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/login - 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. 39 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_DATA_LINK_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. 40 Exploring NASDAQ data options.

Example: Zillow Housing Data

NASDAQ provides Zillow real estate data for free.

Fig. 41 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

	region_id	region_type	region
None
0	99999	zip	98847;WA;Wenatchee, WA;Leavenworth;Chelan County
1	99998	zip	98846;WA;nan;Pateros;Okanogan County
2	99997	zip	98845; WA; Wenatchee; Douglas County; Palisades
3	99996	zip	98844;WA;nan;Oroville;Okanogan County
4	99995	zip	98843;WA;Wenatchee, WA;Orondo;Douglas County
...	...	...	...
89300	100000	zip	98848;WA;Moses Lake, WA;Quincy;Grant County
89301	10000	city	Bloomington;MD;nan;Garrett County
89302	1000	county	Echols County;GA;Valdosta, GA
89303	100	county	Bibb County;AL;Birmingham-Hoover, AL
89304	10	state	Colorado

89305 rows × 3 columns

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

	region_id	region_type	region
None
10	9999	city	Carrsville;VA;Virginia Beach-Norfolk-Newport N...
20	9998	city	Birchleaf;VA;nan;Dickenson County
56	9994	city	Wright;KS;Dodge City, KS;Ford County
124	9987	city	Weston;CT;Bridgeport-Stamford-Norwalk, CT;Fair...
168	9980	city	South Wilmington; IL; Chicago-Naperville-Elgin...
...	...	...	...
89203	10010	city	Atwood;KS;nan;Rawlins County
89224	10008	city	Bound Brook;NJ;New York-Newark-Jersey City, NY...
89254	10005	city	Chanute;KS;nan;Neosho County
89290	10001	city	Blountsville;AL;Birmingham-Hoover, AL;Blount C...
89301	10000	city	Bloomington;MD;nan;Garrett County

28104 rows × 3 columns

cities.info()

<class 'pandas.DataFrame'>
Index: 28104 entries, 10 to 89301
Data columns (total 3 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   region_id    28104 non-null  str  
 1   region_type  28104 non-null  str  
 2   region       28104 non-null  str  
dtypes: str(3)
memory usage: 878.2 KB

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

	region_id	region_type	region
None
94	999	county	Durham County;NC;Durham-Chapel Hill, NC
169	998	county	Duplin County;NC;nan
246	997	county	Dubois County;IN;Jasper, IN
401	995	county	Donley County;TX;nan
589	993	county	Dimmit County;TX;nan
...	...	...	...
89069	1003	county	Elmore County;AL;Montgomery, AL
89120	1002	county	Elbert County;GA;nan
89204	1001	county	Elbert County;CO;Denver-Aurora-Lakewood, CO
89302	1000	county	Echols County;GA;Valdosta, GA
89303	100	county	Bibb County;AL;Birmingham-Hoover, AL

3097 rows × 3 columns

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

	region_id	region_type	region
None
94	999	county	Durham County;NC;Durham-Chapel Hill, NC
169	998	county	Duplin County;NC;nan
2683	962	county	Craven County;NC;New Bern, NC
4637	935	county	Chowan County;NC;nan
4972	93	county	Ashe County;NC;nan
...	...	...	...
87475	1180	county	Martin County;NC;nan
87821	1147	county	Lenoir County;NC;Kinston, NC
88578	1059	county	Greene County;NC;nan
88670	1049	county	Graham County;NC;nan
88823	1032	county	Gaston County;NC;Charlotte-Concord-Gastonia, N...

100 rows × 3 columns

nc_counties.info()

<class 'pandas.DataFrame'>
Index: 100 entries, 94 to 88823
Data columns (total 3 columns):
 #   Column       Non-Null Count  Dtype
---  ------       --------------  -----
 0   region_id    100 non-null    str  
 1   region_type  100 non-null    str  
 2   region       100 non-null    str  
dtypes: str(3)
memory usage: 3.1 KB

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. 42 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. 43 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.