NASDAQ Data Link

NASDAQ Data Link (formerly Quandl) provides access to a wide variety of financial and alternative datasets.

Getting Started

1. Create an account at data.nasdaq.com/sign-up - choose Academic if you’re a student

2. Set up two-factor authentication (Google Authenticator recommended)

3. Get your API key from data.nasdaq.com/account/profile

4. Store the key as a GitHub Secret (recommended) or in a .env file

Install the package:

pip install nasdaq-data-link

Fig. 37 NASDAQ Data Link homepage.

Set-up

import nasdaqdatalink
import os
from dotenv import load_dotenv

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
%matplotlib inline

# Load API key
load_dotenv()
NASDAQ_API_KEY = os.getenv('NASDAQ_API_KEY')

# Configure the API
nasdaqdatalink.ApiConfig.api_key = NASDAQ_API_KEY

Secure API Key Usage

For GitHub Codespaces, use secrets:

# Recommended approach
import nasdaqdatalink as ndl
import os

NASDAQ_API_KEY = os.environ.get('NASDAQ')
ndl.ApiConfig.api_key = NASDAQ_API_KEY

Exploring Available Data

Click EXPLORE on the main data page to see all available datasets. Much of the data is premium (paid), but you can filter for free data.

Fig. 38 Exploring NASDAQ data options.

Example: Zillow Housing Data

NASDAQ provides Zillow real estate data for free.

Fig. 39 Zillow housing data on NASDAQ.

The data is organized into tables:

• ZILLOW/DATA - The actual housing values

• ZILLOW/REGIONS - Region identifiers and descriptions

• ZILLOW/INDICATORS - Indicator descriptions

Understanding the Data Structure

Before downloading data, you need to understand what filters are available. Check the documentation for filtering options.

Exploring Regions

Let’s first pull the regions table to understand what geographic areas are available.

regions = nasdaqdatalink.get_table('ZILLOW/REGIONS', paginate=True)
regions

---------------------------------------------------------------------------
ForbiddenError                            Traceback (most recent call last)
Cell In[3], line 1
----> 1 regions = nasdaqdatalink.get_table('ZILLOW/REGIONS', paginate=True)
      2 regions

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/get_table.py:19, in get_table(datatable_code, **options)
     17 while True:
     18     next_options = copy.deepcopy(options)
---> 19     next_data = Datatable(datatable_code).data(params=next_options)
     21     if data is None:
     22         data = next_data

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/model/datatable.py:31, in Datatable.data(self, **options)
     29 if not options:
     30     options = {'params': {}}
---> 31 return Data.page(self, **options)

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/operations/list.py:30, in ListOperation.page(cls, datatable, **options)
     26 request_type = RequestType.get_request_type(path, **options)
     28 updated_options = Util.convert_options(request_type=request_type, **options)
---> 30 r = Connection.request(request_type, path, **updated_options)
     32 response_data = r.json()
     33 Util.convert_to_dates(response_data)

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/connection.py:40, in Connection.request(cls, http_verb, url, **options)
     36 options['headers'] = headers
     38 abs_url = '%s/%s' % (ApiConfig.api_base, url)
---> 40 return cls.execute_request(http_verb, abs_url, **options)

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/connection.py:52, in Connection.execute_request(cls, http_verb, url, **options)
     47 response = session.request(method=http_verb,
     48                            url=url,
     49                            verify=ApiConfig.verify_ssl,
     50                            **options)
     51 if response.status_code < 200 or response.status_code >= 300:
---> 52     cls.handle_api_error(response)
     53 else:
     54     return response

File ~/Documents/fin-data-analysis-text/.venv/lib/python3.9/site-packages/nasdaqdatalink/connection.py:120, in Connection.handle_api_error(cls, resp)
    109 d_klass = {
    110     'L': LimitExceededError,
    111     'M': InternalServerError,
   (...)
    116     'X': ServiceUnavailableError
    117 }
    118 klass = d_klass.get(code_letter, DataLinkError)
--> 120 raise klass(message, resp.status_code, resp.text, resp.headers, code)

ForbiddenError: (Status 403) (Nasdaq Data Link Error QEPx04) A valid API key is required to retrieve data. Please check your API key and try again. You can find your API key under your account settings.

Note

The paginate=True parameter is important! Without it, you only get the first 10,000 rows. With pagination, the limit extends to 1,000,000 rows.

# Filter for just cities
cities = regions[regions.region_type == 'city']
cities

cities.info()

# Filter for counties
counties = regions[regions.region_type == 'county']
counties

Finding Specific Regions

You can search the text in a column to find specific regions. Let’s find all NC counties:

nc_counties = counties[counties['region'].str.contains(';NC')]
nc_counties

nc_counties.info()

There are 100 counties in NC, so this worked.

Downloading Housing Data

Now we can use these region IDs to pull actual housing data. First, convert to a list:

nc_county_list = nc_counties['region_id'].to_list()

Now pull the data for a specific indicator. ZATT is one of the housing indicators.

Warning

Be careful with large downloads! They can exceed API limits and cause timeouts.

# Example: Pull NC county data (commented to avoid API calls during build)
# zillow_nc = nasdaqdatalink.get_table(
#     'ZILLOW/DATA', 
#     indicator_id='ZATT', 
#     paginate=True, 
#     region_id=nc_county_list,
#     qopts={'columns': ['indicator_id', 'region_id', 'date', 'value']}
# )
# zillow_nc.head(25)

Exporting Data

You can export to CSV for exploration in Excel:

# Export counties to CSV
counties.to_csv('counties.csv', index=True)

Using Rapid API

Rapid API is a marketplace for thousands of APIs - markets, sports, weather, and more. Many have free tiers.

Fig. 40 Rapid API marketplace.

REST APIs

Most APIs on Rapid API are REST APIs - a standardized way for computers to communicate using standard data formats like JSON.

Learn more at the API Learn page.

Working with JSON Data

API responses typically come in JSON format. Here’s how to handle them:

import requests

# Make API request
response = requests.get(url, headers=headers)

# Convert to JSON
data_json = response.json()

# Flatten nested JSON to DataFrame
df = pd.json_normalize(data=data_json)

Fig. 41 JSON structure - nested dictionaries and lists.

For deeply nested JSON, use the record_path parameter:

# Flatten nested 'events' key
df = pd.json_normalize(data=data_json, record_path=['events'])

Data on Kaggle

Kaggle is another great source for datasets. Search their datasets for finance-related data.

Examples:

• Consumer Finance Complaints

• Stock price histories

• Economic indicators

Kaggle data is usually pre-cleaned for competitions, but you’ll still need to do some preparation work.