andreasKK / PyConCur

Converts one currency to another by webscraping rates

PyConCur

Just learning Python and having fun

Converts one currency to another by web scraping currency rates.

List of Available Currencies

Module Usage

$ ls -1
example_your_project.py

$ git clone https://github.com/Funkallero/PyConCur.git

$ ls -1
example_your_project.py
PyConCur/

usage   : ConCur([TO], [FROM], [AMOUNT])
default : ConCur('USD', 'EUR', 1)

examples:

c = ConCur ('dkk', 'usd', 100) >>> c.conversion returns 100 USD in DKK
c = ConCur ('dkk', 100)        >>> c.conversion returns 100 EUR in DKK
c = ConCur ()                  >>> c.conversion returns   1 EUR in USD

Examples

>>> from PyConCur.concur import ConCur
>>> c = ConCur()
>>> c.conversion
1.136991
>>> c.timestamp
'2019-06-30 08:02:00'
>>> y = ConCur('nok', 'sek', 42)
>>> y.conversion
38.57582323046362
>>> y.all_rates()

Src : x-rates.com
Base: EUR
Date: '2019-06-30 08:45:59' UTC

                  Country Currency     Rate From   Rate To
0          Argentine Peso      ARS     61.479698  0.016266
1       Australian Dollar      AUD      1.663562  0.601120
2          Bahraini Dinar      BHD      0.421699  2.371358
3           Botswana Pula      BWP     12.390234  0.080709
4          Brazilian Real      BRL      4.619463  0.216475
5         Bruneian Dollar      BND      1.558730  0.641548
6           Bulgarian Lev      BGN      1.955830  0.511292
7         Canadian Dollar      CAD      1.488455  0.671838
8            Chilean Peso      CLP    807.165778  0.001239
9   Chinese Yuan Renminbi      CNY      7.954439  0.125716
10         Colombian Peso      COP   3859.088521  0.000259
11          Croatian Kuna      HRK      7.435511  0.134490

# .... and so on (it will display all rates)

Run a test to check accuracy. Testing PyConCur against https://fixer.io API.

>>> from PyConCur.concur import test_concur
>>> test_concur()

Running 100 conversions in total.
Each conversion must score above 99% accuracy in order to pass.
The test itself is passed, if the average accuracy of all conversions is above 99.8%.

RESULT: TEST PASSED

TEST SUMMARY:
Passed: 99
Failed: 1
Avg Accuracy: 99.873%

TEST ERRORS:

Test 73 returned below or equal to 99% accuracy.

Conversion from 200 OMR to MUR.
Tried Conversion: 18725.565 MUR
Actual Conversion: 18522.166 MUR
Error Margin: 98.91%

CLI Usage

usage: concur.py [-h] [-a A] [-f F] [-t T] [-p P] [-test TEST]

optional arguments:
  -h, --help  show this help message and exit
  -a A        A = the amount to convert (default: -a 1).
  -f F        F = the currency to convert from (default: -f EUR).
  -t T        T = the currency to convert to (default: -t USD).
  -p P        Get all rates in relation to EUR in a dataframe. (usage: -p all)
  -test TEST  Test conversion accuracy. Runs 100 tests. (usage: -test on)

Examples

$ python3 concur.py

Converting Euro to US Dollar
Rates from 2019-07-07 00:15:57 UTC
Source: ecb.europa.eu

1 EUR = 1.126 USD

$ python concur.py -t dkk -a 10

Converting Euro to Danish Krone
Rates from 2019-07-07 00:15:57 UTC
Source: ecb.europa.eu

10.0 EUR = 74.635 DKK

$ python concur.py -t aud -f usd -a 1857.35

Converting US Dollar to Australian Dollar
Rates from 2019-07-07 00:15:57 UTC
Source: ecb.europa.eu

1857.35 USD = 2658.621 AUD

$ python concur.py -p all

Src : x-rates.com
Base: EUR
Date: 2019-07-07 00:17:34 UTC

                  Country Currency     Rate From   Rate To
0          Argentine Peso      ARS     61.479698  0.016266
1       Australian Dollar      AUD      1.663562  0.601120
2          Bahraini Dinar      BHD      0.421699  2.371358
3           Botswana Pula      BWP     12.390234  0.080709
4          Brazilian Real      BRL      4.619463  0.216475
5         Bruneian Dollar      BND      1.558730  0.641548
6           Bulgarian Lev      BGN      1.955830  0.511292
7         Canadian Dollar      CAD      1.488455  0.671838
8            Chilean Peso      CLP    807.165778  0.001239
9   Chinese Yuan Renminbi      CNY      7.954439  0.125716
10         Colombian Peso      COP   3859.088521  0.000259
11          Croatian Kuna      HRK      7.435511  0.134490
12           Czech Koruna      CZK     25.972408  0.038502
13           Danish Krone      DKK      7.494460  0.133432

# .... and so on (it will display all rates)

Run a test to check accuracy. Testing PyConCur against https://fixer.io API.

$ python concur.py -test on

Running 100 conversions in total. Each conversion must score above 99% accuracy in order to pass.
The test itself is passed, if the average accuracy of all conversions is above 99.8%.

RESULT: TEST PASSED

TEST SUMMARY:
Passed: 99
Failed: 1
Avg Accuracy: 99.869%

TEST ERRORS:

Test 35 returned below or equal to 99% accuracy.

Conversion from 347 BHD to MUR.
Tried Conversion: 33223.278 MUR
Actual Conversion: 32813.637 MUR
Error Margin: 98.76%

Error Example

$ python concur.py -t aud -f usda -a 1857.35

Input Error. Currency 'USDA' cannot be found.
Try again.

$ python concur.py -t audt -f usda -a 1857.35

Input Error. Both currencies 'AUDT' and 'USDA' cannot be found.
Try again.

To do:

[1]

Implement re.match() to match input and scrape ecb if match else x-rates

[2]

Scrape more sites other than x-rates.com and ecb.europa.eu

[3]

Add more currencies. Currently supports 53

[4]

Extend DataFrame to easily change base currency

/Christian Lindeneg