inflation and exchange rate information is available, respectively. Additionally, all dates for which data is available can be show by setting the range_table_dates parameter to False. The ‘Overlap’ column shows the range of dates shared by the ‘InflationDates’ and ‘ExchangeDates’ columns.