How to Visualize Browse AI 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 Browse AI-connected Python applications and scripts for visualizing Browse AI data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Browse AI data, execute queries, and visualize the results.

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

Connecting to Browse AI Data

Connecting to Browse AI 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.

Start by setting the Profile connection property to the location of the BrowseAI Profile on disk (e.g. C:\profiles\BrowseAI.apip). Next, set the ProfileSettings connection property to the connection string for BrowseAI (see below).

BrowseAI API Profile Settings

BrowseAI is a web scraping and automation platform that allows you to train robots to extract and monitor data from websites. Authentication is performed using an API key that is sent as a Bearer token in the Authorization header.

The BrowseAI API key has the format {userId}:{apiKey}, where both parts are UUIDs separated by a colon. This full string is provided as a single value in the BrowseAI dashboard.

To obtain your API key:

1. Log in to your BrowseAI account at https://app.browse.ai
2. Navigate to Settings in the dashboard
3. Locate the API section
4. Copy the full API key string displayed (format: userId:apiKey)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your BrowseAI API key (format: userId:apiKey).

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

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

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

Execute SQL to Browse AI

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

df = pandas.read_sql("SELECT Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'", engine)

Visualize Browse AI Data

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

df.plot(kind="bar", x="Id", y="Status")
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 Browse AI 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\BrowseAI.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_user_id:your_api_key'")
df = pandas.read_sql("SELECT Id, Status FROM RobotTasks WHERE RobotId = 'your-robot-id'", engine)

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