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Description:

peopledatalabs 4.0.2

People Data Labs Python Client
Official Python client for the People Data Labs API.

Table of Contents

🔧 Installation
🚀 Usage
🏝 Sandbox Usage
🌐 Endpoints
📘 Documentation

Upgrading to v2.X.X
Upgrading to v3.X.X
Upgrading to v4.X.X

🔧 Installation

Install from PyPi using pip, a package manager for Python.
pip install peopledatalabs

Sign up for a free PDL API key.

🚀 Usage
First, create the PDLPY client:
from peopledatalabs import PDLPY

# specify your API key
client = PDLPY(
api_key="YOUR API KEY",
)

Then, send requests to any PDL API Endpoint.
Getting Person Data
By Enrichment
result = client.person.enrichment(
phone="4155688415",
pretty=True,
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code};"
f"\nReason: {result.reason};"
f"\nMessage: {result.json()['error']['message']};"
)

By Bulk Enrichment
result = client.person.bulk(
required="emails AND profiles",
requests=[
{
"metadata": {
"user_id": "123"
},
"params": {
"profile": ["linkedin.com/in/seanthorne"],
"location": ["SF Bay Area"],
"name": ["Sean F. Thorne"],
}
},
{
"metadata": {
"user_id": "345"
},
"params": {
"profile": ["https://www.linkedin.com/in/haydenconrad/"],
"first_name": "Hayden",
"last_name": "Conrad",
}
}
]
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Search (Elasticsearch)
es_query = {
"query": {
"bool": {
"must": [
{"term": {"location_country": "mexico"}},
{"term": {"job_title_role": "health"}},
]
}
}
}
data = {
"query": es_query,
"size": 10,
"pretty": True,
"dataset": "phone, mobile_phone",
}
result = client.person.search(**data)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Search (SQL)
sql_query = (
"SELECT * FROM person"
" WHERE location_country='mexico'"
" AND job_title_role='health'"
" AND phone_numbers IS NOT NULL;"
)
data = {
"sql": sql_query,
"size": 10,
"pretty": True,
"dataset": "phone, mobile_phone",
}
result = client.person.search(**data)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By PDL_ID (Retrieve API)
result = client.person.retrieve(
person_id="qEnOZ5Oh0poWnQ1luFBfVw_0000",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Fuzzy Enrichment (Identify API)
result = client.person.enrichment(
name="sean thorne",
pretty=True,
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Getting Company Data
By Enrichment
result = client.company.enrichment(
website="peopledatalabs.com",
pretty=True,
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Bulk Enrichment
result = client.company.bulk(
requests=[
{
"metadata": {
"company_id": "123"
},
"params": {
"profile": "linkedin.com/company/peopledatalabs",
}
},
{
"metadata": {
"company_id": "345"
},
"params": {
"profile": "https://www.linkedin.com/company/apple/",
}
}
]
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Search (Elasticsearch)
es_query = {
"query": {
"bool": {
"must": [
{"term": {"tags": "big data"}},
{"term": {"industry": "financial services"}},
{"term": {"location.country": "united states"}},
]
}
}
}
data = {
"query": es_query,
"size": 10,
"pretty": True,
}
result = client.company.search(**data)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

By Search (SQL)
sql_query = (
"SELECT * FROM company"
" WHERE tags='big data'"
" AND industry='financial services'"
" AND location.country='united states';"
)
data = {
"sql": sql_query,
"size": 10,
"pretty": True,
}
result = client.company.search(**data)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Using supporting APIs
Get Autocomplete Suggestions
result = client.autocomplete(
field="title",
text="full",
size=10,
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Clean Raw Company Strings
result = client.company.cleaner(
name="peOple DaTa LabS",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Clean Raw Location Strings
result = client.location.cleaner(
location="455 Market Street, San Francisco, California 94105, US",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Clean Raw School Strings
result = client.school.cleaner(
name="university of oregon",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Get Job Title Enrichment
result = client.job_title(
job_title="data scientist",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Get Skill Enrichment
result = client.skill(
skill="c++",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code}"
f"\nReason: {result.reason}"
f"\nMessage: {result.json()['error']['message']}"
)

Get IP Enrichment
result = client.ip(
ip="72.212.42.169",
)
if result.ok:
print(result.text)
else:
print(
f"Status: {result.status_code};"
f"\nReason: {result.reason};"
f"\nMessage: {result.json()['error']['message']};"
)

🏝 Sandbox Usage
To enable sandbox usage, use the sandbox flag on PDLPY
PDLPY(sandbox=True)

🌐 Endpoints
Person Endpoints

API Endpoint
PDLPY Function

Person Enrichment API
PDLPY.person.enrichment(**params)

Person Bulk Enrichment API
PDLPY.person.bulk(**params)

Person Search API
PDLPY.person.search(**params)

Person Retrieve API
PDLPY.person.retrieve(**params)

Person Identify API
PDLPY.person.identify(**params)

Company Endpoints

API Endpoint
PDLPY Function

Company Enrichment API
PDLPY.company.enrichment(**params)

Company Bulk Enrichment API
PDLPY.company.bulk(**params)

Company Search API
PDLPY.company.search(**params)

Supporting Endpoints

API Endpoint
PDLJS Function

Autocomplete API
PDLPY.autocomplete(**params)

Company Cleaner API
PDLPY.company.cleaner(**params)

Location Cleaner API
PDLPY.location.cleaner(**params)

School Cleaner API
PDLPY.school.cleaner(**params)

Job Title Enrichment API
PDLPY.job_title(**params)

Skill Enrichment API
PDLPY.skill(**params)

IP Enrichment API
PDLPY.ip(**params)

📘 Documentation
All of our API endpoints are documented at: https://docs.peopledatalabs.com/
These docs describe the supported input parameters, output responses and also provide additional technical context.
As illustrated in the Endpoints section above, each of our API endpoints is mapped to a specific method in the PDLPY class. For each of these class methods, all function inputs are mapped as input parameters to the respective API endpoint, meaning that you can use the API documentation linked above to determine the input parameters for each endpoint.
As an example:
The following is valid because name is a supported input parameter to the Person Identify API:
PDLPY().person.identify({"name": "sean thorne"})

Conversely, this would be invalid because fake_parameter is not an input parameter to the Person Identify API:
PDLPY().person.identify({"fake_parameter": "anything"})

Upgrading to v2.X.X
NOTE: When upgrading to v2.X.X from vX.X.X and below, the minimum required python version is now 3.8.
Upgrading to v3.X.X
NOTE: When upgrading to v3.X.X from vX.X.X and below, the minimum required pydantic version is now 2.
Upgrading to v4.X.X
NOTE: When upgrading to v4.X.X from vX.X.X and below, we no longer auto load the API key from the environment variable PDL_API_KEY. You must now pass the API key as a parameter to the PDLPY class.