#!/usr/bin/env python3
"""
Tools for Obtaining Data from the European Central Bank
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
# Modules #
import re
import requests
import pandas as pd
from easymoney.support_money import floater
[docs]def ecb_xml_exchange_data(return_as = 'dict', ecb_extension = "/stats/eurofxref/eurofxref-hist.xml"):
"""
| This tool goes out to the European Central Bank via their generously provided API
| and coerces XML data into a dictionary.
| Expects 'time', 'currency', 'rate' in the XML data.
| Returns a nested dictionary of the form: ``{time: {currency: rate}}``.
| DO NOT WRITE PROCEDURES THAT SLAM THEIR SERVERS.
:param return_as: 'dict' for dictionary (nested); 'df' for pandas dataframe.
:type return_as: str
:param ecb_extension: URL to the exchange rate XML data on "http://www.ecb.europa.eu".
Defaults to '/stats/eurofxref/eurofxref-hist.xml'.
:type ecb_extension: str
:return: exchange rate with EUR as the base-currency.
:rtype: dict or Pandas DataFrame
"""
# Initialize
all_currency_codes = list()
# Constuct the URL to the XML data on the ECB's website.
xmlpath = "http://www.ecb.europa.eu" + ecb_extension
# Request the data from the sever.
url_request = requests.get(xmlpath)
# Convert the XML to str, split on 'Cube><Cube' and remove the first element.
parsed_xml = str(url_request.content).split("Cube><Cube")[1:]
# Initialize the exchange rate dict
exchange_rate_db = dict()
# Iterate though the parsed XML
for j in parsed_xml:
date = re.findall(r'time="(.*?)"', j)[0]
ccodes = re.findall(r'currency="(.*?)" rate=', j)
rates = re.findall(r'rate="(.*?)"', j)
exchange_rate_db[date] = dict(zip(ccodes, [floater(x) for x in rates]))
all_currency_codes += [c for c in ccodes if c not in all_currency_codes]
# return as dict
if return_as == 'dict':
return exchange_rate_db, all_currency_codes
# return as pandas df
elif return_as == 'df':
# Convert to pandas dataframe, convert date --> column and reindex
df = pd.DataFrame.from_dict(exchange_rate_db, orient = 'index')
df['date'] = df.index
df.index = range(df.shape[0])
# Melt the dataframe
df = pd.melt(df, id_vars = ['date'], value_vars = [d for d in df.columns if d != 'date'])
df.rename(columns = {"variable" : "currency", "value" : "rate"}, inplace = True)
# Convert date column --> datetime
df.date = pd.to_datetime(df.date, infer_datetime_format = True)
# Sort by currency code
df.sort_values(['date', 'currency'], ascending = [1, 1], inplace = True)
# Refresh index
df.index = range(df.shape[0])
return df, all_currency_codes
ecb_currency_to_alpha2_dict = { "CYP": "CY"
, "EEK": "EE"
, "LTL": "LT"
, "LVL": "LV"
, "MTL": "MT"
, "ROL": "RO"
, "SIT": "SI"
, "SKK": "SK"
, "TRL": "TR"
}