5 Best Ways to Program to Find Out the Conversion Rate of Two Currencies in Python

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💡 Problem Formulation: This article addresses the task of calculating the conversion rate between two different currencies using Python programming. The objective is to input an amount in one currency (e.g., USD) and receive the equivalent amount in another currency (e.g., EUR) based on real-time or most recent exchange rates. The desired output is a converted monetary amount accompanied by an updated exchange rate.

Method 1: Using forex-python

The forex-python library is a free library that provides currency exchange rates and cryptocurrency prices. It supports various sources and is easy to use. By installing the library and implementing a few lines of code, one can retrieve live exchange rates and convert amounts with updated values.

Here’s an example:

from forex_python.converter import CurrencyRates

def get_conversion_rate(from_currency, to_currency):
    c = CurrencyRates()
    rate = c.get_rate(from_currency, to_currency)
    return rate

# Example Usage
print(get_conversion_rate('USD', 'EUR'))

Output: 0.8523 (May vary based on current rates)

This snippet shows how to use the forex_python library to get real-time conversion rates. It initializes a CurrencyRates object and calls the get_rate() method with the two specified currencies to return the current conversion rate. Please note that the actual output will fluctuate with the live forex market.

Method 2: Using requests and an API service

This method utilizes HTTP requests to access a currency conversion API service. By sending an API request to a service such as Open Exchange Rates or Fixer.io, one can receive JSON formatted data with recent exchange rates. It requires API keys for access but offers a programmatic way of obtaining currency data.

Here’s an example:

import requests

def get_conversion_rate(api_key, from_currency, to_currency):
    url = f"https://api.exchangeratesapi.io/latest?base={from_currency}&symbols={to_currency}"
    response = requests.get(url, params={'access_key': api_key})
    data = response.json()
    return data['rates'][to_currency]

# Example usage with placeholder API Key
api_key = 'YOUR_API_KEY'
print(get_conversion_rate(api_key, 'USD', 'EUR'))

Output: 0.8523 (May vary based on current rates)

This code uses the requests module to perform an API request to a currency exchange API service, retrieving a JSON object and extracting the conversion rate. It is critical to replace ‘YOUR_API_KEY’ with a valid API key issued by the respective currency exchange service.

Method 3: Using BeautifulSoup for Web Scraping

Web scraping with BeautifulSoup allows one to parse HTML and extract information from a web page. This method would involve identifying a financial website that publishes currency rates and scraping the desired information. Note that this method requires consideration for the legality and ethics of scraping a particular website.

Here’s an example:

import requests
from bs4 import BeautifulSoup

def get_conversion_rate(from_currency, to_currency):
    url = f"http://example-currency-website.com/{from_currency}to{to_currency}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    rate = soup.find('div', {'id': 'exchange-rate'}).get_text()
    return float(rate)

# Example usage
print(get_conversion_rate('USD', 'EUR'))

Output: 0.8523 (The output would depend on the website’s current data)

This snippet uses the requests library to fetch a web page containing exchange rates and then employs BeautifulSoup to parse and search for the relevant data. It assumes there’s a div with an id of ‘exchange-rate’ containing the exchange rate, which you must adapt to the website’s actual structure.

Method 4: Using pandas and pandas-datareader

The pandas-datareader library allows fetching financial data from various sources and is particularly well-suited for batch operations on financial time series data. This method may require setting up a data source or finding a compatible one, but it is powerful for more complex financial analysis tasks.

Here’s an example:

import pandas_datareader.data as web
from datetime import datetime

def get_conversion_rate(from_currency, to_currency):
    start = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
    df = web.DataReader(f'{from_currency}/{to_currency}', 'av-forex', start, api_key='YOUR_ALPHAVANTAGE_KEY')
    return df['Exchange Rate'][0]

# Example usage with placeholder API Key
print(get_conversion_rate('USD', 'EUR'))

Output: 0.8523 (May vary based on current rates)

By using the pandas-datareader library, this code snippet leverages the Alpha Vantage Forex API to retrieve the current exchange rate. The example demonstrates fetching today’s USD to EUR rate. The user must replace ‘YOUR_ALPHAVANTAGE_KEY’ with a valid Alpha Vantage API key.

Bonus One-Liner Method 5: Using a Pre-Defined Exchange Rate

If a real-time rate isn’t required, one can use a recent static rate for conversions. Less accurate but super simple and doesn’t require internet connectivity.

Here’s an example:

def convert_currency(amount, rate):
    return amount * rate

# Example usage with a predefined rate
usd_to_eur_rate = 0.84
print(convert_currency(50, usd_to_eur_rate))

Output: 42.0

This is the simplest method, using a hard-coded conversion fraction to convert an amount from one currency to another. It multiplies the given amount by the predefined rate, in this case converting 50 USD to EUR at a rate of 0.84.

Summary/Discussion

  • Method 1: forex-python. Practical for real-time rates. Requires third-party library installation. Not all sources may be reliable or available.
  • Method 2: API service. Accurate and official data source. Requires API key and may be rate-limited or have cost implications for frequent calls.
  • Method 3: BeautifulSoup Web Scraping. Flexibility to choose source. Legality and ethics must be considered, and the method can break with website layout changes.
  • Method 4: pandas-datareader. Excellent for batch processing and historical analysis. Requires API key and can be overkill for simple conversion rates retrieval.
  • Bonus Method 5: Pre-Defined Exchange Rate. Simple and quick. Does not require internet access but is not accurate for real-time applications and requires manual updates.