Nasdaq Data Link Python Client

Methods like Database.all() and Dataset.all() return a PaginatedList (or ModelList). These objects allow you to check for more results using has_more_results() and access request metadata like current_page or total_results via hash lookup or convenience methods. Note that iterating through a list only iterates through the current page; you must fetch subsequent pages manually using params={'page': N}.

import nasdaqdatalink

# Get first page of databases
databases = nasdaqdatalink.Database.all()

# Check for more results and access metadata
if databases.has_more_results():
    print(f"Page: {databases.current_page}")
    
    # Fetch the next page
    more_databases = nasdaqdatalink.Database.all(params={'page': 2})
    for db in more_databases:
        print(db.database_code)

# Access raw values or metadata
print(databases.values)
print(databases.meta)

Install the official Nasdaq Data Link Python client using pip. This package is compatible with Python v3.7+.

pip install nasdaq-data-link

On some systems, you may need to use pip3:

pip3 install nasdaq-data-link

The Quick method is used for simple data retrieval. Both methods return data as a pandas DataFrame.

Retrieve Time-Series (Dataset) Data

Use nasdaqdatalink.get() to retrieve dataset data:

import nasdaqdatalink
data = nasdaqdatalink.get('NSE/OIL')
# data is a pandas DataFrame

Retrieve Non-Time Series (Datatable) Data

Use nasdaqdatalink.get_table() to retrieve datatable data:

import nasdaqdatalink
data = nasdaqdatalink.get_table('ZACKS/FC', ticker='AAPL')
# data is a pandas DataFrame

Note: If an api_key is not set, you may receive limited or sample data.

To enable debug logs for the nasdaqdatalink module, use the standard Python logging library:

import nasdaqdatalink
import logging

logging.basicConfig()

data_link_log = logging.getLogger("nasdaqdatalink")
data_link_log.setLevel(logging.DEBUG)

The client automatically attempts to load an API key from ~/.nasdaq/data_link_apikey if it exists.

Note: If the file exists but is empty, a ValueError will be thrown.

If you want to use a custom path for your API key file, call read_key() explicitly:

import nasdaqdatalink
nasdaqdatalink.read_key(filename="/data/.corporatenasdaqdatalinkapikey")

You can use environment variables to configure the SDK without inline boilerplate:

A MergedDataset allows you to perform a full outer join on multiple datasets. You can specify specific columns to include from each dataset using the column_index key in a configuration dictionary.

import nasdaqdatalink

# Merge specific columns from AAPL and MSFT with all columns from TWTR
merged_dataset = nasdaqdatalink.MergedDataset([
    ('WIKI/AAPL', {'column_index': [11]}),
    ('WIKI/MSFT', {'column_index': [9, 11]}), 
    'WIKI/TWTR'
])

# Retrieve the merged data
data = merged_dataset.data()

To download all datasets within a database, use the Database object. You can either retrieve a download URL or download the files directly to a local path. Use the params argument to specify the download_type (e.g., 'partial' or 'complete').

import nasdaqdatalink

# Get the download URL
url = nasdaqdatalink.Database('ZEA').bulk_download_url()

# Download to a local folder or file
nasdaqdatalink.Database('ZEA').bulk_download_to_file('/path/to/destination')

# Perform a partial bulk download
nasdaqdatalink.Database('ZEA').bulk_download_to_file('.', params={'download_type': 'partial'})

Use nasdaqdatalink.bulkdownload() to download an entire database as a zip file to your current working directory. The method returns the filename of the downloaded zip.

Basic Bulk Download

import nasdaqdatalink
asdaqdatalink.bulkdownload('EOD')

Partial Download

To download only the data from the previous day, use download_type='partial':

import nasdaqdatalink
asdaqdatalink.bulkdownload('EOD', download_type='partial')

Custom Filename

Specify a custom path and filename using the filename option:

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