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LinkedFrame is a Python library designed to enrich data using LinkedIn, Google Search API, and Proxy Curl. It provides tools to fetch and process LinkedIn data, making it easier to integrate and analyze professional information.
To install LinkedFrame, use pip:
pip install linkedframe
To get started with LinkedFrame, follow these steps:
Step 1: Import the necessary modules
from linkedframe.enrichment import LinkedInDataEnrichmentProcessor
Step 2: Initialize the LinkedInDataEnrichmentProcessor
with your API keys
df_processor = LinkedInDataEnrichmentProcessor(
cse_id="your_cse_id",
google_console_api_key="your_google_console_api_key",
openai_key="your_openai_key",
proxycurl_api_key="your_proxycurl_api_key"
)
Step 3: Prepare your DataFrame with email addresses
df = pd.DataFrame({'email': ['example@example.com']})
Step 4: Process the emails to enrich the DataFrame with LinkedIn data
processed_df = df_processor.process_emails(df, email_col='email')
Step 5: Analyze and use the enriched data as needed
print(processed_df)
Step 6: Check the ProxyCurl API limit
df_processor.get_limits()
This is a basic example to demonstrate how to use LinkedFrame for data enrichment.
To use LinkedFrame, you will need the following API keys:
These API keys are required to initialize the LinkedInDataEnrichmentProcessor
and utilize the library's features.
FAQs
A library for enriching data with LinkedIn and Google Search API and Proxy Curl
We found that linkedframe demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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