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https://github.com/quandl/quandl-python


https://github.com/quandl/quandl-python

api-client data-frame dataset python quandl retrieve-data

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README 

        
# 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](https://github.com/Nasdaq/data-link-python) in order to take advantage of new features.



# Quandl Python Client

[![Build Status](https://codebuild.us-east-1.amazonaws.com/badges?uuid=eyJlbmNyeXB0ZWREYXRhIjoiZUtvUFNYREloNE4vV0xWWEUxVS81S0toZjdQbzQrWXhQZ1BUbE1mZ1FMVXdSZXQ2K1ZLQ1ducmtqYTVWa2xBZXhRMWVGemVKWitzVm5MNXI4cGZYb21RPSIsIml2UGFyYW1ldGVyU3BlYyI6Im0vdUljcjBjdmpGVU9XdXUiLCJtYXRlcmlhbFNldFNlcmlhbCI6MX0%3D&branch=master)]()

[![PyPI version](https://badge.fury.io/py/Quandl.svg)](https://badge.fury.io/py/Quandl)



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](https://www.quandl.com/docs/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](./2_SERIES_UPGRADE.md)



## Installation



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



```shell

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:



```shell

pip3 install quandl

```



## Configuration



| Option | Explanation | Example |

|---|---|---|

| api_key | Your access key | `tEsTkEy123456789` | Used to identify who you are and provide full access. |

| 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]



```python

import quandl

quandl.ApiConfig.api_key = 'tEsTkEy123456789'

```

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

```python

quandl.ApiConfig.verify_ssl = False

```



### 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:



```python

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:



```python

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](./FOR_ANALYSTS.md).



### Logging



Currently, Quandl debug logging is limited in scope.  However, to enable debug

logs you can use the following snippet.



```python

import quandl

import logging



logging.basicConfig()

# logging.getLogger().setLevel(logging.DEBUG)  # optionally set level for

everything.  Useful to see dependency debug info as well.



quandl_log = logging.getLogger("quandl")

quandl_log.setLevel(logging.DEBUG)

```



### 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](./FOR_DEVELOPERS.md).



## 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](https://nose.readthedocs.org/en/latest/) for running tests.

* [tox](https://pypi.python.org/pypi/tox) for testing against multiple versions of python.

* [flake8](https://flake8.readthedocs.org/en/latest/) for syntax checking.

* [virtualenv](https://virtualenv.pypa.io/en/latest/) 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

python -W always setup.py -q test

```



Running an individual test



```python

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

```



Example:



```python

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](http://pandas.pydata.org/pandas-docs/dev/timeseries.html) for more information about timeseries in pandas.



## Release the Package



To release the package, you can follow the instructions on this [page](https://packaging.python.org/tutorials/packaging-projects/#packaging-python-projects)



## Additional Links



* [Quandl](https://www.quandl.com)

* [Quandl Tools](https://www.quandl.com/tools/api)

* [API Docs](https://www.quandl.com/docs/api)



## License



[MIT License](http://opensource.org/licenses/MIT)