Quandl Python Save Abandoned

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Quandl is now Nasdaq Data Link

We are excited to announce that the Quandl technology platform is being transformed into a new global solution: Nasdaq Data Link. This development is the first step toward realizing a single, unified data discovery and delivery experience with Nasdaq.

In accordance with this change Nasdaq Data Link has released a new version of this Python Client. Moving forward, we are encouraging user to use this VERSION in order to take advantage of new features.

Quandl Python Client

Build Status PyPI version

This is the official documentation for Quandl's Python Package. The package can be used to interact with the latest version of the Quandl RESTful API. This package is compatible with python v2.7.x and v3.x+.

Deprecation of old package

Please see this readme for more information and upgrade instructions: 2.x series transition notes


The installation process varies depending on your python version and system used. However in most cases the following should work:

pip install quandl

Alternatively on some systems python3 may use a different pip executable and may need to be installed via an alternate pip command. For example:

pip3 install quandl


Option Explanation Example
api_key Your access key tEsTkEy123456789
use_retries Whether API calls which return statuses in retry_status_codes should be automatically retried True
number_of_retries Maximum number of retries that should be attempted. Only used if use_retries is True 5
max_wait_between_retries Maximum amount of time in seconds that should be waited before attempting a retry. Only used if use_retries is True 8
retry_backoff_factor Determines the amount of time in seconds that should be waited before attempting another retry. Note that this factor is exponential so a retry_backoff_factor of 0.5 will cause waits of [0.5, 1, 2, 4, etc]. Only used if use_retries is True 0.5
retry_status_codes A list of HTTP status codes which will trigger a retry to occur. Only used if use_retries is True [429, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511]
import quandl
quandl.ApiConfig.api_key = 'tEsTkEy123456789'

By default, SSL verification is enabled. To bypass SSL verification

quandl.ApiConfig.verify_ssl = False

Local API Key file

Save local key to $HOME/.quandl_apikey file

import quandl

Load the API Key without exposing the key in the script or notebook

import quandl

Set a custom location for the API key file, e.g. store the externally outside a docker container

import quandl
quandl.save_key("ourcorporateapikey", filename="/srv/data/somecontainer/.corporatequandlapikey")

and call within the docker container with mount point at /data

import quandl

Retrieving Data

There are two methods for retrieving data in Python: the Quick method and the Detailed method. The latter is more suitable to application programming. Both methods work with Quandl's two types of data structures: time-series (dataset) data and non-time series (datatable).

The following quick call can be used to retrieve a dataset:

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

This example 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().

A similar quick call can be used to retrieve a datatable:

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

This example retrieves all rows for ZACKS/FC where ticker='AAPL' and stores them in a pandas dataframe. Similarly you can then view the dataframe with data.head().

Note that in both examples if an api_key has not been set you may receive limited or sample data. You can find more details on these quick calls and others in our Quick Method Guide.


Currently, Quandl debug logging is limited in scope. However, to enable debug logs you can use the following snippet.

import quandl
import logging

# logging.getLogger().setLevel(logging.DEBUG)  # optionally set level for
everything.  Useful to see dependency debug info as well.

quandl_log = logging.getLogger("quandl")

Detailed Usage

Our API can provide more than just data. It can also be used to search and provide metadata or to programmatically retrieve data. For these more advanced techniques please follow our Detailed Method Guide.

Local Development


If you wish to work on local development please clone/fork the git repo and use pip install -r requirements.txt to setup the project.


We recommend the following tools for testing any changes:

  • nose for running tests.
  • tox for testing against multiple versions of python.
  • flake8 for syntax checking.
  • virtualenv for use with tox virtualization.

The following are instructions for running our tests:

  1. Make sure a version of python 2.7 or python 3.x is installed locally in your system. To avoid permission issues on OSX we recommend installing the packages from: https://www.python.org/downloads/
  2. Install virtualenv and tox using: pip install tox virtualenv
  3. Run following command (you may notice slow performance the first time): python setup.py install
  4. Run the following command to test the plugin in all versions of python we support: tox

Once you have all required packages installed, you can run tests locally with:

Running all tests locally

python -W always setup.py -q test

Running an individual test

python -m unittest test.[test file name].[class name].[individual test name]`


python -m unittest -v test.test_datatable.ExportDataTableTest.test_download_get_file_info

We would suggest downloading the data in raw format in the highest frequency possible and performing any data manipulation in pandas itself.

See this link for more information about timeseries in pandas.

Release the Package

To release the package, you can follow the instructions on this page


MIT License

Open Source Agenda is not affiliated with "Quandl Python" Project. README Source: quandl/quandl-python
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