使用Python从Quandl检索数据 [英] Retrieving data from Quandl with Python
问题描述
如何使用Python API( https://www. quandl.com/help/python )?在 https://www.quandl.com/help/api 上，它显示您可以使用rows = n仅获取数据集的前n行.使用rows = 1获取任何数据集的最新观测值."但是如果我使用rows=1，我将获得第一个观察结果，而不是最新的观察结果.
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 get only the first n rows of your dataset. Use rows=1 to get the latest observation for any dataset." but if i use rows=1 I will get the first observation instead of the latest.
此外，我需要获取美元的汇率，但要从 https://www.quandl.com/resources/api-for-currency-data 似乎我需要检索每种货币的汇率，而不仅仅是获取包含每种货币对美元的所有最新汇率的数据集.这不可能吗?
Besides, I need to get exchange rates for USD but from https://www.quandl.com/resources/api-for-currency-data it seems that I need to retrieve the exchange rates for each currency instead of just having a dataset with all the most recent exchange rates for each currency versus USD. Isn't this possible?
推荐答案
import Quandl
您首先需要按降序对数据集进行排序以获取最新值:
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')
Value
Date
2015-05-15 1.1428
您还需要分别请求每种货币的汇率:
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 2015-05-15
Data columns (total 23 columns):
AUD 11130 non-null float64
BRL 5120 non-null float64
GBP 11137 non-null float64
CAD 11143 non-null float64
NY) 8577 non-null float64
DKK 11145 non-null float64
EUR 4116 non-null float64
HKD 8637 non-null float64
INR 10629 non-null float64
JPY 11131 non-null float64
MYR 11115 non-null float64
MXN 5405 non-null float64
TWD 7650 non-null float64
NZD 11121 non-null float64
NOK 11136 non-null float64
SGD 8636 non-null float64
ZAR 11110 non-null float64
KRW 8523 non-null float64
LKR 10277 non-null float64
SEK 11136 non-null float64
CHF 11137 non-null float64
THB 8556 non-null float64
VEF 5114 non-null float64
dtypes: float64(23)
memory usage: 2.0 MB
>>> fx_rates.tail()
AUD BRL GBP CAD NY) DKK EUR HKD Date
2015-05-11 0.7899 3.0385 1.5593 1.2107 6.2086 NaN 1.1142 7.7535
2015-05-12 0.7989 3.0223 1.5685 1.1987 6.2086 NaN 1.1240 7.7528
2015-05-13 0.8118 3.0265 1.5748 1.1950 6.2043 NaN 1.1372 7.7517
2015-05-14 0.8082 2.9910 1.5766 1.1991 6.2013 NaN 1.1368 7.7505
2015-05-15 0.8053 2.9779 1.5772 1.2009 6.2051 NaN 1.1428 7.7505
INR JPY ... NZD NOK SGD ZAR KRW Date ...
2015-05-11 63.96 120.05 ... 0.7350 7.5605 1.3361 12.0820 1095.39
2015-05-12 64.19 119.80 ... 0.7377 7.4720 1.3336 12.0430 1093.81
2015-05-13 63.88 119.09 ... 0.7488 7.3597 1.3239 11.8760 1089.72
2015-05-14 63.47 119.20 ... 0.7500 7.3829 1.3199 11.8220 1089.46
2015-05-15 63.36 119.36 ... 0.7489 7.3113 1.3195 11.7645 1083.05
LKR SEK CHF THB VEF
Date
2015-05-11 133.3 8.2950 0.9344 33.71 6.2842
2015-05-12 133.5 8.3022 0.9266 33.70 6.2842
2015-05-13 133.5 8.2085 0.9162 33.51 6.2842
2015-05-14 133.4 8.2531 0.9146 33.50 6.2842
2015-05-15 133.4 8.2174 0.9174 33.48 6.2842
[5 rows x 23 columns]
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