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Quick Method Guide - Quandl-Python

This quick guide offers convenient ways to retrieve individual datasets or datatables with the Python package without the need for complex commands.

Retrieving Data

Retrieving data can be achieved easily using the two methods quandl.get for datasets and quandl.get_table for datatables. In both cases we strongly recommend that you set your api key via:

import quandl
quandl.ApiConfig.api_key = 'tEsTkEy123456789'

Datasets

To retrieve a Quandl dataset you can make the following call:

import quandl
data = quandl.get('NSE/OIL')

This finds all data points for the dataset NSE/OIL and stores them in a pandas dataframe. You can then view the dataframe with:

data.head()

However we recommend applying filters to streamline the results. To do this you may want to specify some additional filters and transformations.

import quandl
data = quandl.get('NSE/OIL', start_date='2010-01-01', end_date='2014-01-01',
                  collapse='annual', transformation='rdiff',
                  rows=4)

This revised query will find all data points annually for the dataset NSE/OIL between the year 2010 and 2014 and transform them using the rdiff transformation. The query parameters used are documented in our api docs. Since the data was retrieved in the pandas format you can then view the dataframe with data.head().

Available parameters:

The following additional parameters can be specified for a dataset call:

Option Explanation Example Description
api_key Your access key api_key='tEsTkEy123456789' Used to identify who you are and provide more access. Only required if not set via quandl.ApiConfig.api_key=
<filter / transformation parameter> A parameter which filters or transforms the resulting data start_date='2010-01-01 For a full list see our api docs

For more information on how to use and manipulate the resulting data see the pandas documentation.

Download Entire Database (Bulk Download)

You can download all the data in a database in a single call. The following will download the entire EOD database as a zip file to your current working directory:

import quandl
quandl.bulkdownload('EOD')

After the download is finished, the quandl.bulkdownload will return the filename of the downloaded zip file.

To download database data from the previous day, use the download_type option:

import quandl
quandl.bulkdownload('EOD', download_type='partial')

You can also change the filename of the downloaded zip file by using the filename option:

import quandl
quandl.bulkdownload('EOD', filename='/my/path/EOD_DB.zip')

Download Multiple Codes

Sometimes you want to compare two codes. For example if you wanted to compare the closing prices for Apple and Microsoft, you would obtain the two Quandl codes:

WIKI/AAPL vs. WIKI/MSFT

Append the column you wish to get with a ., and put them into an array.

['WIKI/AAPL.11','WIKI/MSFT.11']

Just make a normal get call with the array passed to get, and your multiset will be returned.

Download Multiple Codes (Multiset)

If you want to get multiple codes at once, delimit the codes with ',', and put them into an array. This will return a multiset.

For example:

data = quandl.get(['WIKI/AAPL.11','WIKI/MSFT.11'])

Which outputs:

See www.quandl.com/docs/api for more information.
Returning Dataframe for  ['WIKI.AAPL.11', 'WIKI.MSFT.11']

        WIKI.AAPL - Close  WIKI.MSFT - Close
Date                                                          
1997-08-20        6.16                     17.57
1997-08-21        6.00                     17.23
1997-08-22        5.91                     17.16
1997-08-25        5.77                     17.06
1997-08-26        5.56                     16.88

Datatables

Datatables work similarly to datasets but provide more flexibility when it comes to filtering. For example a simple way to retrieve datatable information would be:

import quandl
data = quandl.get_table('ZACKS/FC')

Given the volume of data stored in datatables, this call will retrieve the first page of the ZACKS/FC datatable. You may turn on pagination to return more data by using:

import quandl
data = quandl.get_table('ZACKS/FC', paginate=True)

This will retrieve multiple pages of data and merge them together as if they were one large page. In some cases, however, you will still exceed the request limit. In this case we recommend you filter your data using the available query parameters, as in the following example:

import quandl
data = quandl.get_table('ZACKS/FC', paginate=True, ticker=['AAPL', 'MSFT'], per_end_date={'gte': '2015-01-01'}, qopts={'columns':['ticker', 'per_end_date']})

In this query we are asking for more pages of data, ticker values of either AAPL or MSFT and a per_end_date that is greater than or equal to 2015-01-01. We are also filtering the returned columns on ticker, per_end_date and comp_name rather than all available columns. The output format is pandas.

Download table data as a zip file. You can download all the table data in a data table in a single call. The following will download the entire F1 table data as a zip file to your current working directory:

import quandl
data = quandl.export_table('MER/F1')

You can also specify where to download the zip file:

import quandl
data = quandl.export_table('MER/F1', filename='/my/path/db.zip')

Note that if you are downloading the whole table, it will take longer to generate the zip file.

You can also specify what data you want to download with filters and parameters.(cursor_id and paginate are not supported for exporting table zip file):

import quandl
quandl.export_table('ZACKS/FC',  ticker=['AAPL', 'MSFT'], per_end_date={'gte': '2015-01-01'}, qopts={'columns':['ticker', 'per_end_date']})

After the download is finished, the filename of the downloaded zip file will be printed.

Sometimes it takes a while to generate the zip file, you'll get a message while the file is being generated. Once the file is generated, it will start the download of the zip file.

Available parameters:

The following additional parameters can be specified for a datatable call:

Option Explanation Example Description
api_key Your access key api_key='tEsTkEy123456789' Used to identify who you are and provide more access. Only required if not set via quandl.ApiConfig.api_key=
<filter / transformation parameter> A parameter which filters or transforms the resulting data start_date='2010-01-01' For a full list see our api docs
paginate Wether to autoamtically paginate data paginate=True Will paginate through the first few pages of data automatically and merge them together in a larger output format.

For more information on how to use and manipulate the resulting data see the pandas documentation.

Things to note

  • Some datatables will return sample data if a valid api key is not used. If you are not receiving all of the expected data please double check your API key.
  • When using the paginate=True option depending on the total number of rows in the result set you may receive an error indicating that there are more pages that have not been downloaded. This is due to a very large result sets that would be too large to send via the analyst method. If this happens we recommend you take one of two approaches:
    • (recommended) Refine your filter parameters to retrieve a smaller results set
    • Use the the Detailed method to iterate through more of the data.

More usages

For even more advanced usage please see our [Detailed Method Guide] (./FOR_DEVELOPERS.md).