How to Visualize Bright Data 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 API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Bright Data-connected Python applications and scripts for visualizing Bright Data data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Bright Data data, execute queries, and visualize the results.

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

Connecting to Bright Data Data

Connecting to Bright Data 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.

Using API Key Authentication

To use the Bright Data API, you need an API key from the Bright Data Control Panel. Navigate to Account Settings > API to generate or retrieve your API key.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Bright Data API key from the Control Panel.

Example connection string:

Profile=C:\profiles\BrightData.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key"

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

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

engine = create_engine("api:///?Profile=C:\profiles\BrightData.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")

Execute SQL to Bright Data

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 AccountStatus WHERE  = ''", engine)

Visualize Bright Data Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Bright Data 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("api:///?Profile=C:\profiles\BrightData.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")
df = pandas.read_sql("SELECT ,  FROM AccountStatus WHERE  = ''", engine)

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