How to Visualize LinkedIn Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for LinkedIn, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build LinkedIn-connected Python applications and scripts for visualizing LinkedIn data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to LinkedIn data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live LinkedIn data in Python. When you issue complex SQL queries from LinkedIn, the driver pushes supported SQL operations, like filters and aggregations, directly to LinkedIn and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to LinkedIn Data

Connecting to LinkedIn data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Follow the procedure below to install the required modules and start accessing LinkedIn through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize LinkedIn Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with LinkedIn data.

engine = create_engine("linkedin:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to LinkedIn

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT VisibilityCode, Comment FROM CompanyStatusUpdates WHERE EntityId = '238'", engine)

Visualize LinkedIn Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the LinkedIn data. The show method displays the chart in a new window.

df.plot(kind="bar", x="VisibilityCode", y="Comment")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for LinkedIn to start building Python apps and scripts with connectivity to LinkedIn data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("linkedin:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT VisibilityCode, Comment FROM CompanyStatusUpdates WHERE EntityId = '238'", engine)

df.plot(kind="bar", x="VisibilityCode", y="Comment")
plt.show()