easymoney 1.0.0

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 be separated in time (inflation), currency (conversion) as well as the ways in which these sources refer to the currency being used (e.g., ISO Alpha2 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 Codes, Currency Codes and a region’s Natural Name.

NOTICE: THIS TOOL IS FOR INFORMATION PURPOSES ONLY.

Import the tool

from easymoney.money import EasyPeasy

Create an instance of the EasyPeasy Class

ep = EasyPeasy()

Prototypical Conversion Problems

Currency Converter

ep.currency_converter(amount = 100, from_currency = "USD", to_currency = "EUR", pretty_print = True)

# 88.75 EUR

Adjust for Inflation and Convert

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

# 140.66 CAD

Convert Currency in a more Natural Way

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

# 68.58 EUR

Handling Common Currencies

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.

Normalization

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

# 118.98 USD

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

# 120.45 USD

EasyMoney also understands that, while these two nations may share a common currency, inflation may differ.

Options

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

ep.options(info = 'all', pretty_print = True, overlap_only = True)

Above, the InflationRange and ExchangeRange columns provide the range of dates for which inflation and exchange rate information is available, respectively. The Overlap column shows the range of dates shared by these two columns. Additionally, the dates of known transitions from one currency to another are also provided.

To gain access to a summary of the exchange data alone, ‘exchange’ can be passed to info. Similarly, ‘inflation’ can be passed to inspect inflation information separately.

# Currency Information Alone
ep.options(info = 'exchange', pretty_print = True)

# Inflation Information Alone
ep.options(info = 'inflation', pretty_print = True)

Changing pretty_print to False will return the information in options() as a Pandas DataFrame.

inflation_df = ep.options(info = 'inflation', pretty_print = False)

It is also possible to simply obtain a list of regions for which inflation information is available.

inflation_list = ep.options(info = 'inflation', rformat = 'list', pretty_print = False)

This can also be done for exchange rate information.

currency_list = ep.options(info = 'exchange', rformat = 'list', pretty_print = False)

Databases

It’s also straightforward to gain access to the databases used by EasyPeasy().

To see all of the International Organization for Standardization (ISO) Alpha2 and Alpha3 codes (along with a region’s natural name) currently cached:

ep.ISOAlphaCodesDB

To see all of the known transitions from one currency to another:

ep.CurrencyTransitionDB

To see the raw Exchange Rate information currently cached:

ep.ExchangeRatesDB

To see the raw Consumer Price Index (CPI) information currently cached:

ep.ConsumerPriceIndexDB

Finally, to see the relationships between Country Names, ISO Alpha2/3 Codes and Currency Codes currently understood by EasyPeasy():

ep.ConsumerPriceIndexDB

The databases used by EasyPeasy() can be saved disk so they can be used offline or modified. To do so, one can simply pass a directory when creating an instance of the EasyPeasy() class.

ep = EasyPeasy('/path/of/your/choosing')

If this directory does not contain any of the required databases, it will be populated with them. Conversely, if the the directory already contains some of the required databases, EasyPeasy() will automagically read in the existing databases and generate only those databases that are missing.