Quandl Python Client

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

Installation

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

Configuration

Option	Explanation	Example
api_key	Your access key	tEsTkEy123456789
api_version	The API version you wish to use	2015-04-09
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'
quandl.ApiConfig.api_version = '2015-04-09'

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

quandl.ApiConfig.verify_ssl = False

quandl.ApiConfig.api_version is optional however it is strongly recommended to avoid issues with rate-limiting. For premium databases, datasets and datatables quandl.ApiConfig.api_key will need to be set to identify you to our API. Please see API Documentation for more detail.

Local API Key file

Save local key to $HOME/.quandl_apikey file

import quandl
quandl.save_key("supersecret")
print(quandl.ApiConfig.api_key)

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

import quandl
quandl.read_key()
print(quandl.ApiConfig.api_key)

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
quandl.read_key(filepath="/data/.corporatequandlapikey")

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 similiar 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. Similarily 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.

Detailed Usage

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

Local Development

Setup

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.

Testing

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]`

Example:

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

Recommended Usage

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

Additional Links

• Quandl
• Quandl Tools
• API Docs

License

MIT License