Identity Resolution + Our Data
The most comprehensive identity graph on the market.
Supercharge your database with a single source of robust people profiles over 3 billion profiles and growing.
Extensive linkages
When you’re trying to build your own dataset, the more linkages between fields, the stronger the data. Consolidate email addresses, phone numbers, street addresses, social profiles and more into a single person.
Consistent, transparent data build process
When creating links between data, our process is deliberate and transparent. We use probabilistic and deterministic matching to combine fragments of data with high precision.
Unique linkages
Thanks to our data union, we have the ability to tap into more unique linkages than any data provider on the market. We link B2B data to consumer data, contact info to social profiles, and more!
Let's get to work, together
We’re interested in finding the best way to power your platform – it’s what we do, and it’s why we put such an emphasis on the quality of our data as well as the flexibility of our integrations.
For engineers, by engineers
Tap into and discover linkages between hundreds of data fields including:
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"""
I want to find employment history and social profiles for people in my existing list of identities (containing name, email and current address)
"""
import requests, json
import pandas as pd
API_KEY = "####" # Enter your api key here
PDL_URL = "https://api.peopledatalabs.com/v5/person/enrich"
# Load in existing identities (containing names, emails, and current addresses)
existing_identities_list = "my_existing_identities.csv" # Enter path to your identities csv file here
existing_identities = pd.read_csv(existing_identities_list, header=0)
# Send an enrichment request for each the profiles in our existing identities list
enriched_identities = []
num_successful_responses = 0
for idx, existing_profile in existing_identities.iterrows():
# Set Person Enrichment API parameters
# We want to find work experience and profiles given a name, email and address
params = {
"api_key": API_KEY,
"first_name": [ existing_profile['first_name'] ], # e.g. Jennifer
"last_name": [ existing_profile['last_name'] ], # e.g. Jackson
"email": [ existing_profile['email'] ], # e.g. sean@peopledatalabs.com
"street_address" : [ existing_profile['street_address'] ], # e.g. 1234 Main Street
"locality": [ existing_profile['city'] ], # e.g. Boise
"region": [ existing_profile['state'] ], # e.g. Idaho
"postal_code": [ existing_profile['zip_code'] ], # e.g. 83701
"required": "experience AND profiles" # requirement for successful match
}
# Send a single API request
api_response = requests.get(PDL_URL, params=params).json()
# Parse the enrichment from the response
if api_response['status'] == 200:
# enriched_profile is a single profile object
# Successful matches are guaranteed to contain work experience and social profiles
# See the full schema for all available fields at: https://docs.peopledatalabs.com/docs/fields
enriched_profile = api_response['data']
num_successful_responses += 1
elif api_response['status'] == 404:
# If no profile match is found then, response will have a 404 status
enriched_profile = {}
else:
# Print if we get some other error
error = api_response['error']
print(f"Request was unsuccessful with status {api_response['status']}")
print(f"Error:
Type: {error['type']}
Message: {error['message']}")
enriched_profile = {}
enriched_identities.append(enriched_profile)
# Print Summary:
print(f"Total Number of Responses Received {len(enriched_identities)}")
print(f"Number of Successful Matches: {num_successful_responses}")
# Write enrichment results to csv
enriched_identities_dataframe = pd.DataFrame(enriched_identities)
enriched_identities_dataframe.to_csv("my_enriched_identities.csv", index=False)
Flexible data consumption to suit your needs
Just starting out? Our Search API and Enrichment API offer plug and play connection with easy onboarding. Need more data? Our on-prem data license gives you the data foundation you need.
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If your operation runs on personally identifiable information; make sure you have the best and most complete data available.
Fill in data gaps
Frequently missing key data points your customers need? Resolve information on people more quickly to allow your customers to get what they need.
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