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Quandl API for Python

See http://www.quandl.com/api

Basic wrapper to return datasets from the Quandl website as Pandas dataframe objects with a timeseries index, or as a numpy array. This allows interactive manipulation of the results via IPython or storage of the datasets using Pandas I/O functions. You will need a familarity with Pandas (http://pandas.pydata.org/) to get the most out of this.

Example

An example of creating a pandas time series for IBM stock data, with a weekly frequency

import Quandl
data = Quandl.get("GOOG/NYSE_IBM",frequency="weekly")
data.head()

will output

No authentication tokens found,usage will be limited 
Returning Dataframe for  GOOG/NYSE_IBM
          Open    High     Low   Close   Volume
Date                                               
2013-03-28  209.83  213.44  209.74  213.30  3752999
2013-03-15  215.38  215.90  213.41  214.92  7937244
2013-03-08  209.85  210.74  209.43  210.38  3700986
2013-03-01  200.65  202.94  199.36  202.91  3309434
2013-02-22  199.23  201.09  198.84  201.09  3107976

Usage

Usage is simple and mirrors the functionality found at http://www.quandl.com/api

A request with a full list of options would be the following.

import Quandl
data = Quandl.get('PRAGUESE/PX',authtoken='xxxxxx',startdate='2001-01-01',enddate='2010-01-01',frequency='annual',transformation = 'rdiff',rows= 4,formats='numpy')

All options beyond specifying the dataset (PRAUGESE/PX) are optional,though it is helpful to specify an authtoken at least once to increase download limits, it should be cached after that.

you can then view the dataframe with:

data.head()

See the pandas documentation for a wealth of options on data manipulation.

Authtokens are saved as pickled files in the local directory so it is unnecessary to enter them more than once, unless you change your working directory.To replace simply save the new token or delete authtoken.p.

Complex Example

Quarterly normalized crude oil prices since 2005, only returning first 4 values.

import Quandl
data = Quandl.get("IMF/POILAPSP_INDEX",frequency="quarterly",startdate="2005",transformation = "normalize",rows="4")
data.head()

returns:

No authentication tokens found,usage will be limited 
Returning Dataframe for  IMF/POILAPSP_INDEX
               Price
Date                  
2013-02-28  212.792283
2012-12-31  200.073398
2012-09-30  210.212855
2012-06-30  179.322638

##Uploads You can now upload your own data to Quandl through the Python package.

At this time the only accepted format is a date indexed Pandas DataSeries.

Things to do before you upload:

• Make an account and set your authentication token within the package with the Quandl.auth() function.
• Get your data into a data frame with the dates in the first column.
• Pick a code for your dataset - only capital letters, numbers and underscores are acceptable.

Then call this function Quandl.push(data,code = "TEST", name ="Test", desc="test") All parameters but desc are necessary

if you wish to override the existing set at code TEST add override= True

Example

Uploading a pandas DataSeries with random data

data = pandas.DataFrame(numpy.random.randn(6, 3), index=['Dec 12 2296', 'Dec 21 1998', 'Oct 9 2000','Oct 19 2001', 'Oct 30 2003', 'Nov 12 2003'],columns=['D', 'B', 'C'])
print Quandl.push(data,code = "F32C", name ="Test", desc="test", authtoken = "YOURTOKENHERE")

Will return the link to your newly uploaded data

Recommended Usage

The IPython notebook is an excellent python environment for interactive data work. Spyder is also a superb IDE for analysis and more numerical work.

I would suggest downloading the data in raw format in the highest frequency possible and preforming any data manipulation in pandas itself. See the following:http://pandas.pydata.org/pandas-docs/dev/timeseries.html

Questions/Comments

Please send any questions, comments, or any other inquires about this package to [email protected]

Dependencies

Pandas https://code.google.com/p/pandas/

dateutil (should be installed as part of pandas) http://labix.org/python-dateutil

License

MIT License