Retrieving Data From Quandl With Python

How can I get the latest prices from a Quandl dataset with the Python API (https://www.quandl.com/help/python)? On https://www.quandl.com/help/api, it says 'You can use rows=n to g

Solution 1:

import Quandl

You first need to sort the dataset in descending order to get the most recent value:

Quandl.get("FRED/DEXUSEU", rows=1, sort_order='desc')
             ValueDate2015-05-151.1428

You also need to request the exchange rate separately for each currency:

fred_rates = pd.DataFrame({'Currency': {'DEXBZUS': 'Brazilian Real (BRL)',
                                        'DEXCAUS': 'Canadaian Dollar (CAD)',
                                        'DEXCHUS': 'Chinese Yuan (CNY))',
                                        'DEXDNUS': 'Denish Krone (DKK)',
                                        'DEXHKUS': 'Hong Kong Dollar (HKD)',
                                        'DEXINUS': 'Indian Rupee (INR)',
                                        'DEXJPUS': 'Japanese Yen (JPY)',
                                        'DEXKOUS': 'South Korean Won (KRW)',
                                        'DEXMAUS': 'Malaysian Ringgit (MYR)',
                                        'DEXMXUS': 'Mexican Peso (MXN)',
                                        'DEXNOUS': 'Norwegian Krone(NOK)',
                                        'DEXSDUS': 'Swedish Krona (SEK)',
                                        'DEXSFUS': 'South African Rand(ZAR)',
                                        'DEXSIUS': 'Singapore Dollar (SGD)',
                                        'DEXSLUS': 'Sri Lankan Rupee(LKR)',
                                        'DEXSZUS': 'Swiss Franc (CHF)',
                                        'DEXTAUS': 'New Taiwan Dollar (TWD)',
                                        'DEXTHUS': 'Thai Baht (THB)',
                                        'DEXUSAL': 'Australian Dollar (AUD)',
                                        'DEXUSEU': 'Euro (EUR)',
                                        'DEXUSNZ': 'New Zealand Dollar (NZD)',
                                        'DEXUSUK': 'British Pound (GBP)',
                                        'DEXVZUS': 'Venezuelan Bolivar (VEF)'}})
fred_rates['symbol'] = frates.Currency.map(lambda x: x[-4:-1])

rates = [Quandl.get("FRED/{0}".format(fx)) for fx in fred_rates.index]
fx_rates = pd.concat(rates, axis=1)
fx_rates.columns = [fx for fx in fred_rates.symbol]

>>> fx_rates.info()
<class'pandas.core.frame.DataFrame'>
DatetimeIndex: 11168 entries, 1971-01-04 to 2015-05-15Data columns (total 23 columns):
AUD11130 non-null float64
BRL5120 non-null float64
GBP11137 non-null float64
CAD11143 non-null float64
NY)    8577 non-null float64
DKK11145 non-null float64
EUR4116 non-null float64
HKD8637 non-null float64
INR10629 non-null float64
JPY11131 non-null float64
MYR11115 non-null float64
MXN5405 non-null float64
TWD7650 non-null float64
NZD11121 non-null float64
NOK11136 non-null float64
SGD8636 non-null float64
ZAR11110 non-null float64
KRW8523 non-null float64
LKR10277 non-null float64
SEK11136 non-null float64
CHF11137 non-null float64
THB8556 non-null float64
VEF5114 non-null float64
dtypes: float64(23)
memory usage: 2.0MB

>>> fx_rates.tail()
               AUDBRLGBPCADNY)  DKKEURHKDDate2015-05-110.78993.03851.55931.21076.2086NaN  1.11427.75352015-05-120.79893.02231.56851.19876.2086NaN  1.12407.75282015-05-130.81183.02651.57481.19506.2043NaN  1.13727.75172015-05-140.80822.99101.57661.19916.2013NaN  1.13687.75052015-05-150.80532.97791.57721.20096.2051NaN  1.14287.7505INRJPY   ...       NZDNOKSGDZARKRWDate                        ...                                               
2015-05-1163.96120.05   ...    0.73507.56051.336112.08201095.392015-05-1264.19119.80   ...    0.73777.47201.333612.04301093.812015-05-1363.88119.09   ...    0.74887.35971.323911.87601089.722015-05-1463.47119.20   ...    0.75007.38291.319911.82201089.462015-05-1563.36119.36   ...    0.74897.31131.319511.76451083.05LKRSEKCHFTHBVEFDate2015-05-11133.38.29500.934433.716.28422015-05-12133.58.30220.926633.706.28422015-05-13133.58.20850.916233.516.28422015-05-14133.48.25310.914633.506.28422015-05-15133.48.21740.917433.486.2842  

[5 rows x 23 columns]