Use pandas to Visualize Twitter Data in Python

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Twitter Python Connector

Python Connector Libraries for Twitter Data Connectivity. Integrate Twitter with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Twitter enables you use pandas and other modules to analyze and visualize live Twitter data in Python.

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

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

Connecting to Twitter Data

Connecting to Twitter 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.

All tables require authentication. You can connect using your User and Password or OAuth. To authenticate using OAuth, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can register an app to obtain your own.

If you intend to communicate with Twitter only as the currently authenticated user, then you can obtain the OAuthAccessToken and OAuthAccessTokenSecret directly by registering an app.

See the Getting Started chapter in the help documentation for a guide to using OAuth.

Follow the procedure below to install the required modules and start accessing Twitter 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 Twitter Data in Python

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

engine = create_engine("twitter:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Twitter

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

df = pandas.read_sql("SELECT From_User_Name, Retweet_Count FROM Tweets WHERE From_User_Name = 'twitter'", engine)

Visualize Twitter Data

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

df.plot(kind="bar", x="From_User_Name", y="Retweet_Count")

Free Trial & More Information

Download a free, 30-day trial of the Twitter Python Connector to start building Python apps and scripts with connectivity to Twitter 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("twitter:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT From_User_Name, Retweet_Count FROM Tweets WHERE From_User_Name = 'twitter'", engine)

df.plot(kind="bar", x="From_User_Name", y="Retweet_Count")